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Surface-Enhanced Raman Spectroscopy for Biomedical Applications: Recent Advances and Future Challenges
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Surface-Enhanced Raman Spectroscopy for Biomedical Applications: Recent Advances and Future Challenges
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  • Linley Li Lin
    Linley Li Lin
    Sixth People’s Hospital, School of Medicine & School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, P. R. China
  • Ramon Alvarez-Puebla
    Ramon Alvarez-Puebla
    Departamento de Química Física e Inorganica, Universitat Rovira i Virgili, Tarragona 43007, Spain
    ICREA-Institució Catalana de Recerca i Estudis Avançats, Barcelona 08010, Spain
  • Luis M. Liz-Marzán
    Luis M. Liz-Marzán
    CIC biomaGUNE, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián 20014, Spain
    Ikerbasque, Basque Foundation for Science, University of Santiago de nCompostela, Bilbao 48013, Spain
    Centro de Investigación Cooperativa en Red, Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Donostia-San Sebastián 20014, Spain
    Cinbio, University of Vigo, Vigo 36310, Spain
  • Matt Trau
    Matt Trau
    Centre for Personalized Nanomedicine, Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD 4072, Australia
    School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD 4072, Australia
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  • Jing Wang
    Jing Wang
    Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou 350117, China
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  • Laura Fabris
    Laura Fabris
    Department of Applied Science and Technology, Politecnico di Torino Corso Duca degli Abruzzi 24, 10129 Torino, Italy
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  • Xiang Wang
    Xiang Wang
    State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Department of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
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  • Guokun Liu
    Guokun Liu
    State Key Laboratory of Marine Environmental Science, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry and Toxicology, College of the Environment and Ecology, Xiamen University, Xiamen 361005, China
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  • Shuping Xu
    Shuping Xu
    State Key Laboratory of Supramolecular Structure and Materials, College of Chemistry, Jilin University, Changchun 130012, PR China
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  • Xiao Xia Han
    Xiao Xia Han
    State Key Laboratory of Supramolecular Structure and Materials, College of Chemistry, Jilin University, Changchun 130012, PR China
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  • Liangbao Yang
    Liangbao Yang
    Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui 230031, P. R. China
    Department of Pharmacy, Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, Anhui 230031, P. R. China
  • Aiguo Shen
    Aiguo Shen
    School of Bioengineering and Health, Wuhan Textile University, Wuhan 430200, P. R. China
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  • Shikuan Yang
    Shikuan Yang
    School of Materials Science and Engineering, Zhejiang University, Hangzhou 310027, P. R. China
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  • Yikai Xu
    Yikai Xu
    Key Laboratory for Advanced Materials and Feringa Nobel Prize Scientist Joint Research Center, Frontiers Science Center for Materiobiology and Dynamic Chemistry, School of Chemistry and Molecular Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, P. R. China
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  • Chunchun Li
    Chunchun Li
    School of Materials Science and Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, P. R. China
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  • Jinqing Huang
    Jinqing Huang
    Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong 999077, China
  • Shao-Chuang Liu
    Shao-Chuang Liu
    Molecular Sensing and Imaging Center, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, P. R. China
  • Jian-An Huang
    Jian-An Huang
    Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Aapistie 5 A, 90220 Oulu, Finland
    Research Unit of Disease Networks, Faculty of Biochemistry and Molecular Medicine, University of Oulu, Aapistie 5 A, 90220 Oulu, Finland
    Biocenter Oulu, University of Oulu, Aapistie 5 A, 90220 Oulu, Finland
  • Indrajit Srivastava
    Indrajit Srivastava
    Department of Mechanical Engineering, Texas Tech University, Lubbock, Texas 79409, United States
    Texas Center for Comparative Cancer Research (TC3R), Amarillo, Texas 79106, United States
  • Ming Li
    Ming Li
    School of Materials Science and Engineering, Central South University, Changsha, Hunan 410083, China
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  • Limei Tian
    Limei Tian
    Department of Biomedical Engineering, and Center for Remote Health Technologies and Systems Texas A&M University, College Station, Texas 77843, United States
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  • Lam Bang Thanh Nguyen
    Lam Bang Thanh Nguyen
    School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 21 Nanyang Link, Singapore 637371
  • Xinyuan Bi
    Xinyuan Bi
    Sixth People’s Hospital, School of Medicine & School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, P. R. China
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  • Dana Cialla-May
    Dana Cialla-May
    Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745 Jena, Germany
    Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743 Jena, Germany
  • Pavel Matousek
    Pavel Matousek
    Central Laser Facility, Research Complex at Harwell, STFC Rutherford Appleton Laboratory, UKRI, Harwell Campus, Oxfordshire OX11 0QX, United Kingdom
    Department of Physics and Astronomy, University of Exeter, Exeter EX4 4QL, United Kingdom
  • Nicholas Stone
    Nicholas Stone
    Department of Physics and Astronomy, University of Exeter, Exeter EX4 4QL, United Kingdom
  • Randy P. Carney
    Randy P. Carney
    Department of Biomedical Engineering, University of California, Davis, California 95616, United States
  • Wei Ji
    Wei Ji
    College of Chemistry, Chemical Engineering and Resource Utilization, Northeast Forestry University, Harbin 145040, China
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  • Wei Song
    Wei Song
    State Key Laboratory of Supramolecular Structure and Materials, College of Chemistry, Jilin University, Changchun 130012, PR China
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  • Zhou Chen
    Zhou Chen
    Sixth People’s Hospital, School of Medicine & School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, P. R. China
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  • In Yee Phang
    In Yee Phang
    Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, International Joint Research Laboratory for Nano Energy Composites, School of Chemical and Material Engineering, Jiangnan University, Wuxi 214122, P. R. China
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  • Malou Henriksen-Lacey
    Malou Henriksen-Lacey
    CIC biomaGUNE, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián 20014, Spain
    Centro de Investigación Cooperativa en Red, Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Donostia-San Sebastián 20014, Spain
  • Haoran Chen
    Haoran Chen
    Sixth People’s Hospital, School of Medicine & School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, P. R. China
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  • Zongyu Wu
    Zongyu Wu
    Sixth People’s Hospital, School of Medicine & School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, P. R. China
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  • Heng Guo
    Heng Guo
    Department of Biomedical Engineering, and Center for Remote Health Technologies and Systems Texas A&M University, College Station, Texas 77843, United States
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  • Hao Ma
    Hao Ma
    State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Department of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
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  • Gennadii Ustinov
    Gennadii Ustinov
    Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745 Jena, Germany
    Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743 Jena, Germany
  • Siheng Luo
    Siheng Luo
    State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Department of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
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  • Sara Mosca
    Sara Mosca
    Central Laser Facility, Research Complex at Harwell, STFC Rutherford Appleton Laboratory, UKRI, Harwell Campus, Oxfordshire OX11 0QX, United Kingdom
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  • Benjamin Gardner
    Benjamin Gardner
    Department of Physics and Astronomy, University of Exeter, Exeter EX4 4QL, United Kingdom
  • Yi-Tao Long
    Yi-Tao Long
    Molecular Sensing and Imaging Center, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, P. R. China
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  • Juergen Popp
    Juergen Popp
    Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745 Jena, Germany
    Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743 Jena, Germany
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  • Bin Ren
    Bin Ren
    State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Department of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
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  • Shuming Nie
    Shuming Nie
    Department of Bioengineering, University of Illinois at Urbana−Champaign, 1406 W. Green Street, Urbana, Illinois 61801, United States
    More by Shuming Nie
  • Bing Zhao
    Bing Zhao
    State Key Laboratory of Supramolecular Structure and Materials, College of Chemistry, Jilin University, Changchun 130012, PR China
    More by Bing Zhao
  • Xing Yi Ling*
    Xing Yi Ling
    School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 21 Nanyang Link, Singapore 637371
    Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, International Joint Research Laboratory for Nano Energy Composites, School of Chemical and Material Engineering, Jiangnan University, Wuxi 214122, P. R. China
    *Email: [email protected]
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  • Jian Ye*
    Jian Ye
    Sixth People’s Hospital, School of Medicine & School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, P. R. China
    *Email: [email protected]
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ACS Applied Materials & Interfaces

Cite this: ACS Appl. Mater. Interfaces 2025, 17, 11, 16287–16379
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https://doi.org/10.1021/acsami.4c17502
Published February 24, 2025

Copyright © 2025 American Chemical Society. This publication is licensed under these Terms of Use.

Abstract

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The year 2024 marks the 50th anniversary of the discovery of surface-enhanced Raman spectroscopy (SERS). Over recent years, SERS has experienced rapid development and became a critical tool in biomedicine with its unparalleled sensitivity and molecular specificity. This review summarizes the advancements and challenges in SERS substrates, nanotags, instrumentation, and spectral analysis for biomedical applications. We highlight the key developments in colloidal and solid SERS substrates, with an emphasis on surface chemistry, hotspot design, and 3D hydrogel plasmonic architectures. Additionally, we introduce recent innovations in SERS nanotags, including those with interior gaps, orthogonal Raman reporters, and near-infrared-II-responsive properties, along with biomimetic coatings. Emerging technologies such as optical tweezers, plasmonic nanopores, and wearable sensors have expanded SERS capabilities for single-cell and single-molecule analysis. Advances in spectral analysis, including signal digitalization, denoising, and deep learning algorithms, have improved the quantification of complex biological data. Finally, this review discusses SERS biomedical applications in nucleic acid detection, protein characterization, metabolite analysis, single-cell monitoring, and in vivo deep Raman spectroscopy, emphasizing its potential for liquid biopsy, metabolic phenotyping, and extracellular vesicle diagnostics. The review concludes with a perspective on clinical translation of SERS, addressing commercialization potentials and the challenges in deep tissue in vivo sensing and imaging.

This publication is licensed for personal use by The American Chemical Society.

Copyright © 2025 American Chemical Society

1. Introduction: At the Interface of SERS and Biomedicine

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Surface-enhanced Raman scattering or spectroscopy (SERS) has emerged as a transformative analytical technique at the intersection of nanotechnology and biomedical sciences. By leveraging the plasmonic properties of metallic nanostructured, SERS offers remarkable sensitivity, enabling the detection of molecular fingerprints even at the single-molecule level. Its discovery has not only given rise to an entirely new field of research in chemistry and biology but also opened a wide range of applications as an ultrasensitive analytical tool, including disease diagnostics, biomarker detection, cellular imaging, and therapeutic monitoring.
The year 1974 marked the inception of SERS technique based on silver (Ag) substrates. (1) Soon after, in the 1980s, the SERS technique began to be used in vitro for the detection of various biomolecules. (2−5) The research at that time primarily focused on the improvement of the SERS sensitivity and the interpretation of spectra. (6,7) By the late 1990s, the emergence of single-molecule SERS (8,9) pushed this technique once again to the forefront of biosensing applications. In 2008, Nie et al. achieved for the first time the in vivo tumor detection using gold (Au) SERS nanotags, opening up new exploration opportunities of SERS in live animals. (10) The past decade has seen a surge of interest in the application of SERS in biomedicine, driven by advancements in nanomaterial synthesis, substrate engineering, and spectral analysis techniques, especially artificial intelligence (AI). These developments have gradually expanded the potential application range of SERS from fundamental research to real-world clinical applications. Key areas of progress include the design of colloidal and solid-state SERS substrates for high-efficiency signal enhancement, the creation of SERS nanotags for multiplexed detection, and the integration of SERS platforms into wearable and microfluidic devices. Additionally, breakthroughs in spectral analysis, quantification methods, and machine learning techniques have opened new avenues for SERS-based diagnostics, enabling precise and reliable detection of nucleic acids, proteins, metabolites, and extracellular vesicles.
This review revisits the recent advances in SERS for biomedical applications over the past decade, exploring cutting-edge developments in substrate design, sensing platforms, and data analysis. We will discuss how innovations in SERS substrates─both colloidal and solid surface-based─have pushed the boundaries of sensitivity and specificity. We will also examine the growing role of SERS nanotags in biomedical sensing, alongside novel devices and chips that facilitate point-of-care diagnostics. Furthermore, this review addresses critical challenges in SERS quantification, including emerging techniques like digital SERS. The latest advancements in AI-assisted spectral analysis, from frequency analysis, noise reduction to spectral unmixing, have improved signal interpretation with the adoption of AI. Next, we will delve into the expanding applications of SERS in biomedicine, covering nucleic acid, protein, metabolite biosensing, as well as tissue bioimaging, and the use of SERS for in vivo deep sensing. Finally, this review provides a comprehensive overview of the current state of the field, while also outlining future challenges and opportunities for further advancements.

2. SERS Substrates: Colloidal Substrates

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Colloidal Au/Ag nanoparticles (NPs) are among the most widely used SERS substrates due to their ease of synthesis, reproducibility, and ability to provide strong plasmonic enhancement. However, a major challenge in utilizing agglomerated colloidal Au/Ag NPs for SERS analysis is ensuring that target molecules specifically adsorb at hotspot regions, where signal enhancement is most effective. The adsorption of target analytes onto plasmonic substrates can occur through direct or indirect mechanisms (Figure 1), which can be controlled by modifying the surface chemistry of the NPs, as detailed in Section 2.1. Such surface modifications can also facilitate the formation of self-assembled layers or agglomerated colloidal NPs, as discussed in Section 2.2. While ensuring adsorption at plasmonic hotspots remains a critical hurdle, we discuss various strategies to achieve adsorption at these critical regions in greater detail in Sections 2.3 and 2.4.

Figure 1

Figure 1. Schematic illustration of the direct and indirect adsorption.

2.1. Surface Chemistry and Solution-Phase Nanoparticles

One of the simplest and most effective methods to achieve strong plasmonic enhancement using colloids involves the use of inorganic salts to induce NP agglomeration. Briefly, plasmonic colloidal NPs are often stabilized by electrostatic forces provided by their capping ligands. The addition of high-concentration of inorganic salts disrupts the electric double layer and significantly weakens interparticle electrostatic repulsion, allowing the NPs to agglomerate via attractive van der Waals forces. As shown in Figure 1, the adsorption of target analytes within the plasmonic enhancement region can be categorized into two forms: direct and indirect adsorption. (11)
The capability for direct adsorption of analytes largely hinges on the surface chemistry of colloidal NPs. Typically, colloidal NPs are coated with a layer of ligands, which are used to direct the growth of the particles into structures and to prevent NP agglomeration. However, this also means that, for NPs capped with strongly adsorbed stabilizing agents, these prepared NPs can only be used to detect molecules with even higher affinity to them. For example, the two most employed stabilizers for directly controlling the morphology and dimensions of NPs are cetyltrimethylammonium bromide (CTAB) and cetyltrimethylammonium chloride (CTAC), which remain adsorbing strongly to NP surface. As a result, the as-prepared colloidal NPs are mostly limited to SERS studies of strongly adsorbing molecules, such as thiols and amines. For instance, Das et al. have demonstrated the trace-amount SERS analysis of Rhodamine 6G dye by using CTAB-capped NPs as the enhancing substrate. (12) Liz-Marzán et al. reported the detection of methylthioadenosine, which contains thiol and amine groups and is crucial to metabolic interactions between cancer cells and their microenvironment, using CTAC-capped NPs. (13)
To widen the range of target analytes, it is also possible to use pretreatment methods to remove and replace strongly adsorbing capping ligands prior to SERS analysis. For example, Tan et al. replaced CTAB by poly(4-styrenesulfonic acid) (PSS) and then citrate ions on Au nanorods surface, enabling the subsequent detection of several dyes and thiabendazole. (14) Zhang et al. showed that the H generated in sodium borohydride solutions could replace thiols from the surface of Au NPs. (15) Since H is not stable in solution and quickly undergoes reactions to H2, this leaves the surface of the NPs accessible for the adsorption of analyte molecules.
Alternatively, weakly adsorbing ligands, such as citrate, chloride ions, which can be easily replaced, can also serve as stabilizing agents for colloidal NPs. Typical examples of such colloids include citrate, hydroxylamine and sodium borohydride reduced Ag or Au colloids. As shown in Figure 2a, Bi et al. showed that Ag colloidal NPs stabilized with chloride ions can serve as a general substrate for the detection of a wide variety of analyte molecules down to single-molecule levels, including weakly adsorbing analytes such as glucose. (16) One challenge with using weakly adsorbing ligands for NP synthesis is controlling morphology, which is essential for achieving strong plasmonic enhancement. Bell and Xu et al. used Ag+ as a growth-directing agent to synthesize hollow Au nanostars. (17) The Ag+ is reduced to Ag0, becoming part of the particles and leaving the surface accessible for analyte adsorption. Moreover, the authors compared SERS activity of nanostars capped with chloride ions and polyvinyl propylene (PVP), finding that PVP capping significantly decreased SERS signals (Figure 2b).

Figure 2

Figure 2. Examples of solution-phase NP-based application. (a) Digital SERS analysis of paraquat in lake water and thiram in bean sprouts extract by using chloride-capped Ag NPs as the enhancing substrates. Reproduced from ref (16). Copyright 2024, The Authors, under exclusive license to Springer Nature Limited. (b) SERS spectra of CV, doxorubicin (DOX), niraparib (NI) obtained on pristine (i), hedroxyethyl-cellulose stabilized (ii), and PVP-capped Au nanostars. Reproduced from ref (17). Copyright 2021, The Authors. (c) SERS spectra of negatively charged dye labeled DNA using Ag NPs modified by spermine (blue), spermidine (red), and ethylenedioxy-diethylamine (black) as the enhancing substrates, respectively. Reproduced from ref (18). Copyright 2012, Royal Society of Chemistry. (d) SERS spectra of MDMA detected by Ag NPs modified with different feedstock proportion of mixed thiols, as well as the normal Raman spectrum of MDMA pow der. Reproduced from ref (19). Copyright 2012, Royal Society of Chemistry. (e) SERS analysis of methanol by using CB modified Au NPs as the enhancing substrates. Reproduced from ref (20). Copyright 2017, Royal Society of Chemistry.

Analyte molecules can also adsorb directly onto unoccupied nanoparticle surface sites. Bell and Xu et al. showed that the adsorption strength of citrate/chloride ion on Ag/Au surfaces is inversely proportional to surface coverage due to charge accumulation, limiting ion coverage and leaving accessible sites for polyaromatic hydrocarbons adsorption, enabling their SERS detection. (21) In contrast, this effect was absent when thiol molecules passivated the surface. (22)
In many cases, colloidal NPs are treated with solutions of inorganic salts (often referred to as the “agglomerating agent”), which induce NP agglomeration, and in turn, creates plasmonic hot-spots that generates strong field enhancement. Therefore, apart from the type of capping ligands, the agglomerating agent can also affect the surface chemistry of the colloidal NPs and their SERS performance. More specifically, the relative affinity between the agglomerating agent and analyte molecule for the enhancing surface is crucial, since the agglomerating agent could compete with the analyte molecule for enhancing surface, which in extreme cases, could completely hinder the adsorption of the analyte. For example, Bell et al. demonstrated that when using MgCl2 to induce agglomeration of Ag colloid, 2′-deoxyadenosine 5′-monophosphate (dAMP) did not show any SERS signal even at 1000 ppm level, as dAMP was out competed by chloride ions which adsorbed more strongly to the surface of Ag NPs. (23) In contrast, when MgSO4 was used, clear SERS signals of dAMP could be observed even at 3 ppb. In other cases, the agglomerating agent has been shown to be able to aid the adsorption of analytes. For example, Liu et al. showed that using halide salts as the agglomerating agent promoted the adsorption of polyaromatic hydrocarbons to colloidal Ag NPs. (24,25)
The indirect analyte adsorption requires the modification of NPs surfaces with a functional layer, which interacts and attracts target analytes to the surface of the enhancing material. In most cases, this approach is used to promote the adsorption of analyte molecules which otherwise interact weakly with the enhancing metal, (26−28) but have also been shown to be effective for inducing selectivity in the analysis of complex samples. (29−31) The most common type of modifier is the self-assembled monolayers (SAMs) of small molecules containing strongly adsorbing head groups and functional tail groups. For example, Graham et al. reported that SAMs of positively charged spermine can be used to functionalize Ag NPs to attract and detect negatively charged dye-labeled DNA (Figure 2c). (18) Similarly, by modifying the colloidal Ag NP surface with positively charged thiol, Bell et al. achieved the SERS analysis of nonadsorbing negatively charged analytes such as perchlorate and picric acid (an explosive) at micromolar level. (32) In some cases, SAMs consisted of a mixture of different types of small-molecules are used to increase the affinity of analytes onto the enhancing surface. For example, Bell et al. realized highly sensitivity SERS detection of 3,4-methylenedioxymethamphetamine (MDMA, “ecstasy”) by modifying the surface of Ag NPs with a mixed SAM consisted of sodium mercaptopropanesulfonate (MPS) and benzyl mercaptan (BZM), which attract the target analyte through the synergistic function of electrostatic attraction and hydrophobic interaction (Figure 2d). (19)
Another class of modifiers used to attract target analytes are large or macro molecules that possess specific structure that are capable of interacting with target analytes. For example, molecules with intercavities can be used as the “host”, to capture “guest” molecules through specific host–guest interactions. Baumberg and Scherman et al. reported the use of cucurbit[n]uril (CB[n]) adsorbed on the surface of Au NPs as a molecular cage to detect alcohol molecules via hydrogen bonding between the CB host and alcohol guest molecule (Figure 2e). (20) Besides host molecules, biological macromolecules such as antibodies and aptamers are also widely used as modifiers in SERS detection of bioanalytes. For example, Yu et al. reported a point-of-care SERS detection platform where SARS-CoV-2 antigen was specifically captured by aptamers functionalized on Au NPs, which enabled its SERS detection within 5 min. (33) Another type of macromolecules that has been widely studied as the modifiers for SERS is molecularly imprinted polymers due to their excellent selectivity and recognition capabilities. For example, Nguyen et al. reported a SERS substrate of Au nanorods coated with a molecularly imprinted polymer, which has been designed to selectively capture rhodamine B in a mixture of different dyes spiked in orange juice. (34)
An emerging class of functional modifiers are inorganic materials which are coated as shells on the surface of colloidal Au/Ag NPs to capture target molecules. The most commonly used shell material for this purpose are metal organic frameworks (MOFs). For example, Yang et al. reported Au nanorod@ZIF-8 NPs, which enabled selective detection of 4-nitrobenzenethiol at nanomolar concentration levels in the presence of whole blood. (35) In addition, 2D materials, such as graphene layers, have also been explored as shell materials for promoting analyte adsorption in SERS. (36)
In summary the surface chemistry of the colloidal NPs affects the performance of SERS substrates, since SERS is a short-range effect that makes the adsorption of the target analyte to the surface of the substrate material crucial in colloidal SERS. Within this context, a major challenge often faced by SERS users is the dilemma between having strong plasmonic enhancement and accessible surface properties, since the synthesis of colloidal NPs which provide strong plasmonic field enhancement typically require the use of strongly adsorbing growth directing agents. (37,38) Therefore, it will be important to develop novel methods which balances the plasmonic and surface properties of colloidal NPs, (39) since both properties play a significant role in successful SERS analysis. Finally, we should note that while the examples discussed in this section focused on colloidal NPs, the same underlining principles regarding analyte adsorption are applicable to advanced SERS substrates that are built from colloidal NPs.

2.2. Self-Assembled Nanoparticle at Water/Oil Interface

In addition to the external stimulation (e.g., by magnetic field) (40) and top-down construction (e.g., by e-beam lithography), (41) a particularly elegant method to form plasmonic NP aggregates for SERS enhancement is to assemble colloidal NPs at the water/oil (w/o) interface to form interfacial arrays. (42) Interfacial arrays of noble metal NPs were first reported in 1988 by Yogev et al., (43) while their first use as enhancing substrates in SERS was reported a year later by Gordon et al. (44) At the time, the authors showed the capabilities of interfacial Ag arrays by demonstrating resonant SERS (surface-enhanced resonant Raman spectroscopy, SERRS) detection of several different types of metal complexes. Since then, interfacial arrays of Ag and Au NPs have grown to become an important and popular class of enhancing substrates for SERS analysis.
It is now well-established that the assembly of solid colloidal NPs at the water–oil interface is driven by the reduction of interfacial energy between the immiscible liquid phases. (45) However, the surface of plasmonic colloidal NPs often carries charged ligand molecules which give the NPs a surface charge. This surface charge helps to maintain colloidal stability by providing interparticle electrostatic repulsion, but also prevents the charged NPs from packing tightly at the interface to form plasmonic hotspots. This makes the development of methods to overcome interparticle electrostatic repulsion crucial for the construction of SERS enhancing substrates via interfacial self-assembly. (46)
As shown in Figure 3a, the most straightforward way to overcome this issue is to remove the charged ligands by modifying the surface of the charged colloidal NPs with charge-neutral organic “modifiers”. For noble metals, modifiers are typically thiols and pyridines, with the most popular being poly(ethylene glycol)methyl ether thiol (PEG-SH) and polyvinylpyrrolidone. (47,48) A crucial disadvantage of the use of modifiers in SERS is that the strongly adsorbed modifiers passivate the surface of the enhancing material, which limits the type of molecules that can be detected using SERS. (49) As a result, SERS studies performed using interfacial nanoparticle assemblies formed via modifiers have been limited to the analysis of a small set of analyte molecules which either interacts strongly with the noble metal surface or with the modifier. (50,51) Another viable way to overcome interparticle electrostatic repulsion is to reduce the surface potential of the NPs. This could be achieved through the addition of a organic cosolvent, such as acetone or ethanol, into the aqueous phase of the biphasic system (Figure 3b). (52,53) In this case, the addition of cosolvents in the aqueous colloid lowers the ability of the aqueous phase to solvate ionic compounds and causes the charged ionic species on the surface of NPs to recombine with their ionic counterparts in solution. (54) This removes the electric double layer, which significantly weakens interparticle electrostatic repulsion. (55) Unlike the modifier approach, the cosolvent approach does not lead to the surface of the plasmonic NPs being passivated by strongly adsorbing modifiers. This advantage along with the simplicity of the method has made the cosolvent approach a widely used method for constructing interfacial arrays for SERS analysis. (56−58)

Figure 3

Figure 3. Methods and application of self-assembled NPs. (a–d) Schematic illustrations of the different methods for overcoming interparticle electrostatic repulsion in interfacial NP self-assembly. Panels a–d were adapted with permission from ref (46). Copyright 2021, The Royal Society of Chemistry. (e) Schematic illustrations of interfacial plasmonic arrays used as the enhancing substrate for SERS detection of norepinephrine in rat brain microdialysates. Reproduced with permission from ref (67). Copyright 2022, Wiley-VCH-GmbH. (f) Schematic illustration of in situ biphasic SERS performed using interfacial arrays as the enhancing substrate. (g) SERS spectra of (i) Au interfacial arrays, (ii–iii) Au interfacial arrays containing 10–3 and 10–5 M naphthalene introduced from the oil phase, (iv–vii) Au interfacial array containing 10–5 M naphthalene introduced from water, hexane, or chloroform phase of the interfacial array, respectively. Panels f–g were adapted with permission fromref (21). Copyright 2022, The Authors. (h) Examples of plasmonic interfacial arrays deposited on various supporting materials. The scalebars corresponds to 0.5 cm. Adapted with permissions from ref (68). Copyright 2021, The Authors. (I) Schematic illustration and photograph showing bacterial headspace probed in situ via SERS using interfacial arrays deposited on quartz as the enhancing substrate. Headspace SERS spectra of the time dependent adsorption from broth spiked with 10–2 M dimethyl disulfide. Adapted with permissions from ref (69). Copyright 2028, Wiley-VCH-GmbH.

Besides using cosolvents, an equally effective approach to induce interfacial self-assembly without passivating the NPs’ surface is to add hydrophilic salts, such as NaCl into the aqueous colloids. (51,59) As shown in Figure 3c, the Debye length of the electric double layer surrounding charged colloidal NPs shrinks with increasing ionic concentration. Therefore, the addition of salt acts similarly to the addition of a cosolvent and leads to a significant decrease to the electrostatic potential around the colloidal NPs. (60,61)
The main disadvantage of using cosolvents or salts to induce interfacial self-assembly is that the significant reduction to the surface potential of the colloidal NPs could also lead to uncontrolled aggregation that occurs in parallel to the self-assembly process. This inevitably leads to the formation of structural defects and/or large aggregates in the product interfacial NP-assemblies, (62,63) which significantly affects the reproducibility of the SERS measurements. To combat this issue, Lin et al., showed that a similar affect can also be achieved through the addition of a second type of oil to create a dual-interface system. (63) In this case, the oil phases need to have different densities and surface tension gradients relative to water, so that the NPs initially assembled at the interface with lower surface tension gradient will be transferred to and compressed at the interface with higher surface tension by Marangoni force.
More recently, Magdalena et al. reported a new class of chemical compounds, termed “promoters”, which could be used to induce self-assembly of various charged colloidal NPs, including Ag and Au NPs, into densely packed interfacial arrays for SERS analysis. (64) In general, promoters are amphiphilic salts, such as tetrabutylammonium nitrate, which carry an opposite charge to the colloidal NPs. As shown in Figure 3d, the amphiphilic property of the promoters allows them to reside at the interface to screen electrostatic repulsion between the adjacent NPs to enable the formation of densely packed nanoparticle arrays with strong plasmonic properties. (65) Importantly, although promoters were ionic salts they do not induce self-assembly via charge reduction like the common hydrophilic salts mentioned above, since the typical amount of promoter needed to induce self-assembly is at ∼ micromolar concentrations. This is far beneath the ionic concentrations required to significantly reduce the aggregation energy barrier, which overcomes the issue with unwanted aggregation in cosolvent or salt-induced self-assembly. In addition, the promoters do not adsorb directly to the surface of the metal NPs. This leaves the surface of the NPs accessible, which opens the possibility for SERS analysis of even weakly adsorbing analyte molecules. (66)
In general, plasmonic interfacial NP arrays are versatile enhancing substrates in SERS and have been used for the analysis of a wide range of analytes. For example, in an early demonstration by Cecchini et al., Au interfacial arrays were used as an enhancing substrate to detect both airborne and solubilized analyte molecules which included malachite green isothiocyanate, mercapto-5-nitrobenzimidazole, 4-methoxy-α-toluenethiol,2,4-dinitrotoluene, aniline, cysteine, and adenosine-5′-trophosphate with limits of detection reaching nanomolar levels. (59) More recently, Shi et al. utilized Au interfacial arrays functionalized with 4-(thiophen-3-ylethynyl)-benzaldehyde to achieve detection of norepinephrine, a neurotransmitter, in biofluid samples extracted from rat brain via microdialysis. (67) As shown in Figure 3e, the 4-(thiophen-3-ylethynyl)-benzaldehyde molecules exhibited dual-functionals, acting both as the modifier to induce interfacial self-assembly and as the Raman reporter for selective indirect detection of norepinephrine. This system allowed quantitative detection of norepinephrine down to 0.6 nM to be achieved within 4 min, which exceeded conventional methods including liquid chromatography–mass spectroscopy and fluorescence spectroscopy.
An advantage that comes uniquely with the use of interfacial arrays as SERS enhancing substrates is their biphasic nature that gives the NPs access to analytes distributed in both organic and aqueous solvents. Indeed, the pioneering work by Gordon et al. were performed in an effort to observe the SERS signals of organic metal complexes which were only soluble in oil. (43) In addition, the oil phase can also be used to extract and concentrate analyte molecules from samples to allow direct SERS analysis of complex real-life samples. For example, Ma et al. utilized the cyclohexane phase of interfacial arrays as an extracting agent which allowed multiplex detection of drug molecules in urine samples. (70)
More recently, interfacial arrays have also found applications as SERS substrates in fundamental research on surface chemistry and plasmonics. For example, Li et al. have taken advantage of the biphasic properties of interfacial arrays to study the effect of solvation and hydrophobic forces on the adsorption of polyaromatic hydrocarbons to Au NPs in situ via SERS (Figure 3f–g). (21) More specifically, this was performed by introducing the polyaromatic hydrocarbons into either the aqueous or oil phase of an interfacial array, which allowed the adsorption kinetics, relative affinity, and stability of the polyaromatic hydrocarbon–metal complex to be studied in situ using SERS. In another work, Zhang et al. took advantage of the contractable properties of interfacial arrays to study the effect of particle–particle gap distance on interparticle plasmonic coupling. (71) The interparticle spacing in Au interfacial arrays was dynamically controlled by regulating the size of the interface via solvent evaporation. Using thiophenol as a Raman tag, the authors observed a rise in the SERS signal intensity of thiophenol as the NPs were pushed closer together during the initial stages of solvent evaporation, which was attributed to the formation of plasmonic hotspots. Interestingly, as the NPs continued to move closer with further solvent evaporation, the SERS signals decreased rapidly and eventually dropped by nearly 3 orders of magnitude. This was attributed to the possibility of quantum effects that start to occur at subnanometer gap distances which would significantly influence the electromagnetic field enhancement.
Interfacial arrays can also be conveniently deposited onto various types of supporting materials to form free-standing SERS enhancing substrates which combine the functionalities of the plasmonic NPs and the solid support (Figure 3h). (68) For example, Kelly et al. showed that Ag and Au interfacial arrays deposited onto quartz support can be used as an enhancing substrate for direct SERS discrimination of live and dead bacteria. (72) As shown in Figure 3i, this method involved in situ SERS analysis of the headspace of bacterial cultures, which typically contained a fermentative metabolite, dimethyl disulfide, that adsorbed spontaneously to the plasmonic NPs to generate strong SERS signals. The sensitivity limit of the method was found to be 1.5 × 107 CFU/mL. This corresponded to detection of bacterial growth within 15 min, which was significantly faster than traditional approaches. In addition, the authors showed that the method was effective for seven different types of bacteria and could be completed using a portable Raman spectrometer, which makes this a potentially viable method for rapid bedside detection of bacterial infections.
Table 1 provides a more comprehensive summary of: the types of plasmonic NPs that have been used for interfacial self-assembly; the types of solid materials that have been used as supports for converting interfacial arrays into solid SERS enhancing substrates; and the types of analyte molecules that have been studied. Despite the excellent progress shown above, there are still challenges that remain in the use of interfacial arrays in SERS. For example, direct characterization of interfacial arrays remain highly challenging, which makes it crucial to develop novel characterization techniques and/or protocols that allow surface chemistry and particle packing to be probed in situ. (73,74) This will pave the way for the development of novel methods to rationally design and control the assembly behavior of NPs at a single-particle level to fine-tune the plasmonic property and SERS activity of interfacial arrays.
Table 1. Examples of Different Types of NPs That Have Been Used in Construction of Interfacial Arrays, Different Types of Support Material That Have Been Used to Convert Interfacial Arrays into Solid Composite Materials, And Different Types of Analyte Molecules That Have Been Studied Using Interfacial Arrays with SERS
ParticleSupportAnalyte
Ag nanowire (53)Superhydrophobic needle (75)Parathion (pesticide) (76)
Au nanorod (77)Filter paper (78)MicroRNA (biomolecule) (79)
Au@Ag nanocube (80)Silicon wafer (81)Trinitrotoluene (explosive) (82)
Au nanostar (69)Polymer film (58)Cd2+ (pollutant) (51)
Ag nanooctahedra (83)Microparticle arrays (84)Pyrene (food contaminant) (85)
Au nanoprism (86)Metal electrode (87)Crystal violet (dye) (50)

2.3. Nanoparticle Aggregates: Hotspot Design Strategy

A fundamental scientific challenge in SERS is the interaction of the hotspot with the target molecule. Simply constructing abundant hotspots or positioning molecules near the surface of nanostructures is not sufficient to achieve highly sensitive and stable SERS detection. Therefore, in addition to the classical colloid agglomeration and rough-surface nanostructure film, various methods have been proposed that focus on both building nanostructures and guiding target molecules into plasmonic hotspots.

2.3.1. Dynamic SERS with 3D Hotspot Matrix

To address the challenges such as the low utilization efficiency of hotspots and the poor stability of classical colloidal agglomerations, Yang and colleagues proposed a dynamic SERS detection method based on the state translation of NPs from the wet state to the dry state, with measurements conducted during this process (including solvent and evaporating stages). Additionally, They introduced a 3D hotspot matrix formed during the evaporation process, characterized by minimal particle size polydispersity and maximum distance uniformity between particles (Figure 4a). (88) This 3D matrix can accommodate high-density hotspots, thereby enhancing SERS effect. To maintain the existence time of 3D hotspots, they further employed nonvolatile substances in the presence of glycerol to construct a dynamic SERS system with higher stability. (89) Glycerol with slow evaporation rate protects NPs and target molecules from photobleaching, thereby improving sensitivity and stability (Figure 4b-c). This dynamic SERS method is similar to the droplet levitating platform proposed by Yang et al. (see section 2.4). (90) The presence of the glycerol can also avoid additional laser effects, such as photobleaching, substrate heating or possible changes in substrate morphology. (91)

Figure 4

Figure 4. Dynamic SERS method. (a) Dark-field optical microscopy for real-time tracing of the morphological alterations of a specific aggregate in the evaporation process of a 1 mL sample. This sample of Ag sols was 10-fold diluted with 0.01 mM citrate solution to avoid overly strong light scattering. Reproduce from ref (88). Long-period and high-stability 3D hotspots for the detection of different molecules. SERS spectra obtained at different concentrations of (b1) NOD and (c1) TBZ in 5% glycerol system. Time-dependent Raman spectra of (b2) NOD and (c2) TBZ based on the long period and high-stability 3D hotspot matrix constructed with the assistance of 5% glycerol. (88,89) A liquid-phase SERS method. (d) Reversible O/W encasing for self-assembly of metal liquid-like Au nanorod arrays is realized in a common cuvette. (e) SERS spectra of TBZ with concentrations of 1, 101, 102, 103, and 104 ppm, respectively. (f) A linear plot of r780/662 against logarithmic concentration of pesticide. Reproduce from ref (14). Capillary capturing target molecules into small gaps method. Schematic diagram of the assembled NP film (g1–i1) and high-speed camera picture (g2–i2) from the initial wet stage to final dry stage of the monolayer NP film. (j) SERS spectra collected from a single A549 cell during the cell death process caused by photothermal stimulation. (k) Schematic diagram of the capillary SERS method for in situ monitoring of the single-cell death process under photothermal stimulation. Reproduced from ref (93).

2.3.2. Liquid Phase SERS Method Combining Hotspots Nanostructures Assembly with Measurement

It is well-known that 2D nanoarray assemblies of liquid/liquid and liquid/gas interfaces, as well as the 3D nanoarray assemblies of liquid/liquid interface, offer several advantages, including self-healing ability, variable shape and high mechanical stability. The reversible Oil/Water encasing strategy to self-assemble metal liquid-like 3D arrays could be achieved just by controlling the surface wettability of the containers, as shown in Figure 4d–f. (14) Different from the liquid–liquid self-assembly arrays, Yang and co-workers employed the phase equilibrium principle of a partially soluble triliquid system (solvent/chloroform/acetone) to construct a high-density hotspot matrix for dispersing antitumor drug in the blood. (92) This method enabled NPs to form a highly stable hotspot matrix with tunable particle gaps. Therefore, water-soluble antitumor drugs in blood could be dispersed rapidly and enter the hotspot matrix spontaneously. Liquid SERS analysis improves the hotspot utilization efficiency and dynamic tunability, facilitating the entry and enrichment of target molecules. In addition, trace molecules can be analyzed in either the single solvent phase or the dual-phase (such as the water/oil interface). The molecular reaction process can be controlled by the phase transfer or diffusion in the liquid dual-phase system, enabling the high-sensitivity detection and quantification of reactants, products, and possible intermediates in the liquid phase through SERS spectroscopy.

2.3.3. Capillary Guiding Molecules into Hotspot Nanostructures Methods

The capillary phenomenon, observed in plant ducts, plays a key role in water transport. Inspired by this, transpiration driven by capillary force has shown the potential to help target molecules enter SERS-active hotspots. Yang et al. developed the nanocapillary pumping model, as shown in Figure 4g–k. (93) Nanocapillary pumping, with solvent evaporation, enables rapid and sensitive SERS detection of various analytes. In this process, the abundance of hotspots and the inevitable entry of molecules into them enhance the effectiveness of the hotspots. Nanocapillary pumping method provides a novel way for generating hotspots that actively attract target molecules, and achieving 2–3 orders of magnitude higher sensitivity on a solid SERS substrate. (94) To enhance molecular accessibility to hotspots, Qin et al. designed a three-layer structure with an intralayer gap of ≈8 nm and an interbedded gap of <3 nm. Through capillary force pumping, the target molecules naturally entered into interbedded smaller gaps. (95) Chen et al. proposed a nanopocket model based on the capillary force using a common MoS2–Ag heterostructure. (96) In this model, target molecules were spontaneously introduced into the MoS2–Ag heterogeneous nanopocket structure, resulting in a 3–7 times increase in SERS signal intensity compared to the nanocapillary model. Overall, the strategy of capturing target molecules into small gaps based on the SERS hotspot substrates has proven to be effective and is expected to be further improved.

2.4. Enriching and Delivering Analytes from Solutions into the SERS Hotspot

Au and Ag micro/nanostructured surfaces are extensively utilized as SERS substrates. There are two main approaches for delivering analytes from solution onto the SERS substrates. The first method is to immerse the SERS substrates into the solution containing analytes, which typically requires several hours to reach the adsorption equilibrium. However, this approach is inapplicable to inert molecules with weak affinity to the SERS substrate. The second method is evaporating a droplet containing analytes on the SERS substrates, which results in an inhomogeneous distribution of analyte molecules owing to the “coffee ring” effect, i.e., the ring-like pattern left by the particle-laden liquid after it evaporates. (97) The pinning of the solid/liquid/gas three-phase contact line causes the “coffee ring” effect, leading to the accumulation of analyte molecules around the pinning circle. Generally, the analytes spread over square centimeters even using 10 μL of analyte solutions, while the laser spot used for SERS measurements is only a few micrometers in size. This means that only 1 in 0.1 billion alanyte molecules has the opportunity to be exposed to the laser. Therefore, it is crucial to concentrate analytes from diluted solutions into a tiny dot (ideally, micrometer size) and guide the analytes toward hotspots in an efficient manner after solvent evaporation to enhance the performance of SERS substrates.
Superhydrophobic surfaces can concentrate analytes in water into a spot after solvent evaporation and prevent the formation of “coffee ring”. For example, superhydrophobic surfaces with lithographically fabricated micropillars can concentrate and deliver biomolecules (e.g., lambda DNA, lysozyme) from highly diluted solutions to a specific area covered by an Au or Ag layer, enabling SERS detection even at attomolar concentrations (10–18 mol/L). (98) However, the wetting transition from the Cassie state to the Wenzel state when the droplet size reaches a threshold value (generally, submillimeter size) limits further size reduction of the analyte aggregate. (99)
A series of methods to concentrate analytes and overcome the unevenness in the process has been developed. For example, inspired by the pitcher plants, Yang and co-workers developed the slippery liquid-infused porous surfaces (SLIPS) based SERS platform. (100) SLIPS consists of a micro/nanoporous substrate membrane infiltrated with lubricating fluid, creating a smooth and stable interface that nearly eliminates pinning of the liquid contact line. (101) The interface enriches the analytes from both aqueous and nonaqueous liquids into a tiny spot, enabling ultrasensitive detection at attomolar levels and eliminating pinning effects for detection in various solvents. (100)
However, in the above platform, the lubricant liquid tends to wrap the liquid droplet and can interfere with SERS detection. To avoid this issue, another analyte enrichment and filtration (AEF)-SERS platform was proposed. (35) This platform consists of two key components. First, the molecularly smooth polydimethylsiloxane (PDMS) brush surface exhibits slippery properties due to the negligible adhesion forces between the droplet and the solid surface, facilitating analytes enrichment. The second critical component is the MOF-encapsulated Au nanorods. A thick layer of porous MOFs was grown onto the Au nanorods, to restrict molecules bigger than the pore size from contacting the Au nanorods and to prevent the formation of hot spots between Au nanorods. The reduction in SERS sensitivity due to the absence of SERS hotspots was compensated by the analyte enrichment effect provided by the slippery PDMS brush surface, which can shrink the analyte dispersion area by millions of times compared to common SERS substrates after solvent evaporation. The indiscriminate enrichment of analyte molecules during solvent evaporation poses challenges for identifying analytes in complex samples containing multiple species. The AEF-SERS platform successfully achieved the selective detection of 4-nitrobenzenethiol at nanomolar concentrations in whole blood. This method avoids the use of lubricants, and MOF filter greatly enhances the analyte identification capability in complex samples.
Self-assembled Au NP monolayers are ideal SERS substrates, while it is challenging to concentrate analyte molecules on the self-assembled Au NP monolayers without disrupting the hotspots. To address this, the slippery Au nanosphere monolayers were reported, which could enhance analyte concentration during solvent evaporation without disrupting hotspots. (81) To form these monolayers, a thin silica shell was used to wrap the Au nanosphere monolayer, allowing the subsequent functionalization of the PDMS brush monolayer. The monolayers concentrate analytes into a 3D Au NP/analyte aggregate after solvent evaporation, boosting SERS signals.
Despite these advances, it is challenging to concentrate all analytes into the final aggregate on slippery surfaces, as residues inevitably form due to wetting defects or evaporation. To overcome this, Yang et al. introduced the acoustic levitation technique into the SERS sensing system, enabling lossless analyte concentration from volatile liquids, solids, or air, facilitating multiplex and multiphase detection at femtomolar levels (Figure 5a). This droplet-levitating enrichment platform overcomes the limitations of slippery surfaces, achieving 20,000 times analyte enrichment and allowing sensitive SERS detection even at attomolar levels (Figure 5b). (90) The combination of this strategy and the nanoporous Ag microparticle SERS substrates (39) have the potential to provide fingerprint information on the molecule of interest with high sensitivity down to a single molecule level, expanding its applicability in the early diagnosis of diseases.

Figure 5

Figure 5. Scheme of enrichment process of dye molecules. (a) Schematic of the multianalyte and multiphase enrichment using the acoustic levitating platform and the consequent multiplex and multiphase SERS detection. (b) Enrichment process of dye molecules from 10 μL of ethanol solutions. Reproduced from ref (90). Copyright 2022, The Authors.

3. SERS Substrates: Solid Substrates

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3.1. 3D Plasmonic Hydrogel Structures

Hydrogels are interesting supports for a wide variety of applications. Their huge capacity to absorb and expel water offers plenty of possibilities for trapping molecular and even nanomaterials. For this reason, hydrogels have been combined with metal nanoparticles to fabricate nanocomposite materials with enhanced functionalities, including SERS sensing. In an early example, it was demonstrated that hydrogels loaded with Ag NPs could readily adsorb analyte molecules, which would be readily detected upon drying because of the reversible creation of hot spots between approaching Ag NPs. (102) Such a dynamic hot spot creation was possible thanks to the properties of the hydrogel and the uniform distribution of the nanomaterial. This review focuses here on the use of hydrogels for biomedical applications, mainly related to 3D cellular models of the real tissue.
Advances in biomodel design and additive manufacturing techniques, including 3D bioprinting, have motivated the implementation of monitoring techniques that are compatible with these biologically elaborate environments. The development of materials for biomodels must consider physical (stiffness, viscoelasticity, porosity), chemical (ionic strength, pH, hydrophilicity, biodegradability), and biological (biocompatibility, cytotoxicity, adherence) properties, aiming to support eukaryotic or prokaryotic growth, differentiation, maturation, and diffusion. While some of these properties are inherent to the biomaterial itself, through synthetic methods, additional functionalities can be incorporated resulting in highly optimized materials that also fulfill the required properties for 3D printing (e.g., thixotropic behavior, noncytotoxic gelling). With a special focus on hydrogels, although naturally derived hydrogels (e.g., alginate, collagen, agarose) typically display high chemical and biological compatibility, they have poor physical properties, which limits their use in 3D printing applications. Synthetic hydrogels, in contrast, often show poor biocompatibility, either due to the biological composition or their inflexible nature to cell-directed restructuring. A variety of materials and hybrid hydrogel-nanomaterial fabrication methods have been recently reviewed. (103) Aiming to study temporal and spatial changes within these complex biological models in a noninvasive manner, our attention is drawn to techniques that employ noninvasive interrogation methods and allow multiplexing (i.e., observation of multiple compounds/biomarkers in a single measurement). As presented in previous sections, SERS is an excellent choice to achieve these goals, featuring the use of near-infrared (NIR) excitation wavelengths for bioimaging or detection of Raman-active (bio)molecules. An attractive approach is the use of “SERS nanotags”, i.e., plasmonic NPs such as Au nanostars or nanorods, coated with Raman reporter molecules and an outer polymeric shell (see section 4 below) that improves stability in biological environments and can be used to provide cellular targeting. (104) SERS nanotags can be readily incorporated into naturally derived or synthetic hydrogels, providing a SERS-active substrate that can be deposited or printed in a variety of geometries. More details about SERS nanotags will be discussed in section 7. Alternatively, the incorporation of bare Au nanostars or nanorods can be used for the label-free detection of Raman-active biomarkers with high spatial resolution. However, conducting SERS in biological environments carries an intrinsic complication due to protein fowling and potential loss of SERS signal intensity and peak resolution. (105)
For the design of hydrogel scaffolds, incorporation of plasmonic NPs in the hydrogel composition requires optimization of their concentration, to achieve meaningful signals in direct SERS sensing. In a recent example, it was shown that increasing Au nanorod concentration led to increased SERS signal for 3D mapping based on the peak at 1083 cm–1 from 4-mercaptobenzoic acid (MBA), whereas excessive absorption (opacity) and scattering (turbidity) of light by Au nanorods significantly hindered SERS intensity (Figure 6a). This impaired penetration effect becomes more relevant at increasing depth into the 3D biomodel, which remains an important issue in SERS bioimaging. (105,106) However, SERS achieves substantially better penetration depth than the current gold standard technique, confocal fluorescence microscopy (CFM), reaching over 1000 μm in depth in direct SERS detection, both in bare hydrogel structures and in those containing cells. (107) This improved light penetration is possible because in Raman microscopy one can readily use longer excitation wavelengths, e.g., 788 nm (within the first biological transparency window) when Au nanorods or Au nanostars are used as plasmonic substrates. On the other hand, by working in the biological transparency window, the hydrogel itself allows transmission of light with longer wavelengths because of reduced scattering and negligible water signal. (108) In terms of xy resolution in hydrogels, equivalent spatial distribution has been demonstrated for imaging of breast cancer tumoroids using CFM and SERS. (106) In this case, a heterogeneous cell population was used, which was labeled with both fluorophores and SERS nanotags; both CFM and SERS microscopy could image the same region of a hydrogel scaffold designed to support tumoroid growth (Figure 6b). Again, because SERS can penetrate deeper into biological models, cell migration could be observed in regions that would otherwise not be distinguished using CFM. (107) Due to the relatively recent application of SERS as an imaging technique for complex 3D biomodels, it is important to prove the temporal and spatial correlative nature of CFM and SERS, so that SERS can be established as a reliable technique for in situ bioimaging.

Figure 6

Figure 6. Raman imaging of SERS hydrogels. (a) 2D imaging of the SERS intensity at 1083 cm–1 in hydrogels with different Au nanorod concentrations: (i) 0.1 mM, (ii) 0.5 mM, (iii) 1 mM, and (iv) 1.5 mM, respectively. Adapted with permission from ref (105). Copyright 2020, John Wiley and Sons. (b) Comparison between the CFM imaging and high-spatial-resolution SERS imaging of the hydrogel scaffold supporting SERS nanotag-labeled tumoroids. Adapted with permission from ref (106). Copyright 2024, The Authors.

A key advantage of SERS nanotags is the potential to create a library to label multiple cell populations. (109) When applying such a library, it is important to correctly match SERS tag intensity with respect to relative cell uptake and cell number in mixed cell populations. In other words, “bright” SERS nanotags should combined with cells present in low numbers, whereas “dim” SERS nanotags should be used for cells in high numbers or with high levels of endocytosis (i.e., phagocytic cells). This method was recently applied to label different cell types in a 3D printed mimic of human skin, modeling melanoma. (110) In this case, the brightest SERS nanotags were used to follow metastatic cell movement across a basement-membrane, prelabeled with a separate and distinguishable SERS tag. As it happens, theory and practice may offer conflicting results, and whereas multiplexing using SERS nanotags can be readily achieved in 2D cell cultures, (111,112) application in 3D models containing biomolecules and scattering components may lead to complications in distinguishing each SERS signal. Fortunately, accurate spectral analysis by machine learning can be used to enhance the signal-to-noise ratio, particularly along the z-axis. (104,110)
Anisotropic plasmonic NPs can also be used for direct SERS in hydrogels, e.g. to monitor the diffusion of cytotoxic drugs (113) or the presence of tumor-associated biomarkers. (106) Here, the potential of SERS as a 3D spectral imaging technique truly shines, as molecules lacking fluorescent emission can be clearly located with spatial and temporal resolution (termed 4D imaging). Again, the importance of minimizing scattering by the host material is paramount. In studies where hydrogel scaffolds containing Au nanorods were filled with cells and extracellular matrix, SERS sensitivity was often reduced in all imaging planes (xyz). (113) Some other aspects should be considered when applying hydrogels loaded with Au NPs. On one hand, reduced SERS signals might result from reshaping or aggregation of anisotropic NPs in biological environments, which can be monitored by UV–vis-NIR spectroscopy and electron microscopy. (104,106,114) Additionally, it has been reported that incorporation of Au nanorods may affect the rheological properties of hydrogels. Although some works report on reinforced hydrogel networks upon addition of inorganic NPs to polymer gels, the general observation is that such hybrid materials must be optimized and well characterized over a range of NP concentrations. (115) For example, addition of Au nanorods at a final concentration of 0.5 mM (Au0) to hydrogels made of methacrylated hyaluronic acid (HAMA) was shown to reinforce the viscoelastic nature of the hydrogel matrix, but no further improvements were noted above 0.5 mM. (105) Similarly, the recoverability of synthetic inks based on polyethylene glycol diacrylate (PEGDA) was shown to be dependent on the concentration of incorporated Au nanorods. (114)

3.2. Nanoparticle Metal Organic Frameworks

Direct SERS analysis in complex fluids, or direct measurements in the sample media upon contacting the plasmonic materials, is often constrained by the nonspecific coadsorption, onto metallic nanostructures, of other species in the matrix solution. (116,117) Such undesired events can significantly increase the complexity of the vibrational assignment or even completely prevent the interaction with the target analyte. Thus, it is of utmost importance to design new plasmonic substrates capable of inhibiting the competitive adsorption of nontargeted elements on the active surfaces. (118) In the case of real biological or environmental samples, a myriad of elements contributes to the final composition, including inorganic ions, cells (i.e., sample cells, bacteria, fungi and algae, etc.) and large molecules such as proteins, lipids, polysaccharides, biofilms, nucleic acids, humic and fulvic substances and other molecules originated from the metabolism of the life (i.e., cells, plants, fish and other animals). At the same time, anthropogenic factors related to human activities (agricultural, industrial, domestic, etc.) can also play a key role in determining the quality of natural samples.
As a large fraction of the species in natural waters or biological fluids are large, their interaction with the plasmonic surface can potentially be restricted by using additional components with molecular sieving properties. These materials may range from porous polymers to inorganic oxides. (118) Thus, it has been very common to coat plasmonic NPs with porous silica (Figure 7a). (119) This approach, however, presents some drawbacks. For example, it is known that single spheres, (120) the most common material, present limited electric film to enhance the Raman signal. To address this issue, other NPs capable of behaving as single hotspots (i.e., nanostars) have been used. (121) Notwithstanding, this approach still encounters some challenges. The fact that the silica coating is in direct contact with the plasmonic material restricts the ability of the optical enhancer to interact with the analytes. To resolve this issue, the use of yolk shells, where the plasmonic materials are separated by a void from the coating, has been employed─for example, with nanostars (Figure 7b). (122) Alternatively, films of NPs deposited on discrete microparticles of polystyrene, to form dense collections of hot spots, that are posteriorly coated with mesoporous silica and converted into hollow capsules through the dissolution of polymer core had been also successfully developed (Figure 7c). (123,124) These strategies have led to advanced functional materials with improved operation in complex samples (125,126) but still exhibit some limitations. First, the pore size is not easily controllable, and second, while these materials may sieve large cellular or molecular entities, they do not impose restrictions on the myriads of small molecules that may be present in the sample. (122)

Figure 7

Figure 7. Fabrication of MOFs. (a) TEM images of Au spherical particles coated with a homogeneous silica shell. Of 98.5 ± 7.0 nm. Adapted with permission from ref (119). Copyright 2009, American Chemical Society. (b) TEM images of a single Au nanostar mesoporous silica coated yolk shell. Adapted with permission from ref (122). Copyright 2019, Royal Society of Chemistry. (c) SEM and TEM/XED analysis of mesoporous silica shells containing Au island films in their interior. Adapted with permission from ref (123). Copyright 2019, John Wiley and Sons. (d) SEM image of core–shell Au nanostars coated with MOF (ZIF-8). Adapted with permission from ref (133). Copyright 2019, John Wiley and Sons. (e) TEM image of yolk shell Au nanostars coated with MOF (ZIF-8) after dissolution of the inner ZIF-67. Adapted with permission from ref (128). Copyright 2023, John Wiley and Sons. (f) STEM images (bright and dark field) for PS@Ag, Ag@ZIF-thin, and thick plasmonic-MOFs. Adapted with permission from ref (135). Copyright 2024, John Wiley and Sons.

In this scenario, plasmonic MOFs have emerged as a significant area of research in the field of SERS, primarily due to their unique structural properties and the ability to enhance Raman signals through localized surface plasmon resonance (LSPR). (127) The incorporation of MOFs into this system provides additional benefits, such as high surface area, tunable pore sizes, shapes and chemical environment, and the ability to selectively capture target analytes, which further enhances the SERS performance. (128,129) Recent studies have demonstrated that the structural characteristics of MOFs can be tailored to optimize their interaction with plasmonic NPs. For example, the design of core–shell structures, where plasmonic NPs are encapsulated within a MOF shell, has been reported to enhance the stability and sensitivity of SERS substrates. (130,131) This configuration not only protects the NPs from environmental degradation but also allows for a more controlled interaction with target molecules, leading to improved SERS signals. Additionally, the high porosity of MOFs enables the efficient diffusion of analytes into the framework, facilitating their interaction with the plasmonic surface. (132) Notwithstanding, plasmonic MOFs are subject to the same drawbacks of generation of electric field enough to yield adequate SERS signals. Thus, analogous as with the silica coatings, nanostars core/shells (Figure 7d), (133) yolk shells, prepared upon the use of two different MOFs, the inner soluble in water and the outer insoluble (Figure 7e), (128) or plasmonic films on discrete polystyrene beads (Figure 7f), (134) had been extensively used as plasmonic cores.
The versatility of MOFs extends to their functionalization, which can be achieved through postsynthetic modifications. This allows researchers to tailor the chemical environment of the MOF to enhance its affinity for specific analytes, thereby improving the selectivity and sensitivity of SERS detection. (136,137) For instance, the incorporation of functional groups into the MOF ligands can promote stronger interactions with target molecules, leading to enhanced Raman signals. Furthermore, the ability to modify the pore size and surface chemistry of MOFs enables the selective capture of analytes based on their size and chemical properties, which is particularly advantageous in complex sample matrices (138) or even to classify between chiral molecules. (139,140)
The application of plasmonic MOFs in SERS has been successfully demonstrated in various studies. For example, MOF coated Ag NP was used for the sensitive detection of pesticides in apple samples. (141) Similarly, the development of a three-dimensional MOF-based SERS platform has shown promise for real-time atmospheric monitoring, highlighting the potential of these materials in environmental applications. (142,143) The hydrophobic nature of certain MOFs can be engineered to enhance their interaction with nonpolar or weakly polar analytes. This modification is crucial for enhancing the interaction between the analyte and the plasmonic surface, as it allows for a greater concentration of target molecules in proximity to the SERS-active sites. Additionally, studies have demonstrated that the hydrophobicity of MOFs can be adjusted through various synthetic strategies, such as incorporating hydrophobic organic ligands, which further enhance their ability to trap hydrophobic guests. (144) This property has been effectively utilized to create molecular traps that facilitate the detection of analytes that typically lack a SERS signal due to insufficient interaction with plasmonic surfaces. (134) The use of ion-selective dyes as chemosensors rather than as analytes (145) represents a novel approach in the development of SERS-based sensors for transition metals. By leveraging the tunability of MOFs and their ability to selectively capture specific ions, it is possible to design sensors that exhibit enhanced SERS signals for transition metal detection. This method capitalizes on the interaction between the analyte and the plasmonic substrate, which is crucial for generating detectable Raman signals (Figure 8a). (134) Moreover, the integration of MOFs with plasmonic materials has opened new avenues for the development of multifunctional sensing platforms. For instance, by leveraging the hydrophobic properties of MOFs in combination with the photothermal excitation of gold nanostars within the biological window, it is possible to create a drug delivery scaffold that can simultaneously monitor the release kinetics using SERS. This can be achieved by employing the same light source both for the generation of heat and for subsequent drug release through thermal diffusion (Figure 8b). (133) Also, the combination of MOFs with other nanomaterials, such as carbon-based materials or metal oxides, can lead to enhanced SERS performance while also providing additional functionalities, such as photocatalytic activity or electrochemical sensing capabilities. (132,146) This multifunctionality is particularly beneficial in complex sensing scenarios where multiple analytes need to be detected simultaneously. In fact, this photocatalytic process can also be directly achieved by using the MOF as the catalyst and illuminating the plasmonic material with visible light to generate hot electrons. By using the same light source for both the catalytic process and SERS monitoring, in an operando spectroscopy setup, it is possible to obtain direct and detailed information on the kinetics and catalytic mechanisms involved (Figure 8c). (135)

Figure 8

Figure 8. Applications of SERS MOFs. (a) (i) SERS spectra of different concentrations of bathocuproine adsorbed on PS@Ag@ZIF-8. (ii) SERS spectrum of the sediments obtained after several washing cycles. (iii) Comparison between the SERS intensities of bathocuproine (band at 1377 cm–1) on PS@Ag@ZIF-8 and PS@Ag after the same washing samples. (iv) SERS spectra of bathocuproine on PS@Ag@ZIF-8 upon immersion into Cu(II) solutions in PBS buffer at different copper concentrations (from 38 to 254 ppb). Adapted with permission from ref (134). Copyright 2023, American Chemical Society. (b) (i) SERS spectral kinetics acquired on a nanostar coated with MOF and loaded with a nucleus dye (Hoechst 33258, HOE) with and without NIR illumination (785 nm); solid and dashed lines represent the time points 0 and 8 h, respectively. Kinetics was built with the SERS intensity of the ring breathing mode of HOE (980 cm–1). (ii) White light image (top) and SERS images of a single cell with the MOF composite time 0 (middle) and after 8 h of irradiation (down). (iii) Confocal microscopy image of cells incubated with core–shell containing HOE before and after NIR treatment. Blue and orange colors represent HOE and cell membrane staining (CellMask Deep Red), respectively. Adapted with permission from ref (133). Copyright 2019, John Wiley and Sons. (c) Photocatalytical degradation curves of rhodamine B under green illumination. Intensity corresponds to band 1202 cm–1. SERS spectra are presented as a 3D graph to guide the diminishing of the intensity across the full spectrum. Adapted with permission from ref (135). Copyright 2024, John Wiley and Sons.

Plasmonic MOFs represent a promising class of materials for enhancing SERS detection capabilities. Their unique structural properties, coupled with the ability to tailor their chemical environment, provide significant advantages in terms of sensitivity, selectivity, and stability. As research in this field continues to advance, it is expected that plasmonic MOFs will play a pivotal role in the development of next-generation sensing technologies across various applications.

3.3. Semiconductor SERS Substrates

Semiconductor materials that exhibit low toxicity and good biocompatibility serve as ideal candidates for the investigation of biologically relevant systems. (147) To date, a variety of semiconductor materials have been demonstrated to exhibit SERS activity, including metal oxides, metal sulfides, metal nitrides, element semiconductors, and organic semiconductors. (148) The design of semiconductor substrates with high enhancement factor is a primary challenge that must be addressed for the development of semiconductor SERS-based applications. In this regard, the first aspect to consider is the enhancement mechanism. Similar to metal SERS, the Raman signal of a molecule on the semiconductor surface is also enhanced through the generating of charge-transfer (CT) transitions and/or the amplifying electric fields. (149−151) The strategies to design and optimize SERS-active semiconductor substrates based on these two enhancement mechanisms are presented below.

3.3.1. Semiconductor Substrates Based on CT Mechanism

In theory, Herzberg–Teller vibronic coupling enables the Raman scattering of a molecule to borrow intensity from CT transitions between the molecule and the semiconductor. (152) Therefore, creating CT routes that resonate with incident photons is crucial for enhancing the CT contribution to SERS. Several strategies involving the introduction of intermediate energy levels within the bandgap have been proposed: (1) introducing surface defects through doping or nonstoichiometric synthesis to enhance the CT interactions at the semiconductor–molecule interface; (153−155) and (2) increasing the electronic density of states by utilizing amorphous semiconductor nanostructures to improve CT efficiency. (156) Besides, creating multiple CT coupling pathways by integrating exciton and/or molecular resonances can further enhance the CT contribution to SERS. (157,158)
In addition, several new modulation methods have emerged to develop sensitive SERS-active semiconductor substrates based on CT mechanism. (159−162) For example, improving the separation of photogenerated electron–hole pairs through semiconductor/semiconductor heterojunctions has demonstrated a promising pathway. (159−161) The formation of the TiO2/ZnO heterojunction results in strong coupling at the interface between the two semiconductor materials, (159) which facilitates an efficient separation of photogenerated electron–hole pairs, which can be supported by photoluminescence spectra and electrochemical impedance spectroscopy. The accumulation of photogenerated electrons enhance the CT efficiency and SERS activity. Furthermore, a TiO2-x facet heterojunction with abundant oxygen vacancies has been shown to boost CT efficiency, enabling ultrasensitive detection and photocatalytic degradation of various antibiotic residues in real water system. (161) In addition, another innovative approach to further enhance CT efficiency has been proposed by drawing inspiration from the modulation techniques employed in dye-sensitized solar cells. (162) This method involves the use of lithium ions to modulate TiO2 nanostructures, which has been demonstrated to enrich the surface states and lower the conduction band edge of TiO2. Such modulation facilitates the CT interaction between the molecule and TiO2, resulting in an enhancement factor approximately 100 times greater than that of pure TiO2 substrates.

3.3.2. Semiconductor Substrates Based on Electromagnetic Mechanism

Recently, studies on the electromagnetic mechanism of semiconductor SERS have achieved considerable progress. Unlike metals, which exhibit LSPR due to their high density of free electrons, semiconductors primarily achieve electromagnetic enhancement through morphological and structural optical effects, such as light trapping, subwavelength focusing, and Mie resonance. In theory, efficient electromagnetic enhancement around semiconductor materials typically requires semiconductors with a high refractive index, along with a small imaginary part of their dielectric functions. For example, simulation results indicate that the electromagnetic field induced by Mie resonance in spherical silicon can reach an intensity equal to or even greater than that of gold plasmons at specific particle sizes. (163)
Semiconductor with specific micro/nanostructures can effectively regulate the near-field optical properties. For instance, a TiO2 electrode with a nanotubular geometry can concentrate electric fields within the nanostructures due to scattering and interference effects. (164,165) Additionally, a core/shell resonator composed of SiO2/TiO2 beads was created through the precise deposition of an ultrathin anatase TiO2 layer on the surface of SiO2 NPs. (166) The differences in refractive index between the SiO2 core and the TiO2 shell facilitate the total internal reflection of light within the shell layer. Concurrently, the electric field generated by multiple light scattering significantly amplifies the Raman scattering of analytes adsorbed onto the resonator surface. Similarly, a TiO2 inverse opal structure demonstrates improved SERS sensitivity through the multiple light scattering effect observed in the photonic microarray. (167)

3.3.3. Semiconductor substrates based on multiple enhancement mechanism

In the visible and NIR regions, near-field optical effects typically require the semiconductor materials with feature sizes ranging from submicrometers to micrometers. However, efficient CT transitions usually occur in semiconductor-molecule systems at the nanometric scale. Therefore, the rational design of semiconductor superstructures offers an effective approach to integrate these two enhancement mechanisms. In this context, the ZnO superstructure model (Figure 9a) presents an opportunity to incorporate multiple resonance effects, including CT resonance, Mie resonance, and molecular resonance (Figure 9b, c). (168) The submicron superstructures are constructed from small-sized nanocrystals, which can generate electromagnetic field enhancement due to the Mie resonances. Simultaneously, the nanocrystals can significantly enhance CT transitions owing to the size-dependent electronic properties in semiconductors. Furthermore, the ZnO superstructure coated with ZIF can further amplify the local electromagnetic field surrounding the particle surface, rendering it suitable as a sensing platform for the sensitive detection of volatile organic compounds. (169)

Figure 9

Figure 9. ZnO superstructures and related optical properties. (a) Diagram that illustrates the microstructure of a ZnO superstructure comprised of closely packed nanocrystallites. (b) The comparison of enhancement factor and near-field scattering efficiency observed on ZnO superstructures with different diameters under the 532 nm excitation. Inset shows the corresponding SEM and electric-field distribution images of ZnO superstructures. (c) Potential CT transition pathway and vibronic coupling mechanism. Adapted with permission from ref (168). Copyright 2019, Wiley-VCH Verlag.

Besides, a hollow multishelled V2O5 microstructure has been demonstrated to enhance the sensitivity of semiconductor SERS. (170) This enhancement arises from the unique multishelled configuration, which facilitates multiple reflections of electromagnetic waves, thereby providing efficient resonant absorption for CT and exciton enhancement. Furthermore, the coupling of the outer and inner shells significantly contributes to a heightened electromagnetic field, further promoting SERS enhancement. Very recently, a novel method for the large-area fabrication of TiO2 microspherical arrays has been proposed, combining flame thermal-assisted synthesis with screen-printing technology. (171) This method represents the simplest strategy among the reported SERS-active semiconductor substrates, yet its achieves high sensitivity by integrating multiple synergistic resonances, including Mie resonance, CT resonance, and molecular resonance.

3.3.4. Applications of Semiconductor Substrates

The expansion of SERS-active semiconductor substrates with high performance provides new opportunities for the development of SERS applications. Notably, several SERS-active semiconductor substrates also demonstrate a variety of catalytic activities, such as photocatalysis, electrocatalysis, and nanozyme catalytic functions. (172−174) For a comprehensive discussion on these applications, we recommend the recent review by Itoh et al. (151) Here, we will introduce the direction for semiconductor SERS in catalysis.
Recently, a unique class of nanomaterials capable of mimicking enzyme functions and demonstrating substantial potential in biomedical detection. (175) The careful design and construction of semiconductor nanomaterials that integrate both SERS and nanozyme effects possess significant implications for ultrasensitive nanozymes-based detection. (176,177) Song et al. have constructed reduced MnCo2O4 nanotubes that serve as efficient SERS substrates with oxidase-like catalytic properties. (178) By establishing a kinetic model for SERS detection of nanozyme catalysis, they found that the affinity of the SERS kinetic strategy is greater than that measured by ultraviolet–visible absorption spectroscopy. This finding suggests that SERS technology can provide ultrasensitive monitoring of the catalytic processes of single-layer molecules at catalyst active sites, thereby laying the foundation for ultrasensitive detection of nanozyme catalysis. Additionally, Song et al. constructed NiCo2S4 transition metal sulfide nanosheets, which exhibit excellent laccase and SERS activity, demonstrating great potential for detecting various catechin molecules. (179)
In addition, Huang et al. constructed biodegradable nanozymes based on γ-MnOOH, demonstrating that the oxidase-like properties of γ-MnOOH nanowires can decompose into inactive Mn2+, thereby enabling ultrasensitive detection of acetylcholinesterase, methomyl, and dichlorvos. (180) Yin et al. developed a single-atom nanozyme, Fe-SA/Ti3C2Tx with peroxidase activity, which also exhibited SERS activity. Through linear discriminant analysis and heatmap data analysis, they achieved the simultaneous identification of five antioxidants: ascorbic acid, uric acid, glutathione, melatonin, and tea polyphenols. (181) Li et al. constructed Mo2N NPs with exceptional peroxidase activity and SERS activity, enabling indirect SERS detection of glutathione, alpha-fetoprotein, and carcinoembryonic antigen in serum samples. (182) Yang et al. prepared biocompatible Cu3SnS4 nanosheets, which generated a substantial amount of reactive oxygen species, mediating oxidative damage to cells under visible light irradiation and facilitating photothermal disruption of bacterial membranes under NIR irradiation, to advance antibacterial therapies. (183) The innovative properties of these SERS and nanozymes provide significant advantages for potential applications in biomedical detection and diagnostics, with their enhanced sensitivity and efficiency poised to revolutionize disease diagnosis and management.

4. SERS Nanotags

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4.1. Fundamentals of SERS Nanotags

SERS nanotags (also known as SERS nanoprobes or SERS NPs) are engineered NPs designed to enhance Raman signals for sensitive detection of biomolecules. They typically consist of four components: noble metal NPs, Raman reporter molecules, a protective layer, and a modification layer (Figure 10). (184) The noble metal NPs serve as the enhancement substrates, to amplify the Raman signal of the reporter molecules through electromagnetic field and/or chemical enhancement mechanism. Since noble metal nanostructures with different morphologies exhibit varying SERS enhancement capabilities, it can effectively optimize the SERS performance of nanotags by rationally designing the morphology of metal substrate. Nanospheres and nanorods are the most used materials. Also, SERS substrates with different morphologies have been investigated to maximize enhancement performance, such as nanostars, nanocages, nanoflowers, and nanowires. (185,186) By constructing sharp tips, corners, or internal gaps on the surface of NPs, more hotspots can be generated, leading to a significant increase in Raman signal intensity.

Figure 10

Figure 10. Typical fabrication process of SERS nanotags: noble metal NPs, Raman reporter molecule adsorption, protective layer coating, and modification layer attachment.

The metallic core is usually coated with Raman reporter molecules that provides a distinct spectral fingerprint, allowing for easy identification. Raman reporter molecules typically have large Raman cross sections and are adsorbed onto the NP surface by self-assembly chemical or electrostatic interactions. Fluorophores with an absorption wavelength close to the laser excitation wavelength have been widely used. Because their signals are further enhanced by SERRS, they are known as resonance Raman reporter molecules. (187) They provide higher Raman signals but also increase fluorescence background. Small organic molecules containing thiol or amino groups show lower background and high affinity to the metal substrates. Their characteristic peaks are distinct and easily assigned, minimizing overlap with the spectral bands of other Raman reporter molecules. In recent years, “orthogonal Raman reporter molecules” with Raman features in the biological silent region (1800–2800 cm–1) range have been reported (discussed in section 4.3). These molecules typically exhibit vibrational modes of functional groups such as alkynes, nitriles, and azides. By avoiding interference from biological background peaks, they provide distinguishable peak information.
The protective layer increases the stability and biocompatibility of the nanotags. Common materials for the protective layer include biomolecules (liposome, phospholipid coating layer, bovine serum albumin), silica (solid or mesoporous silica layer), polymers, thiol-PEG, dopamine, MOF, etc. More information about the protective layer can be found in a related review. (188) At present, thiol-PEG is the most widely used, which can be designed to have carboxylic acid groups or amine group at another terminal of molecular structure. In recent years, biomimetic protective layers with conjugated biomarkers are also developed, including cell membrane, and exosome coatings (discussed in section 4.5).
The modification layer, on the other hand, involves the attachment of biorecognition molecules to enable the nanotags to bind to target biomolecules with high specificity and affinity. These biorecognition molecules include oligonucleotides, antibodies, peptides, and aptamers. The affinity of biomolecules to SERS nanotags could be electrostatic or chemical interactions. For example, negative electronic antibodies can be immobilized with poly-l-lysine modified plasmonic cores for surface coating. Carboxy-reactive groups and primary amine groups can cross-link to form amide bonds in the presence of 1-ethyl-3-(3-(dimethylamino)propyl) carbodiimide (EDC) and N-hydroxysuccinimides (NHS); this method is usually used to modify, label, cross-link or immobilize peptides, proteins, antibodies, and oligonucleotides through their 5′ phosphate groups to the surface of nanotag’s protective layer.

4.2. SERS Nanotags with Interior Gaps

As a novel type of SERS nanotags, gap-enhanced Raman tags (GERTs) with built-in Raman reporters have attracted much attention in recent years. (189) The structure of the GERT typically consists of a metal core, a metal shell and a Raman-active isolation layer in between (Figure 11a). GERTs exhibit the following advantages: ultrahigh brightness with enhanced plasmonic enhancement within the interior gaps; (190,191) excellent colloidal stability and signal photostability, as the Raman reporter molecules are protected by metallic shell structures; stable SERS responses even in an aggregated state, which holds potential in quantitative Raman analysis; suitable for multiplexed encoding by embedding multiple Raman molecules in the internal gap, or different Raman molecules in different gap layers for multishell GERTs. (192) Due to their superior performance, GERTs have been widely used in areas such as information security, (193,194) fast imaging, (195) biosensing, (196−199) in vivo detection and imaging, (200−203) and multifunctional theranostics. (204−206) To fabricate GERTs with high sensitivity, specificity, and stability, the rational design of the internal gap structure, selection of Raman reporter molecules and substrate materials, and control of particle morphology are required.

Figure 11

Figure 11. Characterization and formation mechanism of GERTs. (a) Geometrical illustration of a GERT. (b) The molecular structures, (c) TEM images, and (d) SERS spectra of GERTs with different organic molecules embedded. Reproduced from ref (194). Copyright 2020, The Authors. (e) GERTs with interior gaps of different thickness. Reproduced from ref (211). Copyright 2018, American Chemical Society. (f) Growth mechanism illustration using molecular dynamics simulation. Reproduced from ref (212). Copyright 2021, American Chemical Society. (g) Different core morphologies of GERTs. (i) Petal-like shell GERTs. Reproduced from ref (203). Copyright 2019, The Authors. (ii) Au bipyramid GERTs. Reproduced from ref (214). Copyright 2024, The Authors. (iii) Au nanotriangle GERTs. Reproduced from ref (215). Copyright 2019, American Chemical Society. (iv) Dual-gap nanodumbbell GERTs. Reproduced from ref (216). Copyright 2023, Wiley. (v) Multishell nanorod GERTs. Reproduced from ref (217). Copyright 2016, Elsevier. (vi) Au petal-like core with Ag shell GERTs. Reproduced from ref (218). Copyright 2023, The Authors.

4.2.1. Designing Interior Gaps of GERTs

The internal gaps of GERTs are typically in the subnanometer to few nanometer range. The size of the internal gap in GERTs determines the plasmonic coupling between the metal core and the shell, as well as the near-field enhancement within the gap structure. Several methods for fabricating ultrasmall internal gap structures have been reported and can be divided into two main categories. In the first category, the gap structure is hollowed out by a galvanic exchange reaction between Au and Ag to make a hollow intrananogap when the Au shell is fabricated, and the nanogap can be further filled with Raman-active molecules preadsorbed onto the Au core. (207) However, it is usually difficult to reduce the gap size to subnanometer level using this method. The second and more widely used method involves the preparation of a metal core, then an isolation layer is elegantly deposited around the core to form a junction with subnanometer to nanometer thickness and to separate the core and shell. After the high coverage of the insulating layer around the core, a secondary metallic layer (shell) is formed by direct reduction of the metal on the surface of the insulating layer until the core is completely encapsulated. Materials that have been reported as isolation layers include oligonucleotides modified with fluorescent dyes, Raman-active polymers, and aromatic small molecules. (208,209) These intermediate layers also serve as the source of Raman signals for the entire particle.
The synthesis of embedded Raman-active isolation layers with high precision, yield, and batch–batch reproducibility is critical for the successful fabrication of GERTs. The specially designed oligonucleotide chains modified with fluorescent molecules, (190) while effective as the insulation layers, are limited by cost and availability, making large-scale production difficult. Aromatic Raman-active polymers as the insulation layer also demonstrate uniform signals; (208) they are relatively cost-effective, but not widely or commercially available. In contrast, small aromatic organic molecules are accessible, inexpensive, and feasible. They have strong metal affinity, with functional groups such as thiols, amines, or carboxyl groups that allow direct modification of the metal surface, simplifying synthesis. (194) Therefore, the fabrication of the interfacial layer using the organic molecules remains the most robust and feasible method. Figure 11b–d shows the molecular structures, TEM images, and Raman spectra of GERTs with different organic molecules embedded. (194)
Studies have shown that the layer of embedded Raman reporter molecules is a key factor in the formation of the gap structure, with the thickness of the molecular layer directly determining the gap size. (210) To explain this, GERTs embedded with 1,4-benzenedithiol (BDT) could be used as an example. The 1,4-BDT molecule contains two thiol groups that allow the molecules to form disulfide bonds with each other. When 1,4-BDT is mixed with an Au core for a sufficient time, the molecules self-assemble into a multilayer structure on the Au surface. By controlling the mixing time of 1,4-BDT and the Au core, molecular layers of different thicknesses can be obtained. After growing the Au shell, GERTs with different gap sizes can be fabricated (Figure 11e). (211) In short, optimizing the gap size parameters during synthesis provides a strategy for fabricating GERTs with better Raman performance. Molecular dynamics simulations further confirmed that the surface molecular layers of different thicknesses are compressed during shell growth, changing their orientation and stacking patterns; this results in the formation of gap structures of different thicknesses (Figure 11f). (212)
The introduction of resonance Raman reporter molecules can further increase the sensitivity of GERTs, but these molecules often produce strong fluorescence backgrounds when in resonance with the laser, which can interfere with biological detection and imaging. In recent years, orthogonal-GERTs by embedding alkyne and deuterium-based molecules in the interior nanogaps were developed. (213) They provide characteristic Raman peaks in the 1800–2800 cm–1 range, showing improved photostability and biological stability and background interference-free capability, leading to accurate quantification onto various substrates and in complex media. For example, Au nanorod-based GERTs with embedded 4-mercaptobenzonitrile (4-MBN) molecules have been reported for sentinel lymph node imaging free from interference from light and tissue background. (200)

4.2.2. Design of the Core and Shell Structure

The regulation and design of the morphology of the plasmonic noble metal core or shell in GERTs are of great significance for obtaining highly active GERTs substrates. To fabricate GERTs, the critical step is to select a metallic core, which determines the number of SERS hot spots and provides the space for Raman reporter adsorption. The factors should be considered include the core size, monodispersity, and their capability for robust conjugation with isolation layers. At present, the most common studied GERTs were spherical core–shell structures, using Au or Ag nanospheres as the core. Compared to spherical GERTs, the petal-like multibranched shell structure (Figure 11g, i) provides higher enhancement factors, as the gap structures between the petals offer the more electromagnetic hotspots. Those petal-like GERTs facilitates fast Raman imaging (219,220) and sensitive SERS sensing. (221) This petal-like rough surface is usually induced by the embedding Raman molecules in the insulation layer. So far, several molecules, including oligonucleotides, (222) 4-nitrobenzenethiol (4-NBT), (219) and 4-MBN (223) have been reported to form such shells. But similar thiols, such as 1,4-BDT, 2-naphtalenethiol (NT), biphenyl-4,4-dithiol (BPDT) (i.e., molecules in Figure 11b) do not support the formation of petal-like GERTs. (219) The growth mechanism behind still requires further studies.
In recent years, various GERTs with different core morphologies have been developed, including nanorod GERTs, (214) nanobox GERTs, (224) Au bipyramid GERTs (214) (Figure 11g, ii), and nanotriangle GERTs (185,215) (Figure 11g, iii). Kim et al. reported the dual-gap nanodumbbell GERTs using Au nanorods as the cores, which exhibited the interior gaps not only between core and shell, but also between two semishells (Figure 11g, iv), showing high plasmonic enhancement for label-free Raman assays. (216) Also, GERTs with multiple shells have been proposed, such as nanospherical multishell GERTs with organic molecules embedded, (192) and nanorod multishell GERTs fabricated via galvanic replacement (Figure 11g, v). (217)
NPs composed of Au and Ag are commonly used active substrates, taking advantage of the robust synthesis route of Au NPs and the excellent plasmonic enhancement of Ag material. For example, the Au–Ag nanorod GERTs can be rationally designed to achieve reduced photothermal effect and improved SERS intensity. (225) The Au–Ag petal-core@smooth-shell GERTs, in which petal-like Au NPs serve as the core, providing more hot spots for Raman reporter molecules, and are coated with a Ag shell, further enhancing the brightness (Figure 11g, vi). (218) When using the resonant fluorophores such as IR-780 and IR-1064 as embedded Raman reporters, this kind of resonant GERTs (also denoted as GERRTs) show the single-particle detection capability. (226)

4.2.3. Optical Properties of GERTs

The optical properties of GERTs can be jointly explained by the classical EM theory and the quantum plasmonic theory. Nordlander et al. proposed the plasmonic hybridization model based on EM theory, (227) where the plasmon resonance can be described as the interaction between a nanosphere and a hollow nanoshell. The plasmonic resonance of concentric nanostructures is considered as linear combinations of the plasmons of each part. In this framework, the electromagnetic field enhancement induced by plasmonic coupling inside the GERT nanogap becomes significant as the gap size decreases. However, another important factor affecting GERT signal would be the electron transportation within the gap. When the gap size is reduced to the subnanometer range, electron tunnelling across the junction may occur, (228) and the embedded molecules act as a conductive bridge between the core and the shell and assist in this charge transfer process, analogous to the short circuit. (211) Lin et al. have shown that the optimal size of GERT internal nanogaps is around 1.2–1.6 nm for the maximum SERS enhancement. (211) These unique optical properties make GERTs a good platform to study the charge transport of molecular nanojunctions and its effect on SERS enhancement.

4.3. Orthogonal Raman Reporter Molecules for SERS Nanotags

The overlap of signals from Raman reporter molecules and from endogenous molecules in complex biological systems is a fundamental obstacle, as it affects the accuracy of analysis. It is encouraging to note that mammalian cells exhibit Raman silencing properties in the range of 1800 to 2800 cm–1, which is considered a “biological Raman transparent window”. Most endogenous molecules in this region do not produce Raman scattering signals. This discovery has led to molecules containing alkyne, (229) azide, (230) and metal carbonyl (231) groups as highly attractive Raman reporter molecules. These types of molecules are referred to as “orthogonal molecules”, as they do not interfere with each other or with living organisms in terms of physical and chemical properties. In fact, many ready-made orthogonal molecules have extremely weak signals in the silent region, and many are not ideal Raman reporter molecules in terms of composition or structures. Therefore, it is critical to systematically design and synthesize orthogonal molecules to make SERS nanotags for biolabeling.
So, what characteristics should the ideal orthogonal molecules in SERS nanotags possess? First, orthogonal molecules should be directly bound to the surface of the SERS substrate, as the chemical enhancement effect requires direct contact between the reporter molecules and the substrate, and the electromagnetic field enhancement effect will sharply increase as the distance between the reporter molecules and the SERS substrate decreases. (232) In addition, it is important to avoid the detachment of reporter molecules during subsequent modification processes, which is crucial for the signal intensity and stability of SERS nanotags. (233,234) Therefore, orthogonal molecules should contain thiol or amino groups, which have strong affinity for noble metal elements. (235,236) Second, the characteristic scattering groups of orthogonal molecules (such as alkynes, nitriles, or metal carbonyls) must be connected to molecules with large Raman scattering cross sections to enhance the scattering intensity in the silencing region. (237) Third, the absorption wavelength of orthogonal molecules should be close to the wavelength of excitation light, leading to the appearance of SERRS, which will increase the signal intensity by 2–3 orders of magnitude. (238) Finally, orthogonal molecules should not have too many characteristic peaks in the silent region to avoid spectral overlap between different SERS characteristic peaks in multichannel detection.
Following these empirical rules, Shen et al. designed and synthesized a series of 4-mercaptobenzene derivatives (OPE molecules) with different substitution groups and constructed a set of orthogonal molecules and corresponding SERS nanotags that can be modulated by alkyne scattering, known as the SERS “palette” (Figure 12a). (239) This demonstrated the design and development of orthogonal-molecular-type reporter molecules for SERS nanotags, significantly improving the accuracy and signal-to-noise ratio of SERS nanotags in both multiplex detection and multitarget imaging. (239) The simultaneous and precise detection of heavy metals such as silver and mercury ions in drinking water, organic polluted water, and pigment rich beverages, (243) as well as the one-step detection of biomolecules such as alkaline phosphatase in serum samples, have also achieved good results. (244) Since the Raman readout of orthogonal molecules is unique and specific, and the “Lighting-up” of SERS depends on the aggregation of NPs, Shen et al. also combined magnetic bead immunization, DNA hybridization, adapter recognition, and spectral encoding to obtain more SERS signal outputs without increasing the number of orthogonal molecules, such as click SERS, (240) mixing SERS, (241) and combined SERS (242) (Figure 12b). These signal readout technologies are widely used in multiplex liquid biopsy, bacterial identification, and precise detection or imaging research.

Figure 12

Figure 12. Design and application of orthogonal Raman molecules. (a) Three-color SERS imaging of HeLa cells using SERS nanotags of an alkyne SERS palette; the SERS nanotags were modified with OPE0 (red), OPE1 (green), and OPE2 (blue). Reproduced from ref (239). (b) (i) “Click” SERS for 10-plex synchronous biomarkers detection. Reproduce from ref (240). (ii) “Mixing” SERS for multiplex detection of liver cancer antigens. Reproduce from ref (241). (iii) “Combined” SERS emissions for high-throughput optical labels on microscale objects, such as bacterium. Reproduce from ref (242).

In the biological Raman transparent window, the Raman scattering of carbon materials has also attracted widespread interest, such as the 2D bond characteristic Raman shift of graphene located at ∼2700 cm–1. Based on this, Chen et al. reported a novel graphene shell isolated Au nanocrystal (GIAN) can be used as a SERS tag and demonstrated that GIAN has good chemical stability even in strong acid solution, oxidation solution, and biological environments. (245) Furthermore, it was found that the synthesized GIAN could be suspended at the liquid–liquid interface to adsorb or attach lipids and water-soluble analytes to SERS detection systems, mainly due to the large surface area and unique π–π electrostatic stacking characteristics of graphene. (36,246)
For either organic small molecule or graphene shells, the adsorption, assembly, or bonding processes on the surface of noble metal NPs are tedious, complex, and of poor reproducibility. Liu et al. keenly discovered that Prussian blue, a coordination polymer, can be easily assembled onto the surface of Au NPs. Its rich CN bonds and resonance enhancement in a wide visible wavelength contribute to the strong Raman signal, demonstrating the performance of Prussian blue based SERRS nanotags in high-sensitivity immunoassays and cancer cell imaging. (247) On this basis, Shen et al. modulated the Raman shift of its CN bond through clever ion exchange reactions. The Prussian blue analog on the surface of Au NPs not only protects the stability of the substrate core, but also contributes to high signal-to-noise ratio and high-intensity multicolor SERS signals. (242)
The volume-active SERS (VASERS) technique provides another way to modify the orthogonal molecules onto the NP surface. VASERS integrates a large number of reporter molecules into a polydopamine (PDA) shell with a thickness of several nanometers around metallic NPs via Michael addition, allowing for their controllable arrangement within the 3D zone of the electromagnetic fields of the NPs. (248) This results in a enhancement of the intrinsic signal of the nanotags, making them about 1 order of magnitude brighter than typical self-assembled-monolayer-based SERS nanotags. VASERS strategy enables the use of arbitrary reporter molecules, regardless of their (strong, weak, or even no) affinity to metallic NPs. (249) That is to say, these orthogonal molecules can be devoid of thiol groups and other similar moieties. This implies that there is no need to obtain orthogonal Raman reporters via costly and time-consuming molecular engineering processes. For example, leveraging the advantages of the VASERS technique, SERS nanotags with 32 resolvable Raman colors using commercially available orthogonal molecules were reported, enabling supermultiplexed imaging of 10-panel combined immune checkpoints with high spectral resolution in clinical biopsies of breast cancer. (249)

4.4. NIR-II SERS Nanotags

Over the past decade, near-infrared II (NIR-II, 1000–1700 nm) SERS nanotags have been paid considerable attentions for in vivo spectroscopic detection and bioimaging. (250−252) The NIR-II excitation light generally yields minimal tissue autofluorescence as well as low optical absorption and scattering, which lead to deeper tissue penetration, higher spatial resolution, and higher detection sensitivity in biological tissues than the visible (400–700 nm) and near-infrared I (NIR-I, 700–900 nm) counterparts. (253,254) Similar to conventional SERS nanotags, the most important two components of NIR-II SERS nanotags are the plasmonic substrates and Raman reporters.
NIR-II plasmonic materials play determinant roles in the performance of NIR-II SERS nanotags through the electromagnetic amplification of Raman signals. (255) The properties of plasmonic substrates are closely correlated with their sizes, compositions, shapes, and interparticle interactions. It has been verified that anisotropic noble-metallic nanostructures offer a broader degree of freedom for tuning the plasmon bands from the visible to NIR regions. (256) The plasmonic substrates with resonant LSPR peak at the NIR-II region are usually preferred. For example, He et al. report in their quantitative study that resonant NIR-II nanorods shown an enhancement factor of 4 orders of magnitude, higher than the nonresonant nanorods under 1064 nm excitation (Figure 13a). (257) So far, a multitude of plasmonic Au nanostructures have been endeavored to redshift the plasmonic properties into the NIR-II region, including Au nanorod, (258) Au nanoshells, (259) and hollow Au nanocages. (260) Despite the great SERS enhancement ability, these NIR-II plasmonic Au nanostructures mostly exhibit large overall sizes with at least one dimension of >200 nm, leading to low in vivo targeting delivery and inefficient cell uptake. Li and co-workers have developed a pore engineering strategy to make NIR-II plasmonic nanostructures. (261,262) Specifically, Au@Au–Ag dot-in-cubic nanoframes were designed with a solid spherical Au NP core and the Au–Ag alloy cubic nanoframe (Figure 13b). Such DCFs exhibited remarkable plasmonic properties and near-field enhancement tunable over a spectral range from 700 to 1400 nm through varying the wall hole size and wall thickness while maintaining the small size less than 100 nm in all dimensions, verified by experimental investigations and theoretical analysis. (261) The NIR-II plasmonic nanoframes were further utilized to make NIR-II SERS nanotags, which were demonstrated with remarkable performance for in vivo NIR-II SERS detection and photoacoustic imaging of tumors. Further, a type of porous cubic-AuAg nanoshells (pc-AuAg NSs) with hollow interiors were fabricated by the galvanic replacement reaction and Au/Ag codeposition on the Ag nanocubes as the sacrificial template, followed by oxidative etching with H2O2 (Figure 13c). (262) It could effectively tune the plasmonic properties of pc-AuAg NSs in the NIR range by varying the pore features such as size and pore number could. The pc-AuAg NSs show superior NIR-II plasmonic properties and exceptional NIR-II SERS amplification ability, which is attributed to the high-density hot spots from the nanopores. These NIR-II pc-AuAg NSs were successfully applied for in vivo noninvasive and highly accurate NIR-II SERS visualization of macro- and microtumors (Figure 13c).

Figure 13

Figure 13. Representative NIR-II SERS nanotags. (a) Quantitative enhancement factor study of NIR-II resonant nanorods. Reproduced from ref (257). Copy 2022, American Chemical Society. (b) Design and optical properties of Au@Au–Ag dot-in-cubic nanoframes. They show high SERS intensity to penetrate thick phantoms. Reproduced from ref (261). Copyright 2021, Wiley. (c) NIR-II SERS nanotags made with pc-AuAg NSs and its application for in vivo SERS imaging of tumors. (i) Schematic illustration of the synthetic process of pc-AuAg NSs. (ii) SEM image of Ag and Au of pc-AuAg NSs. (iii) Schematic illustration of the application of NIR-II SERS nanotags for in vivo imaging of tumors in a 4T1 tumor-bearing mouse. Reproduced from ref (262). Copyright 2022, The Authors. (d) The core–shell nanotags with IR-1064 molecules embedded. (i) Schematic illustration of the synthetic process of the core–shell nanotags. (ii) Absorbance spectra of IR-1064 molecules and core–shell nanotags. (iii) They were applied for sentinel lymph node (SLN) detection. Reproduced from ref (263). Copyright 2024, Elsevier.

As for Raman reporters, large Raman scattering cross sections, weak or negligible fluorescence emission and strong binding affinity toward plasmonic substrates are required. (264) The resonant absorption wavelength of Raman reporters matching the excitation laser wavelength leads to additional enhancement of Raman signals with the SERRS effects. (257,263) Deng et al. reported the IR-1048 embedded core–shell SERS nanotags, which showed high brightness (1 pM limit of detection) at NIR-II window; using these nanotags, they realized the intraoperative SERS-guided sentinel lymph node navigation under a clinically safe laser on the preclinical animal models (Figure 13d). (263) Despite the advances using SERRS dyes, these dyes emit strong fluorescence that may compromises SERS detection; similarly, most available Raman reporters for NIR-II SERS nanotags emit weak NIR-II fluorescence. The invention of nonfluorescent NIR-II Raman reporters with large Raman scattering cross sections are thus critical. For example, Li et al. reported a nickel dithiolene complex as a NIR-II resonance Raman reporter, with intense absorption (absorption coefficient ε = 9.58 × 104 m–1 cm–1) at around 1000 nm, fluorescence-free features, and high affinity to Au NPs. (265) When adsorbed onto Au nanorods, these dyes form bright and stable SERRS nanotags amenable to high-contrast and highly photostable lymph node imaging as well as in vivo tumor detection.
Despite the great advances of NIR-II SERS nanotag-based biomedical applications, future studies should focus on the following aspects: (1) NIR-II plasmonic nanostructures should be explored to further boost the SERS amplification ability for biomedical applications. (2) The NIR-II SERS measurements have been performed using expensive and relatively large Raman systems, which greatly limiting the clinical translation of NIR-II SERS nanotags. (3) The biocompatibility and biosafety of NIR-II SERS nanotags should be investigated in detail and be ensured for practical applications in humans (More discussions can be found in section 9).

4.5. Surface Functionalization via Biomimetic Cell Membranes

The stability of SERS nanotags in aqueous solutions and their long-term colloidal stability in a biological milieu is of unmet importance for in vitro and in vivo applications. This stability of SERS nanotags is primarily dictated by the Raman reporter molecules adsorbed on the plasmonic Au NP surface. Initial efforts in the SERS field employed chemical modulation strategies that conjugated ethylene glycol linkers on the terminus of a representative Raman reporter molecule, mercaptobenzoic acid. (266) This culminated in improving their stability and aqueous solubility owing to the hydrophilic nature of ethylene glycol and further resulted in a reduction of nonspecific binding. Further research showcased that trimethylene glycol units allowed controlled bioconjugation and increased steric accessibility. (267) Stabilizing SERS nanotags in biological fluids is crucial without compromising their signal due to aggregation in high-ionic-strength media and as such, various polymers have been used as protective layers or “coatings” to stabilize SERS-encoded particles and prevent Raman reporter release or nonspecific protein adsorption. PEG is exceptionally versatile due to its biocompatibility, enabling extended blood circulation and allowing controlled polymerization to incorporate specific end groups like thiols for anchoring onto Au NPs. However, in recent years, PEG-specific immune responses have increasingly hindered their in vivo applications. (268) Another approach is encapsulating Au NPs within cross-linked polymers, such as poly(N-isopropylacrylamide) (pNIPAM). The porous pNIPAM coating permits Raman reporter incorporation and facilitates antibody conjugation via outer polyelectrolyte coatings. (269,270) Amphiphilic diblock copolymers like polystyrene-block-poly(acrylic acid), and dodecylamine-modified polyisobutylene-alt-maleic anhydride (PMA) also stabilize SERS nanotags through uniform coatings. (271) Phospholipids provide biologically compatible coatings for Au NPs, enhancing versatility. (272−274) High-molecular-weight proteins like bovine serum albumin (BSA) stabilize encoded Au NPs by preventing aggregation and desorption of Raman-active molecules through electrostatic mechanisms and thiol binding. (275) Similarly, antibodies can be used as stabilizing agents, allowing one-step bioconjugation of the SERS nanotags. (276)
The last step in creating SERS nanotags involves bioconjugation with a tumor-targeting moiety, providing the SERS nanotags with high specificity toward cancer. This bioconjugation process for SERS nanotags uses a functional coating as a stabilizing layer, typically carboxyl groups or primary amines. These groups facilitate AuNP bioconjugation through amide bond formation via carbodiimide activation. Several polymer chemistry modification strategies like Michael addition, click chemistry, or Diels–Alder reaction have been used to provide AuNP functionalization with biological molecules. Sulfhydryl-containing biomolecules can be conjugated to the BSA layer by forming S–S bonds. Bioconjugation of biopolymer-stabilized Au NPs relies on terminal amine and carboxyl groups using EDC/NHS chemistry. For instance, Knudsen et al. stabilized SERS nanotags with BSA and glutaraldehyde, creating a cross-linked organic encapsulation. (277) This process removes most surface amino groups from BSA, resulting in SERS nanotags with a net negative charge from carboxylic acid groups. For silica-encapsulated SERS nanotags, silanes with amino or thiol groups (e.g., APTMS and MPTMS) are used for bioconjugation. (278) Heterobifunctional cross-linkers, such as those with NHS ester and succinimide ends, couple amino- or thiol-functionalized SERS nanotags to target-specific ligands like antibodies. (279) Besides, biotin-modified antibodies can be bound to the streptavidin-labeled nanotags using the strong affinity between biotin and streptavidin.
An innovative approach using functional cell membrane coatings derived from erythrocytes (red blood cells) as a potential coating was reported by Nie and Srivastava et al. on SERS nanotags of various shapes and sizes (Figure 14). (280) These biomimetic SERS nanotags demonstrated superior enhanced stability, SERS signal amplification, and targeted tumor cell recognition. The membrane coating brought long-term colloidal stability for nanotags and robust protection against adverse conditions such as freezing, lyophilization, high buffer concentrations, and heating. Remarkably, the SERS signal stability profile remained consistent across all these conditions. Furthermore, the study revealed that biomimetic red blood cell membrane (RBCm) coating on dye-tagged Au NPs significantly enhanced SERS, culminating in a 5-fold increase in SERS intensity compared to PEG-coated- dye-tagged Au NPs. This enhancement was confirmed independent of hotspot effects, as consistent absorbance and size distribution data indicated. Additional experiments underscored the importance of tight RBCm wrapping for maximum SERS enhancement. Simple mixing of AuNP-dye with RBCm fragments yielded only a 3-fold increase, whereas bath sonication improved membrane packing, achieving 5-fold enhancement. The observed SERS enhancement is attributed to the electromagnetic enhancement mechanism, where transitioning from a dielectric medium from a polar water environment to a nonpolar lipid bilayer significantly reduces electron damping and amplifies the electromagnetic field. RBCm were functionalized with tumor-targeting peptides to target tumor cells and coated onto dye-tagged AuNPs. Initially, for optimization, a lipid-insertion strategy was adopted using a lipid-tethered tumor-targeting ligand (DSPE-PEG-cRGD) was functionalized RBCm which were subsequently coated onto dye-tagged Au NPs. cRGD peptides target integrin receptors, particularly ανβ3 integrins, which are upregulated in specific cancer cells. The targeting efficiency was evaluated using two representative cancer cell lines: MDA-MB231 (high ανβ3 expression) and PANC-1 (low ανβ3 expression). Strong binding and SERS signals were observed in MDA-MB231 cells, while minimal signals were detected in PANC-1 cells, confirming selective targeting ability. Flow cytometry data further validated these findings. This study demonstrates the potential of cell-membrane-coated SERS nanotags with easy tumor-targeting ligand modifications to boost detection sensitivity toward metastatic tumor cells compared to nonmetastatic tumor cells, offering a promising avenue for advanced biomedical applications. Future researches are investigating into the use of tumor cell-derived membrane coatings for imparting homologous tumor targeting of SERS nanotags, potentially obviating the requirement for supplementary targeting peptides.

Figure 14

Figure 14. RBCm coated SERS NPs. (a) Schematic diagram showing preparation of RBCm vesicles and successful coating on anisotropic Au NPs, leading to RBCM-coated AuNP-RBCm with enhanced dispersibility and biofunctionalization properties. Physicochemical characterization of anisotropic RBCM-coated AuNPs. Negative-stained transmission electron micrographs of (b) bare AuNP1, (c) RBCm-coated AuNP1, (e) bare AuNP2, (f) RBCm-coated AuNP2, (h) bare AuNP3, and (i) RBCm-coated AuNP3. The membrane coating in (c, f, i) are highlighted with white lines. (d, g, j) UV–vis absorbance spectra of AuNPs and AuNP-RBCMs, with the inset boxes zoomed. The inset shows the intrinsic plasmonic absorbance peak shift for different anisotropic AuNPs upon RBCM coating. Reproduced from ref (280). Copyright 2022, American Chemical Society.

Srivastava and Nie et al. further advanced cell-membrane coating technology by developing dual-modality plasmonic nanostructures using colloidal Au nanostars. (281) These nanostructures aimed to overcome the limitations of spectroscopic techniques in image-guided surgery, specifically in detecting hidden or buried tumors within healthy tissues. Coated with RBCm, these biomimetic plasmonic nanostructures enhance both SERS and photoacoustic detection of tumor phantoms within animal tissues. Compared to PEG-coated Au nanostars, the biomimetic coatings significantly reduced protein adsorption in human plasma, minimized uptake by murine macrophages, and improved cellular uptake in tumor cells, all while maintaining robust SERS and photoacoustic signals. The biomimetic RBCm coating comprises of abundantly expressed CD47 proteins on the surface, function as “marker of self” receptors. By binding to signal-regulatory protein alpha (SIRPα) receptors on macrophages, they inhibit phagocytosis of RBCs. Consequently, RBCm-coated nanotags leverage these CD47 proteins to evade immune clearance, resulting in prolonged circulation and enhanced tumor-targeted delivery. (268) Based on this mechanism, it is hypothesized that this is the plausible reason why AuNS-RBCm exhibited reduced macrophage uptake in murine macrophages compared to uncoated PEGylated AuNSs, which lack CD47 receptor expression. RAW 264.7 were taken as the in vitro model of reticuloendothelial system clearance for this study. Moreover, since RBCm are derived from the natural cells of mice and are free from synthetic or immunogenic components, they exhibit inherent biocompatibility. This property has been demonstrated not only with Au NPs but also with nanoparticles, including conjugated polymer nanoparticles. (282,283) Furthermore, by incorporating tumor-targeting peptides like cyclic RGD, these NPs can precisely target ανβ3-integrin receptors on metastatic cancer cells, facilitating accurate tracking through SERS and photoacoustic imaging. To simulate in vivo buried tumor lesions, they demonstrated these nanotags’ effectiveness in image-guided resections of tumor-mimicking phantoms, simulating metastatic tumor cells buried under up to 6 mm of skin and fat tissues. Photoacoustic imaging accurately located the tumors, while SERS signals enabled their identification and differentiation. This study underscores the potential of biomimetic dual-modality NPs for intraoperative cancer detection and resection, offering superior signal quality and biological stability.

5. Emerging Instrumentation in Biomedical Applications

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5.1. Optical Tweezers for Single-Particle and Single-Cell SERS

As a convenient approach to suppress Brownian motion and control the position of micro- and nanoscale objects suspended in solution, optical trapping techniques have been integrated with microfluidic systems to improve the efficiency and reproducibility of SERS measurements for various biomedical applications. Optical tweezers, also known as optical traps, utilize highly focused laser beams to manipulate objects, such as cells, microparticles, NPs, molecules, and atoms, with fine temporal and spatial resolution. The basic principle relies on the momentum of light, where the focused laser beam creates a gradient in light intensity that interacts with the object through absorption, reflection, refraction, or scattering, exerting forces to attract it toward the focus. By adjusting the position of the focused laser beam, researchers can move the trapped object without physical contact. This capability enables dynamic and precise positioning of target analytes near SERS-active substrates, thereby facilitating signal enhancement and acquisition.
For microscale object manipulation, McNay et al. designed a partially Ag-coated silica microparticle as an optically trappable and microscopically visible SERS mobile probe, (289) which was refined by Balint et al. for spatial SERS scanning detections of cell membranes. (290) As shown in Figure 15a, Stetciura et al. decorated silica microparticles with metal NPs as SERS-active satellites and guided to specific compartments to monitor intracellular compositions for single-cell analysis. (284) For nanoscale object manipulation, Svedberg et al. assembled two Ag NPs into a dimer to form a dynamic hotspot with an apparent SERS intensity increase. (291) Tong et al. (292) and Messina et al. (285) harnessed optical aggregation of multiple metal NPs, generating numerous interparticle hotspots and boosting the SERS signal of adsorbed analytes, as illustrated in Figure 15b. (285) Coupled with plasmonic heating, Fazio et al. aggregated Au nanorods to detect nonresonant proteins in the μg/mL range. (293) Furthermore, Hong et al. exploited an additional plasmonic trapping effect at a gap within Au nanobowtie structures to gradually deposit the aggregation of Au NPs and enhance the detection sensitivity for Rhodamine 6G to 100 pM, as presented in Figure 15c. (286) These studies showcase the use of spatiotemporal control in hotspot generation and movement for the sensitive SERS detection of analytes directly from native solutions, eliminating the need for additional capping agents such as salts or acids.

Figure 15

Figure 15. Illustration of optical tweezers-coupled SERS platforms. (a) SERS-active satellite transport by laser tweezers to the surface of a cell. (b) Raman tweezing. (c) Autoenhanced Raman spectroscopy and time-dependent Raman signals after laser exposure. (d) Optical tweezers-coupled Raman spectroscopic platform. SERS spectra and intensity as a function of the distance between two Ag NP-coated beads from beads approaching to beads separating. (e) Optical plasmonic tweezer-coupled SERS platform. SERS spectra recorded at a Ag NP-coated bead dimer with the switching of trapping laser between on (red) and off (black) states. Adapted with permission from refs (284−288). Copyrights 2015, Royal Society of Chemistry; 2011, American Chemical Society; 2016, American Chemical Society; 2021, The Author(s); and 2023, The Authors, respectively.

By combining micro- and nanoscale object manipulation, Dai et al. brought two Ag NP-coated silica microbeads together to create a dynamic hotspot, achieving tunable and reproducible SERS enhancements with single-molecule sensitivity. (287) Figure 15d illustrates this controllable hotspot and demonstrates the emergence, enhancement, and disappearance of the SERS signal from 1% ethanol solution as the two beads were approached and then separated. (287) To prevent potential photothermal damage, the distance between the two beads was adjusted in real-time, preserving protein native states and enabling their accurate structural characterizations during SERS measurements. (287) Moreover, precise spatiotemporal control over the hotspot formation with a finite detection volume could reduce ensemble averaging to directly characterize the structures of low-populated alpha-synuclein species among its predominantly unstructured monomers at 1 μM physiological concentration. (287) Recently, Fu et al. constructed a plasmonic junction between two Ag NP-coated silica microbeads to exert additional optical plasmonic trapping to hold a freely diffusing Ag NP and form a SERS-active nanocavity, as depicted in Figure 15e. (288) Since both optical plasmonic trapping and SERS techniques are surface-sensitive relying on the metallic substrate within a nanometer range, this integration could overcome the optical diffraction limit to precisely define the location of hotspot and reduce the SERS active-detection volume for huge SERS enhancements. By toggling the trapping laser on and off, the Ag NP was alternately trapped and released at the plasmonic junction, capturing target analytes in the confined detection volume for efficient and continues high-throughput single-molecule SERS measurements. (288) It could acquire a statistically significant number of molecular SERS snapshots of human islet amyloid polypeptide among a heterogeneous mixture, comparable to molecular dynamics simulations, providing the unique single-molecule level structural details and population of specific species to reveal the mechanism of pH-regulated amyloid aggregation. (288) These studies present an optically controllable strategy for single-molecule SERS detection by simultaneously reducing detection volume, enhancing molecular signal, and improving measurement efficiency, which overcomes ensemble averaging and enables investigation of complex biological systems at the single-molecule level.
Beyond controlling SERS substrates, optical tweezers further facilitate direct manipulation of diverse biological samples. For instance, Lu et al. selectively isolated a single microbial cell from a mixed population (294) and Shang et al. captured individual lactic acid bacteria along with Ag NPs, (295) enhancing the stability of SERS measurements for rapid identification and classification of microbes at a single-cell level. In addition, Kitahama et al. verified that single molecules could be optically trapped via plasmon resonance at the junction of Ag nanoaggregates, yielding blinking single-molecule SERS signatures. (296) Oyamada et al. reported in situ electrochemical SERS detection of individual small size molecules upon plasmonic optical trapping at the gap of a single metal nanodimer immersed in an electrolyte solution. (297) However, a potential limitation of plasmonic optical trapping lies in the heating effect inherent to metallic plasmonic materials upon illumination, which may compromise the stability of trapped biomolecules and affect the outcomes of SERS analysis. For example, Yang et al. observed conformational changes in a single protein at a plasmonic gold nanopore, noting that intense heating could lead to protein denaturation. (298) Besides, this technique faces challenges from additional thermal phenomena, such as thermophoresis and thermo-osmosis, which may further perturb table SERS measurements. As an alternative trapping technique, all-dielectric tweezers, particularly those employing anapole modes, offer reduced heating, greater stability, and enhanced biocompatibility for the trapping of single and multiple objects, which holds promise to improve the SERS signal stability from trapped biological samples. This approach was proposed theoretically by Hernández-Sarria et al. and then validated through experimental studies on vesicle trapping by Hong et al. and Conteduca et al., respectively. (299−302) These advances demonstrate the unique capability to directly manipulate biological samples through optical trapping techniques, introducing new dimensions for their preparative treatment and spectroscopic detection at the smallest scales and deepening the scope of biomedical investigation. Moreover, the dual functionality for substrate and sample manipulations could optimize spatiotemporal resolution and empower explicit control over molecular position, orientation, and interface interactions near SERS-active substrates at nanoscale precision, which would facilitate the characterization of structures and conformational dynamics of complex biological macromolecules. Further developments in the optical tweezers-coupled SERS platform would enhance its analytical potential for controlling, detecting, and elucidating intricate biological processes, which holds the promise to evolve into a versatile tool and inspire innovative applications for biomedical research in the future.

5.2. Plasmonic Nanopore Systems for Single-Molecule SERS Sensing I: Principles and Instrumentation

Single-molecule SERS represents the frontier of SERS sensing, (303) aiming to detect and analyze individual molecules, crucial for studying chemical reactions and complex biological interactions at the single-molecule level. Achieving single-molecule SERS requires creating “hotspots” that allow for the localized enhancement of Raman signals for single molecules, but challenges such as reproducible hotspot creation, precise molecular positioning, and signal differentiation from noise remain significant. Various strategies, including the use of nanogaps, nanocavities, and nanotips, (304−308) have been developed to achieve single-molecule SERS. However, these methods often rely on the random capture of molecules into plasmonic hotspots, limiting their high-throughput capabilities. Nanopore analysis, a technique used to study and characterize individual molecules by detecting changes in ionic current as they translocate through a nanoscale pore in a solid membrane, offers a promising alternative. Plasmonic nanopores integrate the traditional nanopore features with plasmonic effects by utilizing noble metals like Au or Ag to generate “hotspots” with enhanced electromagnetic fields within the nanopore. The external electric field in nanopores facilitates the sequential capture, analysis, and release of single molecules in these hotspots. “ This allows for simultaneous measurement of electric signals and Raman signals from a single molecule (Figure 16a).

Figure 16

Figure 16. Plasmonic nanopores and related applications. (a) Plasmonic nanopore-based single molecule electrical and optical measurement. Reproduced from ref (309). (b) Integrated system for the optical and electrical detection of nanopore analysis. Reproduced from ref (309). (c) In situ electrochemical modification of metallic nanopore. Reproduced from ref (309). (d) Schematic of a solid-state nanopore with an integrated bowtie-antenna structure for DNA translocation experiments. Reproduced from refs (310and311). (e) Schematic of the bowl-shaped plasmonic nanopore system for Raman detection of single DNA molecules. Reproduced from ref (316). (f) Schematic diagram for the dynamic SM-SERS detection by plasmonic nanopipettes. Reproduced from refs (318and319).

The design and implementation of nanopore systems for single-molecule SERS hinge on two key components: the measurement system and plasmonic nanopore fabrication. The measurement system for nanopore-based single-molecule SERS typically includes a laser source for excitation, a Raman spectrometer for signal detection, an amplifier for ionic current acquisition and a control module for the synchronization of electrical and optical signals (Figure 16b). In previous studies, advanced systems for single-molecule analysis have been reported. For example, Long and co-workers developed a unified optical and electrochemical synchronous measurement platform for protein nanopore, solid-state nanopore, and glass capillary nanopore. (309) In addition, an in situ chemical reduction method for constructing plasmonic nanopores was proposed and used to detect the SERS signal of DNA (Figure 16c). Constructing a plasmonic nanopore with a high enhancement factor is a challenging task. A classical plasmonic nanostructure for nanopore sequencing is reported by Belkin et al. in 2015. (310) The work proved theoretically that nanopore confined plasmonic field formed by paired nanoantenna can ensure nanometer resolution to decode the DNA sequence during the translocation through a nanopore by means of SERS. Xin et al. developed a plasmonic antenna with a sub-3 nm nanogap integrated into a SiNx nanopore, which efficiently captured single DNA molecules into plasmonic hotspots for single-molecule analysis (Figure 16d). (311,312) There are many other plasmonic nanopore have been reported. (313−315) Recently, a plasmonic bowl-shaped nanopore was reported by Zhao et al., (316) that capable of direct Raman detection of single λ-DNA molecules. The plasmonic hot spot has a size of less than 3 nm along the direction of translocation, enabling the revelavation of DNA submolecular segments composed of only 7 bases (Figure 16e). Moreover, plasmonic nanopores fabricated by assembling Ag nanotriangle, Au nanoplates, or Au nanolayer at the orifice of the nanopipets were reported (Figure 16f), enabling the detection of DNA bases, protein Segments and oxygenated/deoxygenated states of single hemoglobin during direct translocation by SERS. (317−319)
In general, these studies collectively demonstrate that the potential of plasmonic nanopores in achieving single-molecule SERS. The plasmonic effects give the nanopore the ability to enhance and measure single-molecule Raman signals, while the nanopore architecture facilitates the easy capture of single molecules into “hotspots”. This integration simplifies single-molecule SERS techniques and allows for the simultaneous acquisition of optical and electrical multimodal signals, thus advancing the precise measurement and characterization of single molecules. Despite the remarkable progress and potential of plasmonic nanopores for single-molecule SERS, several challenges and limitations must be addressed to fully realize their capabilities for precise single-molecule analysis. Producing high-quality plasmonic nanopores necessitates precision nanofabrication techniques, limiting their wide application. The enhancement of the Plasmonic nanopore depends on the strength of the local electromagnetic field, which is limited to a small area around the nanopore. As a result, only molecules close to the nanopore can be accurately detected, while those that are farther away have much weaker signals. Thus, developing nanopores with a high enhancement factor and a low-cost is critical. Additionally, optimizing and extending the duration of single-molecule within the hotspots of plasmonic nanopore to obtain high-resolution ionic current signal and spectral signals is also an important factor in enhancing the accuracy of single-molecule measurements.

5.3. Plasmonic Nanopore Systems for Single-Molecule SERS Sensing II: Biomolecule Sequencing

Single-molecule sequencing for detecting the order of the basic units of an individual biomolecule, such as nucleobases in DNA or amino acids in proteins, is playing significant role in all biomedical fields from mechanical study to clinical diagnosis. (320) Single-molecule sequencing is particularly important for proteins due to the fact that proteins cannot be amplified as polymerase chain reaction (PCR) does for DNAs. Because most biomolecules exhibit folded conformations in liquid, they usually need to be unfolded in nanopore sequencing (Figure 17a). For example, single-molecule protein sequencing needs to first unfold the 3D structure of the protein into a primary structure, identify the amino acids in the protein chain and then determine the order of the amino acids. When SERS-active plasmonic nanopores are used in single-molecule sequencing, the molecule would be unfolded by being driven electrically through the nanopore and then detected in the plasmonic hot spot by SERS (Figure 17b). Because single-molecule DNA sequencers have been commercialized, DNA is simpler than proteins in molecule structure and surface charges, development of single-molecule protein sequencing technologies usually use DNAs for benchmarking. However, identification of 20 proteogenic amino acids in proteins is a longstanding challenge in single-molecule protein sequencing by either fluorescence methods or biological nanopore resistive pulse sensors. (321) SERS-active plasmonic nanopores start to play a role in single-molecule sequencing because they can enhance and detect the intrinsic fingerprint Raman spectra of the 4 nucleobases and the 20 amino acids with single-molecule sensitivity. (322−324)

Figure 17

Figure 17. Plasmonic nanopore for biomolecule sequencing. (a) Unfolding a protein for plasmonic nanopore sequencing. (b) The schematic of the plasmonic bowl-shaped nanopore system in which the DNA (green line) would be first uncoiled by the hydrogel (blue circles) and then passed through the hot spot. (c) The cross-sectional distribution of the surface charge of the bowl-shaped nanopore under electric bias. (d) The simulated EO flow distribution in the bowl-shaped nanopore and path of the DNA movement under electric bias (white line). (e) Schematic of the particle-in-pore sensor that could trap single Au NP for tens of seconds. (f) The molecule would be adsorbed on the NP before trapping. (g) The generated gap-mode hot spot with size of 1–3 amino acids. (h) The time series of collected single-molecule SERS spectra of the vasopressin peptide in the electro-plasmonic nanopore. (i, j, k) Single-molecule SERS spectra at different times from (h) that represent the neighboring amino acids. Panels (b–d) are adapted with permission from ref (316). Copyright 2023, American Chemical Society, and panels (e–k) are adapted with permission from ref (283). Copyright 2022, American Chemical Society.

Therefore, single-molecule SERS sequencing needs to detect an individual biomolecule during its movement through the plasmonic nanopores. Even though the plasmonic hot spot is sensitive enough, it remains an issue how to unfold the molecule and locate it in the hotspot in the plasmonic nanopores controllably to collect enough SERS signals for sequence reconstruction. For a successful sequence reconstruction from the single-molecule SERS data, the molecule must always pass through one hot spot in a linear way. Otherwise, if the molecule passes through 2 or more hotspots, single-molecule SERS signals from different segments of the molecules would be mixed and difficult to be analyzed. Accordingly, Zhao et al. used focused ion beam (FIB) to fabricate an Au bowl-shaped nanopore of 20 nm diameter on a silicon nitride (Si3N4) membrane that could generate 2 strong hotspots for SERS sequencing (Figure 17b) in contrast with conventional cylindrical plasmonic nanopores with 4 hotspots. (316) The surface plasmon resonance of the bowl structure could focus the excitation light into the nanopore edge such that the intensity of the hotspot would increase with decreasing pore diameter. Moreover, the Au bowl-shaped nanopore had the floating surface charges (Figure 17c) that generated the electro-osmotic (EO) sheath flow at the pore edge under positive electric bias. As shown in Figure 17d, the electro-osmotic sheath flow together with the electrophoretic (EP) force unfolded the DNA and directed it to the edge of the bowl-shaped nanopore where one of the 2 hotspots was exactly located. Such single-molecule manipulation of the linear λ-DNA movement through the hot spot was confirmed by the collected single-molecule SERS signals of the DNA bases. The extracted sequences based on the single-molecule SERS spectra were agreed with the segments of the λ-DNA sequence in the database. (316)
For SERS sequencing of single-molecule proteins, Zhou et al. fabricated a sub-10 nm conical plasmonic nanopore by electrochemically coating a glass nanopipette with Au layer to investigate the sequence of single cytochrome c protein under electric bias (Figure 17f). (319) When the electric bias was increased to −700 mV, the single-molecule SERS spectra indicated that the cytochrome c was unfolded and passed through the nanopore in a sequential multistep manner. Based on a single-molecule SERS database of different segments of the cytochrome c, the authors were able to reconstruct the segment sequence of the protein. To increase the SERS sequencing resolution, Li et al. used the conical plasmonic nanopore to detect single-molecule peptides with single-amino acid variations by both resistive pulses and SERS. (325) By collecting large amount of single-molecule SERS spectra of the peptides, they developed a statistical data analysis method the extract the single-molecule SERS features of the peptides and identify the single-amino acid substitution. To increase the SERS sequencing throughput, Iarossi et al. combined nanosphere lithography and focused ion beam to fabricate high-density plasmonic nanopore array with 10 nm pore diameters and 100 pores per square micron on a Si3N4 membrane. (326) They demonstrated high capture rate of the DNAs and achieve single-molecule SERS detection of DNA solution of a low concentration of 50 fM.
In the above plasmonic nanopore sequencing, the biomolecules passed through the nanopores so fast that only different segments of the molecules were observed. As an alternative, Huang et al. developed an electro-plasmonic trapping method, Particle-in-Pore sensor, that enabled detection of neighboring amino acids in a single-molecule peptide (Figure 17e, g). (323,327) The peptide was first adsorbed on a Au NP (Figure 17f) that would then be driven into a Au plasmonic nanopore by electric bias. Because the nanopore had similar surface charge as those on the Au NP, the electro-osmotic force and electrophoretic force on the NP counterbalanced each other. Meanwhile, the plasmonic force of the nanopore dragged the NP to the pore wall to generate a strong and localized single gap-mode hotspot with the size of 1–3 amino acids that could detect both aromatic and nonaromatic amino acids (Figure 17g, k). (327) The combined effect of these forces allowed the NP stayed in the nanopore, and also kept the molecule in the hot spot, for tens of seconds (Figure 17h). Such a long time allowed single-molecule SERS observation of the diffusion of a single vasopressin peptide across the hot spot by signals of its neighboring amino acids from Pro (Figure 17k), Arg-Pro (Figure 17j) to Arg-Pro-Cys (Figure 17i). The authors envisioned that the SERS feature of the neighboring amino acids could be used to sequence single-molecule proteins with single- amino acid resolution. While driving the molecule through the hotspot by electric bias controlled the molecule movement direction with less sequencing resolution, molecular diffusion in the hot spot provided higher resolution but could not control the molecule movement. We expect further development of the plasmonic nanopores may include automatic control to provide both high resolution and movement control for single-molecule SERS sequencing.

5.4. Wearable SERS-Based Sensors

Detection and quantification of biochemicals in body fluids are essential for early disease diagnosis, personalized health monitoring, and precision medicine. The analysis of body fluids, such as blood and urine, for clinical diagnosis takes several days from sample collection to central lab testing, which cannot provide real-time information for timely clinical intervention. (328) Wearable sensors show the potential to provide continuous, real-time quantification of molecular targets noninvasively. The peripheral fluids that can be noninvasively accessed by wearable devices include sweat, saliva, and tears. Among these, sweat is attractive due to several advantages, including reliable sample collection methods, continuous accessibility, and workable sample volume. (329,330) Wearable sweat sensors have shown the capability of analyzing various molecular targets, including electrolytes, metabolites, hormones, drugs, and proteins. (330−332) Recently, wearable SERS sensors have been developed to monitor many of these molecular targets by leveraging the advantages of SERS, including high sensitivity, specificity, and multiplex capability. In addition to these common factors of SERS sensor performance, important design considerations of wearable devices include sample collection efficiency, device stretchability, stability, and durability to enable their reliable in vivo performance. Here, we highlight recent SERS-based sweat sensors, important design considerations for wearable devices, and challenges and opportunities to translate these sensors into clinical and point-of-care applications.
Plasmonic nanomaterials in wearable SERS sensors are mainly made of Ag and Au, owing to high SERS enhancement. Ag nanostructures offer higher SERS enhancement than Au counterparts, although Ag has limited chemical and environmental stability, resulting in decreased SERS performance over time. (333,334) Top-down and bottom-up approaches have been utilized to fabricate and synthesize these materials with well-defined dimensions and shapes, including nanosphere, (335) nanorod, (336) nanocube, (337) nanowire, (338) nanofiber, (339) and nanomesh (340) (Figure 18a–h). In comparison, a top-down approach is relatively high-cost, time-consuming, and less efficient at producing large quantities of nanostructures compared to a bottom-up approach. Here we introduce SERS substrates in sweat sensors fabricated with both approaches, their sensing performances for sweat analysis, and flexibility and stretchability for wearable applications below. Table 2 summarizes plasmonic nanostructures, surface functionalization, substrates used for detecting and quantifying various analytes in sweat, the sensitivity and continuous monitoring capability, and the stretchability and flexibility of the SERS substrates and wearable devices.

Figure 18

Figure 18. Wearable SERS-based sensing devices. SEM images of (a) Ag nanomushroom array, (b) Au nanosphere cone array, (c) Au-coated electrospun thermoplastic polyurethane fibers, and (d) Au nanomesh. (e) TEM image of Ag nanocube superlattice. Scale bar: 50 nm. SEM images of (f) plasmonic nanovoids array (scale bars: 1 μm (top) and 200 nm (bottom)), (g) Au nanorods on chromatography paper, and (h) Au nanospheres on alkalized V2C MXene membrane. Inset: optical image of the Au NP-modified MXene membrane. (i) Optical image of a SERS sensor within PDMS microfluidics and (j) corresponding continuous SERS measurements of lactate with varying concentrations. (k) Optical image of SERS sensors distributed along a paper microfluidic channel and (l) continuous SERS measurements of uric acid with varying concentrations. (m) Optical image of a portable Raman spectrometer used in on-body measurements. (n) Optical image of a multiplex sensor array with a sweat extraction system. Scale bar: 1 cm. (a, i, j) Reproduced from ref (341). Open access. (b) Reproduced with permission from ref (335). Copyright ACS. (c) Reproduced with permission from ref (339). Copyright ACS. (d) Reproduced from ref (340). (e, n) Reproduced from ref (337). Open access. (f) Reproduced with permission from ref (342). Copyright Wiley. (g, k, l, m) Reproduced from ref (336). Open access. (h) Reproduced with permission from ref (345). Copyright ACS.

Table 2. List of Wearable SERS Sensors for Sweat Analysisa
Plasmonic materialsMolecular targetsSurface functionalizationSubstrates for plasmonic materialsSensitivityContinuous monitoringSERS substrate and device flexibilitySERS substrate and device stretchability
Au nanosphere cone array (335)AcetaminophenNoSilicon nanocone array0.13–100 μMYesSubstrate-NoSubstrate-No
Device-YesDevice-No
Au/TPU Nanofibers (339)pH4-MBA or 4-MpyElectrospun TPU NanofiberspH 4–8NASubstrate-YesSubstrate-Yes
Device-YesDevice-Yes
Au nanomesh (340)UreaNoPVA fiber nanomesh1–100 mMNASubstrate-YesSubstrate-Yes
Ascorbic acid1–1000 nMDevice-YesDevice-Yes
Au nanorods (336)Uric acidNoChromatography paper1 −100 μMYesSubstrate-YesSubstrate-No
Device-YesDevice-Yes
Au nanosphere (345)Nicotine, methotrexate, nikethamide, 6-acetylmorphineNoAlkaline-treated MXene10 nM–1 mMNASubstrate-YesSubstrate- NA
Device-YesDevice- NA
Ag nanomushroom array (341)UreaNoSilicon nanopillar array0.1–1000 mMYesSubstrate-NoSubstrate-No
LactateNo0.01–100 mMDevice-YesDevice- No
pH4-MBApH 4–8
Ag nanocube superlattice (337)NicotineNoPMMA100 nM–10 μMYesSubstrate-YesSubstrate-No
pH4-MpypH 4.0–6.5Device-YesDevice-Yes
Ag nanosphere array (342)Dopamine3, 3′dithiodipropionic acid di(Nhydroxysuccinimidyl ester)Au/SiO2 coated polystyrene microarray1 pM–1 nMNASubstrate-NoSubstrate-No
H2S4acetamidobenzenesulfonyl azide1 ppb–100 ppmDevice-YesDevice-Yes
Ag nano snow flakes (361)CreatinineNoHydrophobic filter paper12–40 ngNANANA
CortisolNo16–400 pg
Ag nanoparticles (366)UreaNoSulfonated cellulose nanofiber0.1–1000 mM; LLOD: 63.1 μMNASubstrate-YesSubstrate-Yes
Uric acidNo0.005–1 mM; LLOD: 3.98 μMDevice-YesDevice-Yes
pH4-MBApH 4.0–8.0
Ag nanowire (338)2-fluoromethamphetamineNoSilk fibroin protein film50 ng/cm2–2.5 μg/cm2NASubstrate-YesSubstrate-NA
Device-YesDevice- NA
a

NA denotes not available.

The top-down approach for fabricating a wearable SERS substrate involves the physical deposition of Au or Ag nanometer-thin films onto a nanoscale scaffold. Figure 18a shows the Ag nanomushroom array fabricated on a silicon (Si) wafer. (341) The fabrication steps include self-assembly of a monolayer of polystyrene (PS) nanospheres with a diameter of 120 nm, reactive ion etching to form well-aligned Si nanopillars, residual PS removal, and sputter deposition of a thin Ag layer. The densely distributed gaps of less than 10 nm between nanomushrooms provide strong SERS enhancement of analytes. The SERS substrates provided an enhancement factor of 1.5 × 106 using rhodamine6g (R6G) as a model analyte. These SERS substrates were cut into circular pieces with a diameter of 3.9 mm to assemble into a wearable device. The demonstrated capability with the SERS substrates includes the quantification of urea in the concentration range from 0.1 to 1000 mM, lactate from 0.01 to 100 mM, and pH 4 to pH 8. The continuous monitoring of time-varying concentrations of these analytes was also demonstrated and validated with commercial test kits and a pH meter. Although the SERS substrates are rigid, their small size allows the wearable device, composed of PDMS, skin adhesive, and Kapton tape, to still exhibit flexibility. It is worth noting that the chips should be stored under vacuum or immersed in ethanol to minimize Ag oxidation. Following a similar approach, an Au nanosphere cone array was fabricated by depositing Au onto a Si nanocone array (Figure 18b). (335) The nanogaps between the Au nanosphere cones yield high SERS enhancement. The Au SERS substrate enabled the detection and quantification of acetaminophen in the concentration range of 0.5–100 μM with a low detection limit of 0.13 μM. A human subject study showed that the acetaminophen in sweat reached a concentration of 36.8 μM after the drug intake and decreased after ∼90 min. Electrospun thermoplastic polyurethane nanofibers sputter-coated with 30 nm Au film were demonstrated as a flexible and stretchable SERS substrate (Figure 18c). (339) The fiber intersections provide strong SERS enhancement. The SERS intensity collected from the substrates created with polyurethane microfibers was 5 times lower than that with nanofibers, highlighting the importance of nanoscale scaffold. The SERS substrates were modified with 4-mercaptopyridine (4-Mpy) and 4-mercaptobenzoic acid (4-MBA) to quantify pH using buffer solutions (pH 4–8). The peak intensity ratio calculated as I (pH-active peak)/I (reference peak) of 4-Mpy and 4-MBA was used to quantify pH. The pH sensing performance remained stable with 300 cycles of 50% stretch, demonstrating the stretchability of the SERS substrates. The quantification based on the SERS intensity ratio could be an effective strategy to provide stable sensing performance under stretch by eliminating the variations in the absolute SERS intensity induced by nanostructure changes. Another report showed the electrospun poly(vinyl alcohol) nanofibers could be removed after the sputter disposition of 150 nm Au to yield Au nanomesh as a SERS substrate (Figure 18d). (340) The EM hotspots in crescent-shaped goldmesh are primarily localized at the sharp edges of the nanowire. The Au nanomesh provided the SERS enhancement factor of ∼108 and detection of R6G at the low concentration of 10 nM. The SERS intensity remained stable after 1000 crumpling cycles, likely due to negligible changes in the density of EM hotspots after crumpling cycles. The Au nanomesh adhered to a prestretched PDMS substrate and exhibited stable SERS intensity after 1000 stretching cycles with 50% strain. This confirms that laminating unstretchable nanostructures onto a prestretched elastomer substrate could be an effective strategy to provide device stretchability and improve sensing stability. In addition, the detection of urea (1–100 mM), ascorbic acid (1–100 nM), and 3,4-methylenedioxymethamphetamine at low concentrations of 520 nM and 5.2 μM in water was also demonstrated.
The bottom-up approach for fabricating a wearable SERS substrate involves the synthesis of plasmonic nanomaterials with control over size, shape, and material composition through chemical processes, followed by their deposition or self-assembly onto a substrate. Figure 18e shows a layer of ordered Ag nanocube superlattice, self-assembled on a flexible 200 nm thin poly(methyl methacrylate) (PMMA) film to yield a conformal contact of the SERS substrate with the skin. (337) The closely packed Ag nanocube array with a gap size of ∼1 nm was first formed at the water–air interface and then transferred to the PMMA film via the Langmuir–Blodgett deposition. The SERS substrate enabled the detection of crystal violet (CV) at a low concentration of 1 nM with an enhancement factor of ∼107. In a wearable device, a guard ring was placed around the 2 mm SERS substrate to reduce the strain around the silver nanocube array and minimize deformation effects. The SERS intensity remained stable under ∼30% strain and after 1000 stretching cycles. The sweat sensor was calibrated to detect nicotine in artificial sweat at the concentration range from 100 nM to 10 μM. In vivo demonstration showed that the sweat sensor could monitor the drug concentration changes in iontophoresis-induced sweat on a healthy human subject. The SERS substrate was also functionalized with 4-Mpy to quantify sweat pH from pH 4 to pH 6.5. An omnidirectional plasmonic nanovoids array (OPNA) also showed stable SERS performance under various deformations (Figure 18f). (342) The OPNA was prepared by self-assembling a monolayer of Ag nanospheres with an average diameter of 65 nm onto the curved surface of the pockets decorated microscale semisphere retina. The gap between Ag nanospheres is around 5 nm. The pocket surface was coated with ∼50 nm Au and ∼10 nm SiO2 film to further enhance the plasmon coupling and SERS intensity. The resultant strong electromagnetic field allows the OPNA to detect R6G at an ultralow concentration of 10–16 M with an enhancement factor of 108. The EM hotspots inside the rigid pockets remained stable upon stretching, providing consistent SERS intensity after 40% stretch. The consistent SERS intensity under bending with the angle ranging from 0° to 45° could be ascribed to the omnidirectional characteristics of the nanovoids array. The hydrophilic SERS test domain was confined by hydrophobic surroundings for sweat collection and concentration. The OPNA SERS substrate functionalized with 3, 3′-dithiodipropionic acid di(N-hydroxysuccinimidyl ester) enabled the sensitive and specific detection of dopamine at a low concentration of 1 pM. In addition, 4-acetamidobenzenesulfonyl azide-coated OPNA SERS substrate was used to detect and quantify H2S from 1 ppb to 100 ppm. In addition to self-assembly, as-synthesized Au nanorods could be uniformly deposited onto a flexible chromatography paper as a SERS substrate (Figure 18g). (336) The uniform disposition of Au nanorods on the paper results from the combination of weak interactions, including electrostatic interaction and van der Waals forces. (343,344) The SERS substrates provided consistent SERS intensities measured from different regions and different batches. A ratiometric SERS approach was utilized to eliminate the absolute SERS intensity variations associated with the laser power, alignment, and focus in wearable applications. This approach ensures consistency in the quantification of analytes with benchtop and portable Raman spectrometers, obviating the need for recalibration with the portable spectrometer. The plasmonic paper enabled the quantification of uric acid at the concentration range from 1 μM to 100 μM using the SERS intensity of uric acid normalized with that of PDMS encapsulation coating. The ratiometric SERS intensity remained stable at 45 °C high temperature and under 30% strain. Au nanospheres can also be in situ synthesized onto the surface of two-dimensional transition metal carbon/nitrides (MXene) to form a flexible SERS substrate (Figure 18h). (345) Alkaline treatment of TiVC membranes introduced hydroxyl groups on the MXene surface and facilitated the reduction and nucleation of Au NPs. The Au nanosphere size and gap dimension between the nanospheres could be controlled by varying the growth time. The optimized SERS substrate with dense EM hotspots enabled the detection of R6G at a concentration of 10–15 M with SERS enhancement factor of 8 × 1010. The detection of several drug analytes was demonstrated in artificial sweat, including nicotine, methotrexate, nikethamide, and 6-acetylmorphine at the concentration range from 10 nM to 1 mM.
In wearable sweat sensors, microfluidics plays an important role in enabling continuous monitoring of analytes, including sample collection, transport, and storage, while minimizing the mixing of sweat generated at different times. Additionally, a microfluidic module can be employed to quantify sweat volume and secretion rate, which are potentially linked with the analyte concentrations in sweat. Common soft microfluidic devices are made of PDMS, showing the capability of collecting and transporting sweat (Figure 18i). (341) The hydrophobic PDMS surface needs to be treated with plasma to become hydrophilic, improving sweat sampling and transport. The number of inlets can affect the kinetics in refreshing the sensing chamber and temporal resolution in quantifying time-varying analyte concentrations. The authors demonstrated the continuous monitoring of varying concentrations of lactate with a temporal resolution of 8 min (Figure 18j). Paper-based microfluidics was also utilized in wearable SERS sensors, which has several advantages over plastic-based systems (Figure 18k). (336) The chromatography paper can actively capture and transport sweat through capillary action without the need for additional force, for example, the pressure induced by sweat glands. This reduces the flow impedance while the sweat transports across the microfluidic channel, which is important for accurate quantification of sweat volume and secretion rate. In addition, the high surface area of paper substrates allows for high-density immobilization and storage of functional materials and chemical reagents. The serpentine design could provide the device flexibility and stretchability. Two operation modes for continuous monitoring were demonstrated using uric acid as a model target, including continuously scanning the same sensor and scanning several sensors within the same device at the end time. The ratiometric SERS intensity could reach the equilibrium state within 5 min, determining the temporal resolution in these measurements (Figure 18l). The ratiometric SERS approach provided the wearable sensors under mechanical strain and operation temperature with stable analyte quantification. In addition to paper, other porous substrates can also enable highly uniform nanostructure immobilization and easy integration with microfluidic platform. (346−353)
All the components in a wearable device, including SERS sensors, microfluidic module, encapsulation coating, laser blocker, and skin-interfaced adhesive, need to be rationally designed to provide reliable device performance for in situ monitoring of target analytes. Many of these components can draw design inspiration from advances made in the development of soft, wearable electronic devices. (328,354−360) Several challenges and opportunities are present in facilitating the deployment of SERS-based wearable sweat sensors in the real world. Several reports highlighted the potential utilization of portable Raman spectrometers (Figure 18m), (335,341,345,361) which could be further improved for higher sensitivity and spectral resolution, and miniaturized into a wearable form factor. Only a few reports employed surface functionalization to enable sensitive and specific detection of targets (Table 2), which could be explored further to expand the capability of wearable SERS-based sweat sensors. For example, the utilization of stable biorecognition elements, such as molecularly imprinted polymers and aptamers, can improve sensitivity and specificity in quantifying analytes in body fluids. (362−365) Most in vivo demonstrations rely on measurements of sweat generated through physical exercise. The SERS sensors integrated with a sweat induction module would be beneficial for their convenient deployment and continuous monitoring in vivo (Figure 18n). (337) Additionally, the chemical, mechanical, and thermal stabilities of wearable SERS sensors need to be evaluated, considering the harsh conditions in wearable applications.

5.5. Widefield and Holography Raman Instrumentation

The acquisition time in SERS is a critical parameter that influences the sensitivity and reliability of the technique. SERS relies on the enhancement of Raman signals through the interaction of molecules with plasmonic nanostructures, and the acquisition time can significantly affect the quality of the obtained spectra. Short acquisition times are desirable for rapid analysis, especially in dynamic environments, for monitoring fast processes or for monitoring thousands of events, while longer acquisition times can improve signal-to-noise ratios but may lead to sample degradation or photodamage. State of the art Raman systems with highly efficient plasmonic materials can obtain the SERS spectrum of a “good” SERS molecule in 100 ms. However, standard acquisition times for most molecular systems rage between 1 to 10 s, good for many detection and characterization applications but very slow for, for example, biomedical applications in the fields of infectious disease diagnosis or liquid biopsy of cancer, where a large sample of fluids should be extensively screened or for biomedical imaging, where point-by-point (line-by-line with streamline accessories) not only are very slow but usually destroy the sample.
Widefield SERS techniques have emerged as a solution to the limitations posed by traditional point-by-point scanning methods, which often require extensive acquisition times. Widefield SERS imaging allows for the simultaneous collection of spectra from multiple locations across a sample, significantly reducing the overall measurement time. (367) This approach is particularly advantageous for applications requiring high-throughput analysis, such as screening large libraries of NPs or biomolecules. By averaging multiple acquisitions, widefield SERS can enhance the reliability of the results while maintaining a reasonable acquisition time.
The implementation of widefield SERS involves several critical components and methodologies that enhance its effectiveness and applicability. In widefield SERS, the entire sample is illuminated simultaneously, which contrasts with traditional point-scanning methods. This approach not only increases the imaging speed but also enhances the spatial resolution of the Raman signals collected across a large area. (368,369) The experimental setup typically includes a laser source, a microscope equipped with appropriate optics, and a metal oxide semiconductor (MOS) charge-coupled device (CCD) camera as a detector (Figure 19a). The laser is used to excite the SERS-active NPs, while the microscope facilitates the collection of scattered light from the sample. The use of spatial light modulators (SLMs) or digital micromirror devices can further enhance the imaging capabilities by allowing for patterned illumination, which helps reduce photobleaching and improve signal-to-noise ratios. This modulation of the illumination can be crucial for optimizing the imaging conditions and enhancing the quality of the acquired data. In addition to the hardware components, the integration of computational techniques, such as machine learning algorithms, can facilitate the analysis of complex SERS data. These algorithms can assist in identifying spectral features associated with specific molecular signatures, thereby improving the accuracy of diagnostics and enabling the development of personalized medicine approaches. (369) The combination of widefield SERS with advanced data analysis techniques holds great promise for enhancing the diagnostic capabilities of this technology. In practical applications, widefield SERS has been successful to explore large areas very fast and has a lot of potential for tissue diagnosis and anticounterfeiting among other applications (Figure 19b). (367)

Figure 19

Figure 19. Widefield and holography Raman imaging. (a) Schematic widefield microscope extended with “beam homogenization” and “imaging and spectroscopy” modules. The beam homogenization module ensures a flat illumination profile over the entire field of observation to allow quantitative SERS intensity measurements. The imaging and spectroscopy unit simultaneously records an image of the sample plane alongside a spectrally dispersed copy of said image on the same sCMOS camera. This combination ultimately allows extracting single-particle spectra from the camera recordings. Adapted with permission from ref (369). Copyright 2022, John Wiley and Sons. (b) Optical image and single shot SERS imaging of large plasmonic surfaces of 3 mm × 3 mm and the corresponding and SERS spectra taken along the blue lines marked. The bold spectrum represents the mean spectrum. Adapted with permission from ref (368). Copyright 2022, American Chemical Society. (c) Schematic of the spectrally resolved holographic widefield microscope composed of a Michelson and a shearing interferometer enabling simultaneously spectrally resolved imaging and image-phase measurements. The sample is widefield illuminated and the red-shifted SERS signal separated from residual laser light with a dichroic beamsplitter and a long-pass filter. After propagating through a Michelson interferometer, a conjugate image is formed that is relay-imaged onto an sCMOS camera. A 2D 0-π phase grating is placed slightly offset with respect to the conjugate image plane and generates multiple diffraction orders which propagate through the relay imaging system. A hard aperture placed into the Fourier plane isolates the four first diffraction orders which ultimately self-interfere on the camera. The insets highlight the grating-induced change in PSF (left vs right) alongside a schematic of the Fourier filter. (d) Live cell SERS particle tracking. The SERS signal (pink), recorded at a fluence of 1.8 kW/cm2 and an integration time of 250 ms, is superimposed onto a brightfield image, recorded using Koehler illumination with a 470 nm fiber-coupled LED, of the cells. The trajectories of individual SERS particles are color-coded to show the respective z-positions which are obtained by 3D localizing the particles from image stacks generated via numerical propagation. 40 time-points are recorded per minute. (c, d) Adapted with permission from ref (371). Copyright 2020, Springer-Nature.

SERS holography is another innovative technique that addresses the challenges associated with acquisition time in SERS imaging. Again, unlike conventional SERS imaging, which typically involves lengthy point-by-point scans, SERS holography enables the capture of three-dimensional spectral information in a single shot. This method utilizes interference patterns created by coherent light sources to reconstruct the SERS signal across a wide field of view, allowing for rapid imaging of complex samples (Figure 19c). (370) The ability to obtain comprehensive spectral data quickly makes SERS holography particularly valuable for applications in live cell imaging and real-time monitoring of biochemical processes. For example, by using SERS holography it is possible to record the trajectory of one or several plasmonic particles inside a fluid or a cell (Figure 19d). (371) Also, the dynamics of SERS signals can also be influenced by the acquisition time. For example, dynamic SERS strategies have been employed to capture transient events and fluctuations in SERS intensity, providing insights into the interactions between analytes and plasmonic substrates. (372) By optimizing acquisition times, it is possible to enhance the temporal resolution of SERS measurements, enabling the detection of rare events at the single-molecule level.
In short, acquisition time is a pivotal factor in SERS applications, impacting both the quality of the spectral data and the feasibility of real-time monitoring. Advances in widefield SERS and SERS holography offer promising solutions to the limitations of traditional acquisition methods, enabling rapid and reliable analysis of complex samples. As the field continues to evolve, optimizing acquisition times will remain a key focus for enhancing the capabilities of SERS in various applications, including biomedical diagnostics, environmental monitoring, and materials science.

6. Recent Advances in Spectral Analysis

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6.1. SERS Frequency analysis

6.1.1. General Methods for SERS Frequency Analysis

SERS frequency reflects the difference between the excitation laser energy and the energy of photon emitted from the excited molecule, which corresponds to molecular vibrations unique to each (bio)chemical species. (373−379) Collectively, SERS frequencies across a range of wavenumbers form a SERS spectrum that encodes an enormous amount of molecular vibration information. (380,381) SERS spectral changes mainly exist in two form: wavenumber shift and peak intensity change. Wavenumber shift can be further categorized into red-shift and blue-shift, which correspond to a decrease and an increase in vibration frequency and energy, respectively. (382,383) Meanwhile, peak intensity change reflects the vibration polarizability and/or the change in the population of the adsorbate. (375) In biomedical application aspect, such spectral changes indicate the presence of the target analyte by 1) directly observing their intrinsic vibration fingerprint, and 2) indirectly indicating chemical interaction and/or reaction occurring in the presence of the target analyte. Hence, frequency shift analysis plays a vital role in establishing accurate model for qualitative and quantitative (bio)chemical analysis for biomedical applications. Generally, frequency shift analysis consists of (a) spectral preprocessing, (b) peak deconvolution and measurement, and (c) establishing the relationship between the SERS frequency shift and the presence of the target analyte (Figure 20a). (384)

Figure 20

Figure 20. Conventional SERS frequency analysis strategies. (a) Summary of conventional steps for SERS frequency analysis. (b) Schematic illustration of spectral preprocessing strategies. (c) Peak identification and deconvolution to unravel the reaction events at Ag-MP@ZIF by deconvolution of SERS spectra from 1280 to 1400 cm–1. Reprinted and adapted with permission from (c) ref (385). Copyright 2020, American Chemical Society.

6.1.1.1. Spectral Preprocessing
SERS spectral preprocessing step is crucial in removing signals originating from the instrument or the sample that is irrelevant to the presence of the target analytes, ensuring a “clean” spectra for accurate frequency shift analysis. (384) A standard spectral preprocessing includes spike removal, wavenumber and intensity calibration, and baseline correction (Figure 20b). Specifically, the spikes are detected by their abnormal narrow and intense spectral features and can be removed from the spectra by linear interpolation based on the two boundary points of the spike. (386,387) Subsequently, wavenumber and intensity calibration steps ensure spectra are comparable among different Raman systems, which are crucial for biomedical applications because the analytical result is supposed to be identical by different instruments, as long as the measurement conditions such as excitation wavelength and laser power are the same. Conventionally, using a reference/standard material, the wavenumber axis is calibrated by fitting a function between the measured and theoretical positions of the well-defined Raman bands of a wavenumber standard, while the intensity axis is calibrated by dividing the measured Raman intensities by the intensity response function of the instrument. (388) Next, baseline correction serves two main purposes: removing signals stemming from the SERS substrate and removing the fluorescence originating from the sample. Although numerous baseline correction methods, such as sensitive nonlinear iterative peak clipping, asymmetric least-squares (ALS) smoothing, modified polynomial fitting, and multiplicative scattering correction (MSC), have been developed, the choice of baseline correction methods is highly scenario-specific and not universal. (389−395) It is generally accepted that selection of baseline correction method is trial-and-error and the best method is determined based on the result of subsequent analysis. Beyond the above-mentioned preprocessing steps, other processing methods, such as spectra smoothing, and normalization, are optional preprocessing methods (Figure 20b). For instance, smoothing is only useful when spectral noise significantly affects subsequent peak wavenumber and intensity determination, because smoothing can introduce artificial correlations into the noise structure, and subsequently adversely affect spectra analysis and analytical model construction. (396,397) Meanwhile, normalization aims to exclude the effect of laser power fluctuations and changes in the laser focusing by dividing the entire spectra by a number, such as the area under the curve, or highest peak intensity. (398−400) Collectively, a robust spectral preprocessing step is critical in ensuring high-quality spectra for subsequent peak identification and analytical model construction.
6.1.1.2. SERS Peak Deconvolution and Measurement
SERS peak measurement aims to obtain peak features such as peak center, intensity, full-width at half-maximum intensity (fwhm), and peak area, which are crucial for subsequent establishment of calibration curve. Conventionally, this is done by direct measurement on graphing/plotting software. For SERS spectral region containing multiple overlapping peaks, peak deconvolution must be done prior to peak measurement by fitting the region of interest with multiple peaks such that their accumulative signal is approximately equal to the experimental spectra. (401,402) The most common peak deconvolution method is Gaussian fitting, which accounts for effects such as natural broadening, Doppler effect, materials effect, and Raman instrument effects. For example, peak deconvolution assists in deciphering the chemical species present during different reaction stages of carboxylation by identifying the emergence of two sets of irreversible νCOO- peaks (Figure 20c). (385) In addition to Gaussian fitting, there exist other fitting methods such as Lorentzian, Voigtian and Pearson type IV that can account for hybrid peak profile and/or peak asymmetry, yet these methods remain limitedly explored for practical SERS frequency shift analysis. (403−405) Accurate peak feature measurement is crucial for subsequent establishment of relationship between the SERS frequency shift and the presence of target analytes.
6.1.1.3. Standardization and Calibration of SERS Frequency Shift among Samples
Standardization and calibration of SERS frequency shift among samples is conventionally done by finding a mathematical formula to express the concentration of the target analytes from measured peak features such as peak center, intensity, and fwhm. Here, three methods are outlined, namely: (i) external standard calibration, (ii) internal standard calibration, and (iii) standard addition.
i.

External standard calibration. In an external standard calibration, SERS spectra are obtained from a series of standard solution prepared separately from the sample (Figure 21a, i). A plot of SERS peak features response versus known analyte concentrations is used to produce a calibration curve, which allows unknown analyte concentrations to be calculated by interpolation. The most common method to find the formula of the calibration curve is the least-squares method, in which a “best” straight line among the data points is obtained, which minimizes the sum of squares of vertical different from each point to the straight line. For example, SERS peak intensity at 610 cm–1 of R6G demonstrates a linear relationship with log[concentrations] ranging from 10–10 to 10–7 M on a Ag nanocubes-based superhydrophobic platform (Figure 21a, ii). (406) External standard calibration, however, in many cases are prone to errors due to signal interferences from other compounds in the sample matrices, and the instability/mismatch of chemical form of standard in the sample matrices. Thus, using an internal standard is an alternative method to compensate for certain types of error occurring both in the analyte and reference measurement.

ii.

Internal standard calibration. For internal standard calibration, a known amount of reference species is added to sample, standard and blank, from which a calibration curve is constructed to establish the relationship between SERS peak ratio of the analyte/reference signal and the analyte concentration in the standards (Figure 21b, i). (407) This method ensures that both reference and analyte species possess similar chemical and physical properties because they coexist in a sample matrix. One common strategy employing internal standard calibration is the use of SERS tags that have strong signal in the biologically silent region (1800–2800 cm–1) to avoid interfering signals stemming from biomolecules. For example, mercaptobenzonitrile (MBN) was employed as an internal standard to monitor a ratiometric signal between MBN’s 2223 cm–1 and 2-naphthylthiol (NAT)’s 1378 cm–1 for in vitro detection of Cu2+ in Wilson’s disease (Figure 21b, ii). (408) Apart from SERS tag, isotopologues method can also be employed, in which a stable artificial isotope is introduced to facilitate monitoring the concentration of the target analyte. For example, the use of deuterium isotope codeine-d (6) significantly improved the prediction accuracy and root-mean-square error of prediction (RMSEP) in quantifying codeine in both water and human plasma. (409) Such method is especially useful when other internal standards do not yield a linear relationship due to unequal competition between the target analyte and the internal standard on the SERS substrate.

iii.

Standard addition. Standard addition is an alternative method to construct the calibration curve when it is difficult or impossible to duplicate the sample matrix. (410,411) Specifically, SERS peak features are monitored as the test sample is spiked with a known amount of standard solution of the analyte (Figure 21c, i). The x-intercept of the calibration curve corresponds to the negative of the analyte’s concentration in the original sample. For example, standard addition was employed to construct a calibration curve for quantification of an antibiotic nitroxoline at micromolar concentration range in urine (Figure 21c, ii). (412) It is of note that standard addition method can also be used to validate the calibration curve and the quantification result by calculating the recovery rate (%) from the ratio between the difference between observed and measured concentration and the spiked concentration. Collectively, external standard calibration, internal standard calibration, and standard addition demonstrate traditionally manual SERS frequency shift analysis for analyte concentration determination.

Figure 21

Figure 21. Standardization and calibration of SERS frequency shift among samples. (a) (i) Scheme illustration of external standard calibration method and (ii) calibration for R6G ranging from 10–7 to 10–16 M using 610 cm–1 intensity. (b) (i) Scheme illustration of internal standard calibration method and (ii) calibration for Cu2+ quantification showing SERS spectra and ratio (I2223/I1378) as Cu2+ concentration ranging from 0 to 18 mM. (c) (i) Scheme illustration of standard addition method and calibration of nitroxoline in urine and SERS spectra upon spiking different concentration of nitroxoline. Reprinted and adapted with permission from (a) ref (406), (b) ref (408), and (b) ref (412). Copyrights 2013, 2016, 2021, American Chemical Society.

6.1.2. Machine Learning for Frequency Shift analysis

In biomedical applications, traditional manual SERS frequency shift analysis often has limited performance due to 1) the convoluted analyte signals arising from numerous interfering species and 2) a large amount of time is required to process large volumes of data. (413) Recently, machine learning (ML) algorithms–smart programs based on logic and mathematics - are increasingly employed for frequency shift analysis to identify minute convoluted spectral changes, recognize patterns, and handle large data set. (414) Common machine learning algorithms employed for frequency analysis can be classified into two groups: unsupervised and supervised (Table 3). (415−417) Unsupervised machine learning involves training algorithms on spectra without labeled responses, aiming to identify hidden patterns or intrinsic structures, such as clustering similar spectral profiles. (418,419) Supervised machine learning trains algorithms on labeled data, where the model learns to predict the output from input data by using known input-output pairs, such as identifying biomarkers/disease states from spectral features. (420−422) Selecting the right machine learning algorithm is critical to construct an accurate and robust model for SERS frequency shift analysis.
Table 3. Common Machine Learning Algorithms and Their Signature, Advantages, Disadvantages, and Usual Sample Size (Per Class)
6.1.2.1. Unsupervised Machine Learning
Unsupervised machine learning algorithms are particularly beneficial for SERS frequency shift analysis due to their simplicity yet effective pattern recognition, data exploratory and data visualization. Three commonly used unsupervised machine learning algorithms will be discussed: principal components analysis (PCA), uniform manifold approximation and projection (UMAP), and hierarchical clustering analysis (HCA) (Table 3). (423,428,446−449)
The most commonly used unsupervised algorithm is PCA, which reduce data dimensionality by capturing maximum variance among variables and transforming it into principal components containing useful data. (423,425,427) As the data dimensionality reduces, a 2D or 3D PCA score plot is often employed to visualize data clustering and determine whether the spectra differences among clusters are statistically significant. In one study, a 3D PCA was employed to visualize the clear distinction among spectra of fentanyl (FE), morphine (HER), ketamine (KET), and ternary mixture samples, confirming the capability of SERS platform for drug detection and differentiation (Figure 22a). (424) Besides the data clustering and visualization, PCA is also well-known for its conjunctive use with a supervised algorithm to avoid overfitting due to the curse of dimensionality and to accelerate the model construction process, which is especially beneficial for SERS data due to its inherently huge amount of variables (each wavenumber is considered a variable). For example, PCA was employed prior to support vector machine (SVM) to reduce the data dimensionality of a SERS double-fingerprint (∼2800 variables) to only 5 PCs that captures only the most important spectral features for subsequent construction of model for simultaneous quantification of SO2 and NO2 in the presence of excessive CO2 as interference. (426) It is of note that PCA can only capture linear correlation and thus has limited flexibility and might not be able to maintain both local structure (relationship and patterns among nearby/similar spectra) as well as global structure (overall arrangement and relationships among all spectra in the data set).

Figure 22

Figure 22. Unsupervised machine learning for SERS frequency shift analysis. (a) 3D PCA score plot of the ternary mixture (FE: 0.1 μg/mL, HER: 10 μg/mL, KET: 10 μg/mL) and its single components and the corresponding LLI SERS spectra. (b) UMAP plot showing an overlaid density contour plot and pixels reordered in correct spatial correlation and colored by clusters identified in UMAP. (c) Hierarchical clustering analysis of measured MPY SERS, which categorizes the bacteria species on three main levels using ECM surface chemotype-based classification factors, namely, surface charge, overall ECM chemical characteristics, and type and number of interacting functional groups. Reprinted and adapted with permission from (a) ref (424), (b) ref (429), and (c) ref (432). Copyright 2023, American Chemical Society.

UMAP is an alternative powerful dimensionality reduction technique that excels in capturing complex, nonlinear relationship and preserve both local and global structures. (428−430) In the aspect of SERS frequency shift analysis, UMAP can cluster pixels on a Raman map to provide information on sample heterogeneity. For example, UMAP was employed to understand the spatial distribution and composition of complex biological samples, such as cardiac tissue, using a Raman map (Figure 22b). Specifically, spectra from individual pixel on a Raman map were distinctively segregated into 8 clusters with intracluster spectra being similar to each other. Subsequently, spectra from each cluster were analyzed to identify the typical area with higher intensity and associate the cluster’s origin with the type of tissue, thus enabling spatial mapping and visualization of cardiac structures and compartments. (429) Such spatial mapping of specific peaks enables locating myocardium, collagen, and blood cells, demonstrating UMAP’s effectiveness in identifying and spatially characterizing tissue components. Despite being a powerful clustering algorithm, it should be noted that the high flexibility and complexity of UMAP might cause overfitting issue and make the result harder to be interpreted. Hence, nonlinear UMAP algorithm requires much larger data set (50 000–5 000 000 spectra per class) comparing to linear algorithms such as PCA and HCA (10–100 spectra per class).
To explicitly illustrate the relationship among spectra, HCA is often employed because it provides a dendrogram that indicates the distance or dissimilarity between clusters of spectra, facilitating the interpretation of factors affecting the clustering. (431−433) In addition, HCA can also work directly with the full spectra, preserving all features without the need for dimensionality reduction, which is beneficial for biomedical applications that require the use of all spectral information. In a study using SERS for bacteria differentiation, HCA separated 600 spectra from 6 bacteria species into distinct clusters, revealing a three-level separation based on 1) surface charge, 2) overall biochemical characteristics, and 3) type and number of functional groups (Figure 22c). (64) These examples collectively demonstrate the ability of unsupervised machine learning algorithms for pattern recognition, data clustering and visualization. Despite these benefits, unsupervised model can be incompetent for complex tasks when there are multiple sources of interference such as interfering substances, background noise, and data collection conditions and due to the lack of labeled data for guidance.
6.1.2.2. Supervised Machine Learning
Supervised machine learning algorithms can significantly improve SERS analysis accuracy using labeled data sets, such as known analyte’s molecular structure, concentration, and sample’s disease status. To date, various ML algorithms have been developed, ranging from simple and low flexibility such as partial least-squares (PLS) to more complex and high flexibility such as support vector machine (SVM), tree-based algorithms, and neural network-based algorithms (NN) (Table 3). (382,396,420,422,450−453) In general, simple algorithm performs better when there is a limited number of sample and/or the relationship between SERS frequency shift and analyte concentration is linear, whereas complex algorithm is more suitable for a larger data set and/or nonlinear spectra-analyte concentration relationship. It is of note that all these algorithms are applicable for both qualitative (e.g., classification of disease diagnosis) and quantitative (e.g., quantification of biomarkers) spectral analysis.
PLS is one of the simplest machine learning algorithms. It aims to find a set of latent factors which best account for the maximum multidimensional covariance between the input SERS spectra and the label, followed by constructing regression/classification models. (382,434−436) Notably, the ability to handle highly collinear and noisy data makes PLS a powerful tool for SERS spectra analysis. For instance, PLS regression was employed to construct calibration curve for quantitative analysis of an enantiomers mixture (d- and l-tryptophan) using nanoporous Au NPs (Figure 23a). (434) The constructed calibration demonstrates an excellent fit, with a high cross-validation value R2 of 0.96, and the ability to accurately quantify mixture of unknown component at absolute error of ∼1%. In another study, PLS discriminant analysis (PLS-DA) was employed to analyze 501 breath samples and construct COVID-19 screening model using the SERS superprofile measured on an array of receptors, achieving 96.2% sensitivity and 99.9% specificity. (436) These examples thus demonstrate the PLS algorithm capability to construct accurate qualitative and quantitative models from multivariate data sets.

Figure 23

Figure 23. Supervised machine learning for SERS frequency shift analysis. (a) PLS regression plot of measured against actual mol % L-TRP. The calibration curve is constructed from spectra collected in varying mol % L-TRP mixtures at 10 mM total [TRP] and comparison of measured vs actual mol % L-TRP in different test samples using the NPGB/EC-SERS platform. (b) Support vector machine classification for differentiation between SERS spectra collected from healthy control and prenatal disease. (c) Confusion matrix of averaged classification outcomes across 100 model iterations using a random forest (RF) classifier. The F1 scores are indicated in brackets. Reprinted and adapted with permission from (a) ref (434), (b) ref (437), and (c) ref (432). Copyrights 2018, 2021, 2023, American Chemical Society.

To construct nonlinear or more complex relationships, machine learning algorithms with higher flexibility is often preferred, such as SVM, tree-based, and neural network-based algorithms. Support vector machine (SVM) is well-known for its capability to handle small data set (as small as a few tens of spectra) with nonlinear relationships, by projecting data onto a higher dimension plane and find the optimal boundary to separates labeled groups. (425,454) A critical example showcasing SVM superiority is for prenatal diseases diagnosis using a paper-based SERS, achieving >93% accuracy with a training data set of 40 samples with 40 spectra for each sample (Figure 23b). (437) Another frequently used algorithm is tree-based algorithms, such as random forest and gradient boosting, work by constructing multiple decision trees and aggregating their outputs for improved accuracy and reduced overfitting. (438−442) In one study, random forest were used to construct model for differentiating among 6 bacteria species by SERS spectra changes induced by the interaction between the extracellular matrices and 4-mercaptopyridine (MPY)-functionalized Ag nanocubes, achieving >98% classification accuracy (Figure 23c). (432) Lastly, neural network-based (NN-based) algorithms offer highest flexibility to model highly complex data and capture intricate patterns by constructing multiple interconnected layers of nodes that process input spectra through weighted connections. (374,443,444) For example, a convolutional neural network model was constructed for simultaneous detection of 6 types early stage cancers by analyzing SERS profiles of exosomes at >95% accuracy. (445) Despite offering great potential for SERS spectra frequency analysis and model construction, it is noteworthy that overfitting due to insufficient number of spectra is a persistent issue for supervised algorithm, especially for tree- and neural network-based algorithms which requires hundreds to tens of thousands spectra. In addition, overfitting also stems from inadequate model evaluation/validation with untrained sample. Hence, extra precautions need to be taken at every step from sampling and data collection, model building, and model evaluation to ensure a robust machine learning model for SERS frequency shift analysis.
6.1.2.3. Enhancing Interpretability of Machine Learning Analysis
Although the advances in machine learning algorithm enable accurate SERS frequency analysis and achieve unprecedented analytical performance, models constructed using machine learning algorithm are usually referred to as a “black box” with highly complicated mathematics calculations and limited interpretability. This is thus raising a concern on reliability of the models, whether the analyses are based on (bio)chemical recognition event and limit the (bio)chemical insight conventionally obtained from manual analysis of SERS fingerprint. Here, three main approaches with increasing transparency to enhance the interpretability of machine learning-SERS frequency shift analysis will be discussed: (i) feature importance ranking, (ii) domain knowledge-driven machine learning, and (iii) chemical simulation.
i.

Feature importance ranking. Feature importance ranking algorithms enhance the interpretability of models by identifying the specific wavenumbers or spectral regions that significantly impact the model’s prediction, ensuring that the models are based on meaningful spectral features rather than random noise (Figure 24a, i). The most commonly used algorithms for feature importance ranking is Shapley additive explanations (SHAP), which calculates the effect of each variables on the output values to evaluate their importance. (452,455,456) For instance, SHAP was employed to calculate the feature importance of a convolutional neural network model for classification of samples with high (10–1 to 10–3 M), medium (10–3 to 10–5 M), and low concentration (10–6 to 10–8 M) of 2,3-dihydroxybenzoic acid (2,3-DHBA), a typical bacterial biomarkers and metabolites (Figure 24a, ii). (457) Specifically, SHAP identifies wavenumber of high contributions at 438, 1236, 1442, 1572 cm–1, which are close to SERS peaks of 2,3-DHBA, thus indicating that the spectral classification is reliable because it relies on the spectral features of the biomarker and not random noise. In addition to SHAP, there are other feature importance ranking models such as local interpretable model-agnostic explanations (LIME) and contrastive explanations method (CEM), yet their application for Raman and SERS spectral analysis remains unexplored. (458,459) The aforementioned algorithms for feature importance analysis are especially useful for complex algorithms, thus we recommend studies employing machine learning for frequency analysis to employ these methods more frequently to ensure model reliability, interpretability, and accelerate translation of machine learning-SERS sensor into practice.

ii.

Domain knowledge-driven machine learning. There is an increasing demand for models with an inherent capability to output feature importance concurrently with the target properties (Figure 24b, i). This capability provides a direct correlation between SERS peak features and the underlying chemistry of analysis. Understanding which spectral features are most influential aids in interpreting the chemical composition and interactions in the sample. Such models enhance interpretability and are highly valuable in applications like biomedical diagnostics and chemical analysis. A simple algorithm with inherent capability to output feature importance is PCA, which generates loading plots that capture the contribution of each wavenumber to the respective PCs. (425,432) The loading plots have been employed in many studies, for example in wine flavor classification to unravel the spectral changes induced by different types of interactions such as pi-pi stacking, dipole–dipole interaction, and H-bonds (Figure 24b, ii). (425) Another commonly algorithm capable of outputting feature importance is symbolic regression, such as sure independence screening and sparsifying operator (SISSO), which identifies the important features and finds the best mathematical expressions between these variables and the target properties. (441,460−462) For example, SISSO was employed to establish an interpretable relationship between Raman signals and pure metal–adsorbate adsorption energy, highlighting the strong influence of a specific νCO vibration mode for the prediction of adsorption energy. (460) Importantly, the highly interpretable mathematical formulas established by SISSO enable transferring to a series of metal/alloy surfaces. Collectively, the above examples of algorithms highlight the potential of “transparent box” model to not only yield accurate SERS frequency analysis, but also provide significant chemical insights and enable transfer learning and model extension to similar spectral analysis tasks.

iii.

Chemical simulations. Beside feature importance ranking and domain knowledge-driven machine learning, chemical simulation is a powerful tool to unravel the mechanism behind observed spectral changes and ML-based decisions, such as types of interaction involved, analyte configuration and orientation, and chemoselectivity. The most commonly used simulation method is density functional theory (DFT), which provides theoretical molecular vibration and SERS spectra with high accuracy for the validation of spectral features corresponding to the recognition event. For example, DFT simulation was employed to corroborate with the spectral changes identified on experimental SERS spectra and PCA loading plot to confirm the ring complexation phenomenon (Figure 24c). (426) In the same study, to the authors calculate and compare the interaction energy of different receptor-analyte binding configuration to explain for the accelerated spectral stabilization. DFT simulations can also give insight into analyte adsorption orientation, which significantly influence SERS spectra. In particular, DFT-simulated spectra were employed in conjunction with SERS to unravel the orientation of N-heterocyclic carbenes (NHC) adsorbed on noble-metal surfaces. (463) Besides DFT, molecular dynamics simulation also capable of elucidating SERS substrate-analyte interactions to support the feature importance ranking algorithm. For example, molecular dynamics simulations were employed to mimic the interactions between different bacteria species’ extracellular matrix (ECM) with a Raman molecule (4-mercaptopyridine) on the Au surface, thus rationale the redshifts identified by SERS spectra observation and HCA model. (432) Collectively, DFT and molecular dynamics thus exemplify the great potential of simulation to unravel the recognition mechanism, complement with ML algorithm to achieve excellent model interpretability and thus affirm accurate and robust ML-driven SERS frequency analysis.

Figure 24

Figure 24. Enhancing machine learning model interpretability. (a) Feature importance scores extracted from SHAP analysis for classification among SERS spectra of with high, medium, and low concentration of biomarker 2,3-DHBA. (b) Correlation between PCA score plot and PCA loading plot unravel the spectral changes induced by different types of interactions from different probe molecules. (c) Density function theory (DFT) simulation reveal ring complexation between APDS and SO2 to confirm the identified SERS spectral changes. Reprinted and adapted with permission from (a) ref (457), (b) ref (425), and (c) ref (426). Copyright 2021, American Chemical Society. Copyright 2024, Elsevier. Copyright 2022, Wiley.

The translation from manual to machine learning-driven SERS frequency shift analysis unlock SERS’s great potential by effectively utilize the entire spectra instead of focusing onto one/few specific peaks. Machine learning-driven analysis also enables large-scale data processing, which is crucial for biomedical application that requires a large number of samples and spectra to represent the population and account for the demographic variation such as age, gender, dietary habit, and medical history. However, one significant problem hampering the translation of ML-driven SERS into practical biomedical application is the lack of large, representative (typically tens of thousands to millions of spectra or samples) high-quality labeled data sets, which is crucial for training a robust machine learning model. In addition, differences in instruments and experimental conditions lead to the formation of “local” ML model that are optimized for specific setups with limited generalizability. Such models often perform well only within the context of their training conditions, which can adversely affect the accuracy when applied to data from different instruments or experimental setups. This can hinder reproducibility and collaboration, as results may not be consistent across different research groups or organizations. Hence, to accelerate the practical application of machine learning in SERS analysis, we envisage a need to develop a centralized SERS spectra libraries capable of unifying SERS spectra collected among different research groups. Such data unification, together with advances in instrumentation for platform fabrication and spectra acquisition, will fully realize the potential of ML in empowering SERS technology for biomedical applications.

6.2. SERS Denoising: Spectral and System Noises

Noise interference is hardly avoidable in Raman measurement, even with the introduction of advanced noise-reducing optoelectronic devices such as high-cooling CCD detectors and integrating spheres. (464) For instance, under the condition of either a pushed-to-the-limited acquisition time or a concentration at the limit of detection, the reduced signal intensity is normally accompanied by the increased noise, resulting in a reduced signal-to-noise ratio (SNR). (465) Accordingly, both the qualitative sensitivity and quantitative accuracy of Raman spectroscopy and the clarity of Raman imaging would be diminished. In recent years, advances in computer science have opened a new door with modern denoising algorithms that allow accurate identification and extraction of weak signals under low SNR conditions. (466−469) In this section, the relevant denoising algorithms are discussed from the aspect of the spectral data format, i.e., Raman spectrum (1D) and Raman imaging (3D).

6.2.1. Raman Spectrum (1D)

Both thermal noise and shot noise, the two most common types of noise in Raman measurement, are categorized as Gaussian white noise. This type of noise typically has a uniform power spectral density and follows a zero-centered Gaussian probability density function. (464) Consequently, denoising is either based on a frequency domain or a spatial domain. Frequency domain based denoising, such as Fourier transform filtering and wavelet denoising, capitalizes on the frequency disparities between signal and noise. (476) However, it is difficult to set a right threshold to exactly separate noise from the Raman signal, in which both sharp high-frequency and broad low-frequency peaks are involved. Spatial domain denoising, such as mean filtering and Savitzky-Golay (S-G) filtering, suppresses noise by processing each data point with the consideration of its neighbors. (477) Such averaging operation inevitably distorts peak information, resulting in a less fidelity of the denoised spectrum than that of the actual one. Recently, as shown in Figure 25a, Liu et al. developed a Peak Extraction and Retention (PEER) algorithm, which effectively addresses the loss of feature information in spatial domain denoising. With the integration of the peak detection algorithm and spatial domain denoising one, PEER maximizes the extraction and retention of peak information by adaptive denoising of segmented peaks with different SNR, ensuring high fidelity and high performance of the denoised Raman spectrum. (470)

Figure 25

Figure 25. Development of denoising strategies in Raman spectrum (1D) and Raman imaging (3D), from conventional signal processing algorithms to supervised and self-supervised deep learning ones. For spectral denoising: (a) PEER, a signal processing algorithm integrating peak extraction and retention, (470) (b) a supervised algorithm based on instrumental noise learning, (471) and (c) P2P, a zero-shot self-supervised spectral denoising algorithm. (472) For Raman imaging denoising: (d) a signal processing algorithm integrating Fourier transform denoising with imaging moving average filter, (473) (e) a two-stage supervised algorithm integrating both spectral and imaging processing, (474) and (f) S2S, a zero-shot self-supervised hyperspectral imaging denoising algorithm. (475)

The above denoising algorithms are derived from traditional chemometrics, which involves various parameters to be manually corrected. Differently, deep learning, highlighted by its exceptional ability to learn and represent data features, can adaptively extract signal characteristics from the noise, thereby effectively suppressing the noise. (478,479) By training a residual U-net based model on over 1.5 million experimental spectra with varying SNR, Bergholt et al. effectively realized denoising and reconstruction of low SNR Raman spectra. (480) Further, to reduce the reliance on massive experimental spectra, Shum et al. trained a generative adversarial network (GAN) on a data set containing tens of hundreds of experimental spectra, enabling construction of an extensive data set comprising tens of thousands of simulated spectra. The subsequently developed spectral preprocessing model (RSPSSL) demonstrated consistent and reliable denoising performance. (481) Note that data-driven deep learning models may suffer from limited generalizability. Specifically, the denoising is susceptible to potential serious distortion, once the application scenario exceeds the training domain of the model. To address this issue, Ren et al. developed an attention U-net (noise learning, NL) model to identify and remove instrumental noise, which is tailored to the Raman spectra from a single Raman instrument (Figure 25b). After being trained on over 100,000 instrumental noise spectra, the model notably enhanced the SNR of the output Raman spectrum from the specific Raman spectrometer, effectively transitioning deep learning from a sample-dependent approach to an instrument-dependent one. (471)
To overcome the massive data requirement of deep learning, as shown in Figure 25c, Liu et al. developed a self-supervised spectral denoising algorithm (Peak2Peak, P2P). (482) The P2P algorithm is derived from Noise2Noise (N2N), the most representative self-supervised 2D image denoising algorithm. N2N achieves self-supervised denoising by analyzing and extracting effective information from a large number of noisy images, which have the same subject but randomly distributed noise. (472) Alternatively, by combining data augmentation and a regularization function, P2P induces the deep learning model based on a simple convolutional neural network (CNN) to capture the spectral features under noise interference. The feature extraction capability of P2P is derived from self-supervised feature learning through data augmentation, while the denoising capability of P2P is derived from the combination of the loss function and regularization. Operating on a single noisy spectrum, P2P achieves exceptional spectral denoising with high performance and fidelity, significantly enhancing the efficiency of data mining and extending the scope of deep learning applications.

6.2.2. Raman Imaging (3D)

The aforementioned denoising algorithms can also be applied for Raman imaging, regarding the noise presented in Raman imaging is primarily derived from the spectral acquisition process. For example, Shum et al. used the RSPSSL model to improve the SNR of Raman imaging of cancer tissue, thereby effectively reducing the acquisition time by almost ten times. (481) Ren et al. significantly improved the spatial resolution of TERS imaging with the aid of NL model. (471)
However, spectral denoising algorithms neglected the image feature of hyperspectral (3D) data, resulting in inadequate data exploitation. Therefore, both spectral (1D) and image (2D) denoising algorithms are combined to improve denoising performance. For example, Liu et al. sequentially used the Fast Fourier Transform (FFT) filtering and the moving average filtering for spectral and image denoising, respectively (Figure 25d). The overall denoising significantly improved the clarity of image boundaries and detailed features in Raman imaging. (473) The similar performance was also demonstrated by Shum et al. using deep learning algorithm, in which 1D and 2D residual U-net convolutional neural networks are for denoising spectrum and images, respectively (Figure 25e). (474)
Further, aiming at self-supervised denoising, as shown in Figure 25f, Liu et al. developed a hyperspectral denoising algorithm (Signal2Signal, S2S) derived from P2P. By integrating hyperspectral undersampling and U-net, S2S could improve the clarity and spatial resolution by more than three times, even for a Raman image with a spectral SNR as low as 1.12. The robust detection of target signals and the wide-ranging applicability of S2S not only enhance the temporal resolution of Raman imaging, but also show the potential for real-time denoising during Raman imaging process. (475)
In summary, deep learning significantly bolsters the capability of denoising algorithms in identifying and extracting weak signals. Furthermore, self-supervised deep learning adeptly mitigates the dependence on massive labeled data. This not only substantially reduces the technical barriers to deploying these algorithms but also enhances their versatility and practical applicability.

6.3. SERS Spectral Unmixing: From Chemometrics to Deep Learning

Unmixing various molecular components (endmembers) with the underlying fractions (abundances) in biological samples from SERS spectra is an emerging topic and is increasingly attractive in biomedical applications. Generally, inversely retrieving the SERS spectra of pure substances is challenging. (483−485) In addition to comprehensive understanding of the spectral signals themselves, taking into account the underlying characteristics of biological samples and experimental methods used also benefits spectral unmixing with higher accuracy. For the case of lacking detailed information about the biological samples, methods such as N-FINDR and Multivariate Curve Resolution (MCR) can be employed to directly extract potential components and endmembers from the scene. (486,487) However, such direct decomposition approaches are intrinsically less reliable especially for biomedical applications, which require further experimental evidence to validate the sources of the decomposed spectra. With some contributing components of the spectra available, spectral analysis becomes less complex and more convincing. In other words, full knowledge of the analytes would yield promising unmixing results. Sometimes not all molecular components within samples can be detected by the SERS signal, which is attributed to plasmonic surface science. (488−490) It suggests that a deeper understanding of the interaction between molecules and the substrate helps narrow down the range of potential molecules. In this case, less is more─narrowing the range of signal sources can aid in a deeper analysis of molecular quantity information. Most unmixing algorithms approximated the mixed spectra as a linear combination of individual components. (491,492) When pure substance spectra are available, methods such as non-negative least-squares (NNLS) can be used to decompose the mixed spectra and the corresponding coefficients serve as the basis for relative quantification. (493,494) However, the real situation cannot be limited to linear relationships, which has attracted increasing concerns. (495,496) Considering the interactions between molecules, as well as those between molecules and the substrate in complex systems, more complicated models need to be established. (497−499) Furthermore, molecular spectra may vary under different measurement conditions, and additional factors may reduce the accuracy of unmixing results. (500,501)
Unlike traditional chemometrics, which often relies on linear methods, deep learning enables nonlinear spectral unmixing by extracting high-level features from spectral data, followed by the estimation of endmembers and their abundances. The main network architecture employed for this purpose is the autoencoder composed of an encoder and a decoder. Typically, the encoder maps the spectral data to abundance values, which are then reconstructed back to the spectrum by the decoder as endmembers. (502,503) Georgiev et al. and Su et al. further improved the physical interpretability of autoencoders by introducing a non-negativity constraint for the endmembers and a sum-to-one constraint for the abundances. (504,505) Experiments with both synthetic and biological data sets demonstrated that this constrained autoencoder approach produced more accurate endmember and abundance estimates than conventional unmixing methods. Apart from autoencoders, lots of relevant efforts in hyperspectral unmixing have pointed out alternative architectures exhibits the potential of competitive endmember/abundance estimation for SERS, including CNNs, (506) U-Net, (507) Transformer, (508) and unsupervised learning. (509) We believe developing advanced deep learning-based unmixing methods integrated with the properties of biological samples and experimental design will further boost the deployment of SERS technology in biomedical applications.

7. SERS Quantification: Emerging Techniques

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Following the first discovery of SERS-enabled single-molecule detection, (8,9) floods of works have revealed the detectability of multiple analytes in ultralow concentrations (down to femtomolar or even attomolar level (510−512)) by various kinds of SERS substrates, even simply by the conventional Ag nanospheres. (513,514) The bianalyte SERS technique proposed by Le Ru et al. (515) has been widely used as a statistic proof of the single-molecule sensitivity of the SERS substrate. Nevertheless, the cons come along with the pros that the ultrasensitive electromagnetic hotspots are highly localized and heterogeneous from each other due to the atom-level variations beyond control (516,517) (Figure 26a). Therefore, a slight difference in the adsorption site and orientation of an analyte molecule on the substrate may cause a relatively large SERS signal fluctuation, causing irreproducible intensity-based quantification. (518)

Figure 26

Figure 26. Emerging techniques for SERS quantification. (a) Different factors causing SERS intensity fluctuations including different electromagnetic enhancement due to variable distance between the analyte and the substrate surface (i, ii) and variable adsorption site (i, iii), different adsorption orientations (iv), atomic mobilization (v), NP aggregation (vi), and heterogeneous substrate morphology (vii). (b) Signal calibration by the internal standard. The signal intensity of the analytes can be interfered by a lot of uncontrollable factors while the ratio of analyte signal to internal standard signal can correct these variations, realizing more robust quantification. Three typical strategies of internal standards: (c) co-adsorption of the internal standard molecules with the analytes on the SERS substrate and (d) embedment of the internal standard molecules in the core–shell NP and sensing of the analytes on the outer layer via direct adsorption, specific capturing or analyte-induced chemical change of the surface functionalized molecules. (e) Intrinsic internal standards originated from the substrate and the solvent. (f) The concept of digital colloid-enhanced Raman spectroscopy by single-molecule counting. (g) Dual-mode SERS and electrochemical detection of miRNA. Adapted from ref (523).

Given reproducibility is the premise of any real-world application, (519) primary efforts have been put into advancing the SERS substrate toward ever-higher uniformity. For example, lithography technique, only widely applied in the recent dozens of years, is well acknowledged to produce highly repeatable arrays. (375) Nevertheless, the hotspots are still heterogeneous within one substrate and across different substrates and the difference is unable to be corrected due to the irreversible molecular capture on dried substrates. While for wet chemistry, the uniformity across NPs is much more difficult to control. Though surfactant may enhance the mediation of surface morphology to some extent, (520,521) the surface accessibility to analytes could be largely deteriorated. (522) In this sense, emerging technical routes have been explored to improve the quantitative reproducibility toward reliable and usable SERS.

7.1. Internal Standards SERS Nanoparticles

The analog intensities, generally used for the quantification of relatively higher concentrations, are vulnerable to the variables including hotspot enhancement factor, substrate-molecule interaction and instrumental differences. Internal standard (IS) is a calibration methodology that has been long practiced in analytical chemistry far beyond SERS to correct the signal fluctuations out of manual control. (524) For the effectiveness of calibration, the IS chemical should be carefully selected to compensate all the variables (β) involved in the quantification except the concentration (C), i.e.,
βa(Ca)=βIS(CIS)
(1)
In this sense, given the IS concentration (CIS) is predetermined, i.e., fIS(CIS) is a constant value, the analyte concentration (Ca) can be reliably deducted from analyte signal intensity (Ia) based on a pre-established reflection without the above-mentioned interfering factors after being corrected by the IS signal (IIS) (Figure 26b).
IaIIS=fa(Ca)βa(Ca)fIS(CIS)βIS(CIS)=fa(Ca)fIS(CIS)(whenβa(Ca)=βIS(CIS))
(2)
Isotopically edited counterpart of the analyte molecules has been usually used as the IS in regard to the distinguishable vibrational signatures and comparable chemical and physical properties as the analytes including the affinity to the SERS substrates and Rama cross-section (525) (Figure 26c). This may guarantee the optimal correction of the variables that the analyte molecule encounters with distinguishable IS signals. (526) Conceptual validation has been primarily explored with some classical fluorescence dyes such as rhodamine 6G (R6G-d0), rhodamine B (RhB-d0), nile blue (NB-d0) and crystal violet (CV-d0), with their isotopically edited counterparts as the IS, e.g., R6G-d6/d2, RhB-d2, NB-d2, and CV-d6, respectively. (525,527) Apart from the hydrogen atom, isotopic editing has also been implemented on the carbon atom and the nitrogen atom such as mononucleotide (e.g., 2′-deoxyadenosine-13C10,15N5-monophosphate) (528) and 13C caffeine. (529) Though the absolute localized enhancement factor is not easy to be corrected, the IS has presented superiority in correcting the enhancement factor across different probe volumes, substrate batches and measurement conditions in the collective sense via random coadsorption on the electromagnetic hotspots with the analytes. (525,529) This leads to minimized signal fluctuations within one sample, across different samples and over time for enhanced quantitative repeatability. In biosamples with abundant uncertainties varying the detection capability, IS should be particularly utilized to reliably unveil the biological process, such as the research on the toxicokinetic behavior of paraquat in plasma and lung tissues (IS: paraquat-d8). (530) While for real applications, the isotopically edited counterparts are not easy or not cheap to obtain, a more generalizable alternative can be using the chemicals with similar structures for optimally comparable molecular properties. For example, metformin hydrochloride was used as the IS for phenformin hydrochloride and streptomycin sulfate was used for tigecycline to increase the linearity of SERS detection. (531)
Label-free strategy has been long suffered from the competitive adsorption, so is the situation between the analyte and the corresponding IS chemical, leading to uncertain effective IS concentration, Therefore, a more stable binding of IS molecules on the SERS substrate and the protection of the IS molecules from the background conditions have been paid specific attention to in the design of the plasmonic sensor. (532) The most widely applied strategy is to use the Raman reporters with thiol moiety embedded in the gap of a core–shell structured NP (Figure 26d). In this way, the concentration of the IS molecules is primarily maintained constant. For instance, 4-aminothiophenol was embedded in the interface of Au core@Ag shell NP as the IS, thereafter the reduced signal of methylene blue caused by the competitive adsorption of okadaic acid on the aptamer was corrected by the IS signal for anti-interfering and selective detection of okadaic acid in real food and environmental samples. (533) Similarly, reported by Jiang and co-workers, 1,4-benzenedithiol, embedded Au core@Ag shell NPs, was also used as the IS for the detection of H2O2 and cholesterol inside living cells at single-cell level. (534) In addition, reporter molecules with alkyne and nitrile groups are extremely preferred for the detection in biosamples for their unique signatures in Raman silent region (1800–2800 cm–1) so that the IS signals can be extracted with high confidence from the background signals. (535) Mei et al. reported using 4-mercaptobenzonitrile as the IS to detect a representative bacterial metabolite, pyocyanin, released in skin interstitial fluid for wound infection diagnosis. (536) The identical IS embedded in the gap of Au core–shell nanoflower has also been applied to calibrate the signal change caused by the reduction of 4-nitrothiophenol functionalized on the outer Au shell to 4-aminothiophenol related to the endogenous and exogenous H2S in living cells. (223)
Another kind of IS strategy is to use the intrinsic signals originated from the substrates and the solvents which can free the researches from extra labor in IS functionalization (Figure 26e). These intrinsic IS signals have also been proved elite in calibrating the heterogeneous hotspots as well as batch-to-batch and instrumental variations. Graphene, with a second-order 2D peak at about 2655 cm–1 in the silent region, is suitable to be selected as the IS chemical. In the meanwhile, the decoration of graphite on the NPs and dry substrates can enhance the uniformity and stability. (537,538) Other chemicals introduced in the substrate fabrication for large-scale substrate uniformity (e.g., poly(4-vinylpyridine) (539)) and organic solvents (e.g., ethanol, (540) acetone (541) and dichloromethane (542)) can also be utilized as the IS chemicals. Besides, plasmon-enhanced electronic Raman scattering, reflecting the electromagnetic fields at the identical spot, has also been applied to calibrate the analyte Raman scattering signals in a temporal and spatial manner. (543)
Though there are abundant variables in SERS measurements to cause nonrepeatable quantification, IS protocol has not yet generally practiced in all the quantitative biomedical applications to date. As mentioned above, IS calibration should be promoted especially in biosamples for reliable quantification and the IS chemicals must be carefully selected toward the most confidently discriminable IS signatures and the least spectral overlapping with the analyte-originated signatures. IS stability in effective concentration and properties are also essential for reliable calibration given the varied background conditions and target analyte concentration.

7.2. Digital SERS (dSERS)

While for relatively high analyte concentrations, the random fluctuation of analog intensity may be averaged out upon a wealth of analyte-hotspot interactions in the probe volume after IS calibration, as the lowering concentration, SERS single-molecule intensity fluctuations (SIFs) become increasingly prominent. Therefore, many efforts have been applied on the spectral analysis, particularly, focused on statistical patterns of the spectra rather than individual intensities. As the signal intensity distribution changes from a Gaussian-like to a long-tail pattern as the analyte concentration decreases, (9,544) the parameters derived from the exponentially modified Gaussian distribution (545) and the truncated Pareto mixture distribution (544) have been explored for the quantification of low concentrations. However, the number of noise spectra may eventually overwhelm the number of signal spectra as the lowering concentration, making it difficult to obtain a robust distribution for analyte quantification upon limited spectral number in the practical sense. (515) Therefore, the SIFs are picked out and analyzed for their frequency. To this regard, the fundamental hypothesis is that the probability of single-molecule events should correlate with the analyte concentration even if the concentration is quite low. Herein, fast Fourier transform (FFT) is a mathematic method commonly used to represent frequency. Brulé and co-workers computed the cutoff frequency between the white noise (the noise spectra) and the pink noise (the bright single-molecule events) after FFT of the spectral signals, which showed a Freundlich isotherm with the analyte concentration. (546) Toward a “standardization” process of SERS protocols, statistics including the root mean squared error of prediction, the standard error of performance, bias, etc. have also been suggested to evaluate the reproducibility and trueness of a quantitative SERS method in the interlab scale with regard to distinctive instrumentations, SERS substrates and manual operations. (547)
Digital protocol was first proposed by de Albuquerque et al. to address the signal fluctuations at low concentrations as the scarce probability of a detectable single molecule and the variable single-molecule signal intensity. (548) Apparently, the analog-to-digital conversion can largely spare the quantitative statistics from analog intensity variations since only binary digit (positive or negative state) is given attention. In this work, they prepared the dried liquid sample on a self-assembled Au substrate and demonstrated the limit of quantification of enrofloxacin and ciprofloxacin down to 2.8 pM by counting the positive spectra acquired on the substrate. Technical follow-ups of the digital protocol have been explored using different SERS substrates and for various analytes. (549−552)
While the obstacles inherent to dried substrates may still deviate the detected concentration from the original analyte concentration in the sample, so it is impossible to preestablish a calibration curve usable in subsequent applications. To reduce the digital concept to practice, Bi and co-workers proposed a colloid-based single-molecule detection approach, i.e., digital colloid-enhanced Raman spectroscopy (dCERS) (16) (Figure 26f). In the colloidal system with both hotspots and target analytes randomly and statistically uniform-distributed in the probe-volume level, single-molecule events follow the Poisson distribution. Therefore, desired reproducibility and measurement accuracy can be achieved at ultralow concentrations under the practical demands on accuracy and time efficiency as it is largely determined by the accumulated single-molecule counts 3. (N: the number of detected single-molecule events)
error (%)=1/N×100%
(3)
Digital SERS is actually a tool for efficient spectral analysis with a uniquely statistical perspective to interpret the SERS signals. Analytes at ultralow concentrations can be thereby not only identified but also reliably quantified, propelling SERS toward real-world applications. A range of molecules including proteins, nucleic acids and metabolites has been successfully quantified using digital SERS as long as with identifiable SERS signatures and adequate interaction with the SERS substrates. Actually, this can be further modified according to a specific purpose as long as a calibration curve can be preestablished for a certain analyte and a certain kind of SERS substrates. For real applicational scenarios involving more complicated background matrix and competitive adsorption for the hotspots, comprehensive sensing platform should be developed, incorporating digital SERS for spectral analysis and other pretreatment modules to reduce the interference factors and improve the detection efficiency of the target analytes. (553−556)

7.3. Dual Modality SERS Sensing

Quantification in SERS can benefit from integrating different sensing modalities during detection, an approach needed to improve sensitivity and selectivity and to overcome the impossibility of introducing an internal standard in the measurement, as, in some instances, their use is not possible or not practical. This is why, rather than foregoing the use of SERS as transduction modality, dual modality sensing can be leveraged instead. There are various transduction modalities that can be integrated with SERS in dual modality sensing, spanning from colorimetric detection (557) to photoacoustic imaging. (558) Several of these approaches find application in diagnostics and medical imaging, (559) and it is safe to say that their continued implementation and optimization is motivated by the rapid growth of SERS sensing in clinical diagnostics. However, dual modality SERS sensors most often integrate electrochemistry (560) and fluorescence, (561) likely because of the fundamental overlap among SERS and these techniques.
The SERS effect was first discovered and explained for systems in which a Ag electrode was also acting as the SERS substrate (1,562) and, likely because of this reason, it has been straightforward to envision the integration of electrochemistry (EC) and SERS. However, only recently electrochemical SERS (EC-SERS) has become mainstream, (563) with applications in biosensing, catalysis, and environmental analysis. EC-SERS is generally used to boost the sensitivity of SERS sensing, owing to the fact that analyte-metal interactions at the nanostructured electrode surface can be enhanced in the presence of an applied voltage, thus boosting the limit of detection. Nonetheless, for this approach to work, several constraints need to be taken into account. (564) First of all, one should consider the cleanliness of the nanostructured surface, which also implies the need to remove surface passivating agents in the instances in which the electrode has been fabricated by substrate deposition of colloidal NPs, as the presence of these materials can introduce spurious signals and increase nonfaradaic contributions. Second, the electrolyte properties need to be considered, as these molecules may interfere with the analyte and alter the local ionic strength of the system, thus affecting the SERS response. Furthermore, the laser excitation can locally alter the redox properties of the analytes depending on the wavelength used, thus affecting the electrochemical response. However, this effect, if properly understood and characterized for each analyte, could improve selectivity in multiplexed EC-SERS sensing. Despite these caveats, EC-SERS has found increasing applicability, with a substantial growth in applications covering the detection of disease biomarkers, (565) with combined equipment already on the market. (566) For instance, Zhou et al. have designed a combined EC-SERS sensor based on toehold-mediated amplification to detect miRNAs with LODs of 2.2 and 0.12 fM for the EC and SERS modalities, respectively, also in cancer cells (Figure 26g). (523)
However, an interesting approach in EC-SERS stems from the opportunity to leverage the EC modality to quantify the surface area of the nanostructured sensor surface. This approach is extremely important in terms of analyte quantification in the absence of an internal standard, as the effective surface area of the sensor is a relevant parameter for analyte concentration calculation in SERS, for which however an exact determination is often impossible, especially in the case of nanoscale roughness. Sardar et al. leveraged EC-SERS to increase accuracy in SERS quantification by fabricating a series of SERS substrates constituted by screen printed electrodes (SPEs) with increasing amounts of deposited Au NPs and characterizing them electrochemically with cyclic voltammetry (CV) and chronocoulometry. (567) This initial step was then followed by deposition of increasing concentrations of Raman-active aminothiophenol (ATP) and SERS analysis. By combining the two methods, they were able to correlate the substrate charge (measured by EC) to the ATP signal intensity (measured by SERS) with tight root mean squared error (RMSE) variations, thus confirming the power of the dual modality method in improving quantification accuracy enabled by the introduction of controlled signal diversity in the system.
Dual modality fluorescence-SERS detection is, in addition to EC-SERS, the most studied and leveraged sensing method. Likely because fluorescence/SERS modulation can be achieved by varying the distance from the metallic surface of a molecule that is fluorescent and Raman active, (568) both direct and indirect SERS sensing methods can be envisioned. Dardir et al. developed an ON/OFF indirect SERS sensor comprising Au nanostars and fluorescently labeled DNA strands to recognize influenza A (IAV) viral RNA both in buffer and intracellularly, even at the single particle level. (569) In this approach, the fluorophore, bound to the 3′ end of the surface-functionalized DNA, is brought in proximity to the metal, or away from it, depending on whether the viral RNA is present or not, with a consequent ON/OFF signal in the SERS response. At the same time, the corresponding fluorescence signal is turned OFF/ON, enabling detection not only by means of SERS but also, complementarily, by fluorescence imaging, providing both robustness and sensitivity to mutations. By slight modifications to the nanostar-bound DNA sequence, the method can be envisioned to be responsive primarily to fluorescence (with ON signal in the presence of viral RNA) for seamless introduction to virology laboratories, similar to what can be done with nanoflare-based detection. (570) SERS-tag design can also be leveraged to enable both fluorescence and SERS transduction without real-time modulation of distance-dependent SERS and fluorescence signals. For instance, Bhamidipati et al. showed that the combination of SERS and fluorescence can enable quantification and spatial localization of prostate specific membrane antigen (PSMA) molecules on cells and tissues of prostate cancer patients (571) and improve patient stratification, compared to immunohistochemistry or histopathology alone. (572)

8. Surface-Enhanced Raman Scattering in Biomedicine

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8.1. Nucleic Acid Multiplexing SERS Detection

Nucleic acids are the carriers of genetic information and play significant roles in cellular processes, such as cell division and protein synthesis. Abnormalities in nucleic acids and their modification levels have been proven to be closely related to diseases such as cancer. The development of analytical methods for nucleic acid detection has become a major focus of research in life sciences. Currently, the most common methods for nucleic acid detection include quantitative polymerase chain reaction (PCR), digital PCR, and next-generation sequencing (NGS). While highly accurate, these fluorescence-based methods present challenges for multiplex detection, and NGS-based approaches require long turnaround times (3–4 h) and expensive specialized equipment. SERS is emerging as a new tool for nucleic acid detection, offering the advantages of ultrahigh sensitivity and multiplexing capability. In this section, we will review recent progress in SERS-based multiplex detection of nucleic acid markers, such as microRNAs (miRNAs), (573−576) gene mutations, (577) fusion genes, (578) and viral RNA. (579)
In 2020, Si et al. developed a SERS sensor array for the spatially multiplexed detection of four cancer-associated microRNAs (miR-1246, miR-221, miR-133a, and miR-21). (573) To achieve highly sensitive detection, they integrated SERS with a nonenzymatic DNA cycle amplification strategy known as the catalytic hairpin self-assembly (CHA) reaction. The sensor array, consisting of four different sensing units, was constructed by immobilizing hairpin DNA 1 (HP1) onto Au/Ag alloy NP-coated detection wells. For each target miRNA, specific SERS nanotags were prepared by modifying Au/Ag alloy NPs with Raman reporter molecules and HP2. The presence of target miRNAs initiated the CHA reaction, where the hybridization between HP1 and the target miRNA exposed a hidden toehold in the HP1 stem, allowing it to hybridize with the HP2-functionalized SERS nanotags. This process successfully anchored the SERS nanotags onto the sensor array and released the target miRNA for the next cycle of detection. This strategy enabled the detection of the four target miRNAs in cellular RNA extracts with a detection limit of 0.15 pM. However, the entire process took over 4 h and was not capable of detecting all four miRNAs simultaneously in a single sample.
To reduce assay turnaround time, a lateral flow assay (LFA)-based SERS sensor was combined with CHA for detecting lung cancer-related miRNA biomarkers. (574) SERS nanotags were functionalized with biotinylated HP1 (biotin-HP1), monoclonal antibodies, and Raman reporters. The system was designed with two test lines, each fixed with a different HP2 sequence, enabling spatially duplex detection of miR-196a-5p and miR-31–5p. When the SERS nanotags passed through the conjugate pad, the CHA reaction was initiated by the target miRNAs, which hybridized with biotin-HP1, exposing biotin molecules. As the solution flowed along the strip to the nitrocellulose membrane, the CHA reaction continued, activated by the HP2 sequences fixed on the test lines. This reaction led to the self-assembly of the two HP 2s, releasing the miRNA for subsequent cycles. The SERS nanotags with exposed biotin molecules were then captured by streptavidin-modified test lines, where the accumulation of nanotags created numerous “hotspots” that amplified the SERS signals, enabling detection down to the nanomolar level. Excess SERS nanotags were captured by a control line coated with antihuman IgG. The CHA-assisted SERS-LFA assay was further validated with real serum samples from 30 healthy donors and 120 lung cancer patients across stages I to IV, revealing increasing levels of both miRNA biomarkers with advancing stages. The results closely matched those from the standard quantitative reverse transcription-PCR (qRT-PCR) assay, with relative errors below 5%. Additionally, the entire process took only 40 min, highlighting its potential for clinical applications. Building on this work, Mao et al. from the same research group improved the assay’s efficiency, enabling the detection of dual miRNAs on a single test line and reducing the assay time to 30 min (Figure 27a). (575) This was achieved by immobilizing two different HP2 sequences on a single test line. The use of Au nanocages as SERS nanotag substrates further enhanced detection sensitivity, achieving limits of detection as low as 3.31 pM for miR-21 and 2.18 pM for miR-196a-5p in human urine. The accuracy of the method was confirmed through comparison with the qRT-PCR assay, with relative errors around 3%.

Figure 27

Figure 27. Multiplex SERS detection of nucleic acids. (a) An LFA-SERS sensor combined with CHA for multiplex detection of miRNAs on a single test line. Adapted from ref (575). (b) Magnetic pull-down of target miRNAs for multiplex SERS labeling, forming core–satellite complexes in the presence of target miRNAs. Adapted from ref (576). (c) Identification of nucleic acid targets using acetylene-tagged Hoechst molecules binding to DNA-tethered Au NPs. Adatped from ref (578). (d) Locker probes-assisted RPA combined with proximity-programmed SERS nanotags for the detection of fusion genes. Adapted from ref (582).

Alternatively, SERS detection can be combined with magnetic separation for nucleic acid detection. Lyu et al. developed a magnetic pull-down assisted PCR/SERS assay for the multiplex detection of circulating tumor DNA (ctDNA) mutations in the plasma of colorectal cancer patients. (577) This assay was designed using allele-specific PCR to amplify common colorectal cancer mutant targets, followed by magnetic pull-down of the PCR amplicons. The mutant targets were then identified using SERS nanotags, and the SERS signals were read out for analysis. The PCR/SERS strategy demonstrated high specificity and sensitivity in detecting plasma ctDNA, even with as low as 1% mutant alleles in a background of abundant wild-type cell-free DNA. The results from the PCR/SERS assay were consistent with those obtained using digital PCR in the analysis of nine plasma samples. However, the PCR/SERS assay offered the added advantages of a single-tube multiplex format and reduced assay time compared to digital PCR, making it a more convenient option for ctDNA analysis.
Given the exceptionally strong signals emitted by anisotropic SERS nanotags, Wu et al. proposed a PCR-free SERS assay utilizing anisotropic SERS nanotags for the detection of miRNAs (Figure 27b). (576) Ag-coated magnetic NPs and SERS nanotags were conjugated with capture and detection DNA probes that were partially complementary to target miRNAs. The presence of the target miRNAs initiated the formation of core–satellite complexes, which led to significantly enhanced SERS signals following magnetic separation. This method allowed for the simultaneous detection of three miRNA biomarkers─miR-21, miR-122, and miR-223─with detection limits of 311, 349, and 374 aM, respectively. The researchers applied this method to serum samples from 92 hepatocellular carcinoma (HCC) patients and observed that miR-21 levels decreased while miR-122 and miR-223 levels increased in 11 patients after hepatectomy. Additionally, the assay was used to detect miRNAs in α-fetoprotein-negative patients (n = 14) and in patients at various stages of barcelona clinic liver cancer (BCLC). A similar approach has been utilized for the multiplex detection of exosomal miRNAs. (580) Despite its promise for clinical translation, the entire process required more than 4 h, indicating a need for further optimization to reduce the assay time.
Unlike above the two-step hybridization methods, Xu et al. developed a more rapid and efficient approach by incorporating a magnetic pull-down SERS assay with duplex-specific nuclease (DSN)-mediated signal amplification for the multiplex detection of miRNAs (miR-21, miR-155, and let-7b). (581) This method involved synthesizing complexes of magnetic NPs functionalized with capture DNA probes and SERS nanotags. When a target miRNA was introduced, it specifically hybridized with the capture DNA probe to form DNA/miRNA heteroduplexes. These heteroduplexes were then selectively hydrolyzed by DSN, leading to the release of SERS nanotags from the magnetic NPs, which caused a subsequent reduction in the SERS signal after magnetic separation. Remarkably, the entire process took only 40 min. This method was validated using cell line samples and simulated human samples, demonstrating recovery rates ranging from 97.1% to 106.4%, highlighting its potential for rapid and accurate miRNA detection.
It is important to note that the use of NPs can sometimes lead to false-positive results, particularly at low target concentrations. To address this issue, Jang et al. developed a nucleic acid multiplexing platform that avoids the use of SERS nanotags. (579) This method involves the magnetic separation of target genes, followed by labeling with fluorophore-tagged DNA probes. The signal probes were then either released by raising the temperature of the magnetic particle solution for fluorescence detection or mixed with SERS-active substrates for signal readouts. The SERS-based detection achieved sensitivity ranging from 10 aM to 1 pM of synthetic genes, depending on the types of SERS-active substrates used. As an alternative to thermal release, strand displacement was incorporated with SERS strategies (Figure 27c). (578) In this system, acetylene-tagged Hoechst molecules were used as Raman probes that bind to their corresponding DNA sequence tethered on Au NPs. When the target nucleic acids hybridized with the complementary strand on the Au NPs, the Raman probes dissociated from the particles, leading to a decrease in signal intensity. This system has been successfully demonstrated in the identification of cellular miRNAs.
Zeng et al. introduced an innovative method to minimize nonspecific “noise” signals caused by excessive wild-type background sequences, particularly in rapid isothermal multiplex target amplification scenarios. (582) They combined locker probes with a “proximity-programmed” SERS readout for the multiplex detection of cancer gene fusion nucleic acid variants (Figure 27d). Locker probes are synthetic nucleic acid bases that feature methylene linkages between the 2′-oxygen and 4′-carbon groups of ribose, providing greater thermal stability than conventional Watson–Crick base pairs. These probes have a higher affinity for wild-type sequences, which helps in enriching mutant variants during multiplex recombinase polymerase amplification (RPA). The “proximity-programmed” SERS readout allowed precise control over plasmonic signaling based on hybridization, enabling the specific detection of multiple target amplicons within a single reaction. This approach effectively reduces nonspecific signals and improves the accuracy of multiplex nucleic acid detection.
The SERS-based multiplex detection of nucleic acids thus represents a significant advancement in molecular diagnostics, offering unparalleled sensitivity and specificity for the simultaneous analysis of multiple targets. These technologies hold great potential for applications in areas such as infectious disease diagnostics, genetic screening, and personalized medicine. However, to fully realize their clinical potential, further developments are needed to address challenges related to reproducibility, signal uniformity, and the complexity of assay protocols. The integration of SERS with automated microfluidic systems and the development of novel plasmonic nanostructures are likely to enhance assay performance, making SERS a more practical and scalable solution for routine clinical use. The continued refinement of these approaches is expected to broaden the utility of SERS-based nucleic acid detection, paving the way for more accurate, rapid, and accessible diagnostic tools in healthcare.

8.2. Protein Structure Characterization and Function Exploration

Proteins are structural and functional unit of the cell, made of amino acids that connected by peptide bonds. Raman spectroscopy can provide structural information on proteins including vibrations from amide (I and III), amino acid residues (Phe, Trp, Tyr, etc.) and protein cofactors (heme, flavin, etc.). (583,584) Resonance Raman scattering of protein cofactors can be induced by optimization of laser excitation, which combined with SERS (known as SERRS) allows for selectively collecting SERRS signals of proteins in biological complexes. (585) Intrinsic protein fingerprints can be obtained by SERS either directly or indirectly with protein ligands. SERS-based studies on proteins have widespread applications in biology and biomedicine including exploration of protein structure–function relationships, protein biomarker sensing, tumor targeting and evaluation of antitumor treatment.
Direct contact proteins with noble metals might cause protein denaturation, resulting in protein unfolding or conformation change. (586) On the other hand, coating noble metals (Ag and Au) with biocompatible materials (silica, chitosan, alkanethiol, etc.) is helpful to protect protein native structures. (587) Alternatively, semiconducting SERS-active materials like titanium dioxides are found to display remarkably better biocompatibility than noble metals and photoinduced electron transfer might occur at the semiconducting material-protein interface. (165,588) Additionally, nickel is a promising SERS-active material for proteins and it has been proven to be capable of specifically offering electrons to the redox proteins like cytochrome c (Cyt c) and Cytochrome b5, facilitating in situ investigation of redox protein electron transfer and their dynamics. (589)
Cyt c release from the mitochondria is a crucial event during the early stages of apoptosis, and SERRS has been exploited to probing Cyt c release. Nickel nanowires were used for rapid Cyt c reduction and determination of the redox states of Cyt c released from mitochondria in apoptotic HeLa cells. With the aid of Ag NPs, label-free and highly sensitive Cyt c quantification is achieved by SERRS. (590) The Cyt c–cardiolipin interaction is involved in alteration of mitochondrial membrane permeability at the early phase of apoptosis before Cyt c release from mitochondria. (591) The structural change of cardiolipin-bound Cyt c and its correlation with mitochondrial Cyt c release were explored through SERRS. (592) Herein, in situ monitoring Cyt c release from isolated mitochondria can be achieved on a nanostructured nickel film, according to which the molecular mechanism underlying Cyt c redox-controlled membrane permeability was revealed. Furthermore, Ag NPs coated with nickel shells are found useful for in situ probing of reactive oxygen species (ROS) formation from microsomal monooxygenase. Remarkably, label-free evaluation of an antitumor drug-induced apoptosis was achieved by SERRS-based monitoring of Cyt c release in living cells. (593)
SERS can probe secondary structure information on membrane-attached proteins and investigate protein–membrane interactions. Membrane-associated proteins can be characterized after immobilization on lipid-modified SERS-active supports or indirectly detected by SERS nanotags modified with protein specific antibodies, receptors or aptamers. (594,595) Phospholipid bilayers can be coated on metal supports through electrostatic interactions and other weak interactions. (596) Alkanethiol molecules are usually used to improve the stability of the lipid membranes, and they are attached onto metals through metal-S covalent bonds as a first layer, followed by self-assembly of a second layer of phospholipids. (596) Alternatively, it is also feasible for phospholipid bilayers to be covalently assembled on to semiconducting materials like TiO2 NPs. (597) Immobilization of reduced Cytochrome b5 at Iipid bilayer-coated Ag NPs has recently been achieved and electron transfer between reduced Cyt b5 and oxidized Cyt c or myoglobin was monitored by SERRS in real time. Lipid bilayer-coated SERS-active materials provide a sensitive platform for membrane protein analysis and sensing, and pave the way for in situ exploration of protein structural basis and functions. (596)
Large proteins generally consist of several distinct protein domains, and random orientation of different domains would result in poor SERS spectral reproducibility, especially for nonconjugated proteins. Biocompatible immobilization and controlled orientation are both important for protein function exploration on SERS-active substrates. To this end, spacer molecules linking proteins and SERS substrates were designed and optimized, balancing biocompatibility and Raman scattering enhancement. Then the spacer molecule-modified Ag films were used for capturing His-tagged proteins via nickel-imidazole coordination, by which controlled protein orientation and reproducible SERS spectra were obtained. This strategy enables label-free protein function exploration as evidenced by two model systems of protein–protein and protein-drug interactions. This study is crucially important for understanding the structural basis of protein functional versatility and will contribute to the fields of drug design screening. (598)

8.3. Modeling and Prediction of Protein Molecular Species

AlphaFold3 has revolutionized the field of structural biology, potentially rendering traditional protein structure prediction methods like X-ray crystallography, NMR spectroscopy, and cryo-electron microscopy. (599) While these conventional techniques provide static snapshots of protein conformations, incisive experimental approaches are still required to unveil the trajectory of protein conformational changes. In this regard, SERS has its unique merit to achieve in situ and dynamic characterization of proteins in the physiological environment. It is expected that abundant intrinsic fingerprint information provided by SERS can be used for scrutinizing the structure of proteins, as the secondary structural details can be derived from Raman spectra. To decipher the spectrum to extract the protein’s structure, i.e., to establish a spectrum-structure correlation, efficient and accurate simulation of SER spectra is crucial. Despite considerable effort to developing advanced methods for interpreting and predicting SER spectra, achieving a well-defined spectrum-structure correlation remains challenging. The primary difficulty lies not only in accurately describing the complex experimental environment surrounding proteins but also in balancing calculation accuracy with the computational efficiency of theoretical models. Although proteins possess tertiary structures, their stability and controllability are low, and their large molecular weight and dynamic conformations present significant challenges for simulating protein Raman spectra. In this aspect, such challenge is even more pronounced in SERS, as it involves not only the polarizability changes of proteins (Raman spectroscopy) but also their interactions with the surface (nanosurface science) and the localized electromagnetic field (nanoplasmonics), as illustrated in Figure 28a. To be specific, different orientations of proteins interacting with SERS substrates lead to varying conformations and surface-induced denaturation. In addition, the hotspots where proteins stay is a gradient surface-enhanced electric field. As a result, different amino acids (or chemical bonds) feel very different field strengths, which makes the prediction of SERS spectra complicated. To address this intricate trinity, researchers have explored three main aspects: the electromagnetic field, protein conformations on the surface, and polarizability derivatives. By considering above aspects, a hybrid quantum-classical strategy represents the state-of-the-art method for simulating protein molecular species.

Figure 28

Figure 28. Prediction and modeling of protein molecular species. (a) Three different fields involved in SERS: Raman spectroscopy, nanosurface science, and nanoplasmonics. The most representative examples for modeling nanoplasmonics (left) and nanosurface science (right) in the very beginning are illustrated. (601,610). (b) Models for protein in different environment: powder, solution, adsorbed protein, and free protein in the hotspots. Adapted from ref (602). (c) MD simulation can be used for screening orientations of peptides or proteins. (d) Quantum and classical electrodynamics indicate the distance-dependent phenomenon for SERS. (e) SERS trajectory in Euler space can be described as different orientations in the hotspots. (f) Schematic diagram of SPARC for calculating SERS spectra of proteins. Adapted from ref (626).

The first essential step in understanding SERS spectra of proteins is to simulate the localized EM field. To date, various simulation methods such as Finite-Difference Time-Domain (FDTD), have been used to obtain optical responses without considering the presence of the protein. By calculating EM enhancement factor of Ag spheres with different configurations, (600) Xu et al. has demonstrated single molecule SERS signal of Hemoglobin only comes from the “hotspots” between Ag NPs (Figure 28a left). (601) Furthermore, Wang et al. proposed a model featuring a quasi-uniform electromagnetic field generated in the hotspots (Figure 28b), accounting for the similarity of spectra between SERS and normal Raman. (602) It should be emphasized that surface selection rules are crucial for interpreting SERS spectra of proteins, as only the amino acids close to the metal surface with a polarizability component parallel to the direction of the EM can be selectively enhanced. Therefore, accurate description requires not only distribution and direction of EM field but also orientation of proteins and corresponding polarizability derivatives of each bond. To determine these parameters, DFT with a cluster-molecule model is the most powerful method (Figure 28a right), providing theoretical spectra that align well with the experimental spectra of amino acids and peptides. (603−610) Moreover, Huang et al. demonstrated the feasibility for predicting the normal Raman spectrum of the SARS-CoV-2 S1 subunit by calculating its fragments with hundreds atoms individually. (611) However, these methods still require enormous resources and is almost impossible to calculate Raman spectra of proteins with quantum mechanics. In this regard, molecular dynamics is indispensable (Figure 28c). which not only provides important information on orientation of proteins, but also offers a valuable insight for understanding dynamic SERS spectra. (612) Since Alvaro et al. first applied molecular dynamics to understand peptide orientation on a Ag surface, numerous studies have employed molecular dynamics to interpret SERS spectra. (288,327,613−620) For instance, Ma et al. observed consistent distance-dependent SERS features based on the intensity of aromatic amino acids and conformation from molecular dynamics. (621) Nowadays, it is even possible to obtain SERS spectra of protein species using Ab Initio Molecular Dynamics (AIMD) calculations. (622)
To faithfully address the intricate trinity, i.e. the electromagnetic field, protein conformations and polarizability, involved in SERS process, pioneering studies have established a hybrid quantum-classical strategy for analyzing the SERS spectra of small molecules, which has since inspired protein simulations. As depicted in Figure 28d, Mullin et al. combined quantum and classical electrodynamics to calculate the SERS spectra of pyridine at varying distances from a Ag substrate, revealing the distance-dependent field intensity. (623,624) Banik et al. successfully deciphered time-dependent changes in SERS spectra, uncovering the motions of small molecules within hotspots (Figure 28e). (625) Taking advantages of the tensor nature of Raman spectroscopy, they calculated the SERS spectra of molecules in different orientations by rotating the partial derivatives (PDs) of each vibration, yielding results that closely matched experimental data. As indicated in Figure 28f, it is important to emphasize that the decay nature of the strongly confined electric field generated by LSPR enables high spatial resolution of SERS in z-axis, functioning like molecular-scale computed tomography. Based on this sectioning capability and the tensor nature, Ma et al. proposed a separable PDs-based algorithm with rotational coarse-grained model (SPARC) to isolate and obtain the PDs of each amino acid. This algorithm allows simulation of SERS spectra of a complex protein in seconds through summing SERS spectra of amino acids in different sectioning layers. (626) By comparing the experimental SERS spectrum with simulated spectra of proteins in various conformations, SPARC can reconstruct the 3D protein structure with near-atomic precision. However, handling all the amino acids separately and low efficiency for screening conformations come to the shortcomings of SPARC, which can be solved with precise, data-driven approaches. Recently, a work done by Hu et al. applied random forest model to obtain the simulated SERS spectra for specific molecule, which markedly improved computational efficiency and accuracy. (627) Moreover, the graph neural networks (GNN)-based DetaNet utilizes the gradient of neural networks to calculate partial derivatives of various properties, enabling the prediction of the Hessian matrix and polarizability with the same accuracy as conventional theoretical methods, thus offering the potential for faster SERS spectrum simulations of proteins. (599)

8.4. SERS Detection of Liquid Biopsy-Based Protein Biomarkers

Protein biomarkers in body fluids are increasingly being used as liquid biopsy biomarkers for noninvasive disease detection and cancer screening. However, these disease-associated protein biomarkers are typically present in low abundance and coexist with other high-abundance biomolecules, making their detection extremely challenging. Common methods for serum protein detection in clinical laboratories, such as enzyme-linked immunosorbent assay (ELISA) and electrochemiluminescence immunoassay (ECLIA), offer detection sensitivities at the picomolar level, depending on the antibody–antigen interaction. (628) However, these methods are limited to single-plex detection, require specialized equipment, and are prone to nonspecific binding interferences. The development of various SERS immunoassays and hand-held Raman spectrometers presents a promising solution to these challenges in detecting protein biomarkers in liquid biopsy samples.
Direct SERS-based detection of target proteins typically relies on target-specific ligands and the resulting changes in Raman fingerprints upon protein capture. Gallo et al. developed a fast, easy, and sensitive SERS-based system for detecting the LGALS3PB (90K) biomarker using antibody-functionalized, Au-coated glass surfaces. (629) Raman spectra were collected and analyzed through principal component analysis, revealing distinct changes after interaction with target proteins. This method demonstrated sensitivity to detect 1 pg/mL of 90K proteins in serum samples within 30 min. Muhammad et al. fabricated a highly ordered Au NP array as the SERS substrate and functionalized its surface with aptamers specific to IL-6 (Figure 29a). (630) The aptamer exhibited conformational changes upon interacting with IL-6, corresponding to changes in the Raman intensity ratio. This method was sensitive enough to detect 0.8 pM of IL-6 in simulated serum samples. The authors further validated the effectiveness of the aptamer-SERS biosensor in monitoring IL-6 levels in the serum of X-ray-treated mice. However, both methods face challenges in multiplex protein detection.

Figure 29

Figure 29. SERS immunoassays for liquid biopsy-based protein marker detection. (a) The Au NP array functionalized with IL-6 aptamers for detecting IL-6 through changes in the aptamer’s SERS signal. Adapted from ref (630). (b) A digital single-molecule nanopillar SERS platform for parallel counting of four types of cytokines. Adapted from ref (631). (c) A plasmonic internal standard-embedded LFA-SERS platform for the duplex detection of protein markers. Adapted from ref (636). (d) A multiplex, pump-free SERS microfluidic chip for the duplex detection of protein biomarkers across multiple samples. Adapted from ref (638).

Probe-based SERS immunoassays present significant advantages for robust multiplex detection of protein biomarkers in liquid biopsies. For instance, Li et al. developed a nanopillar SERS platform for monitoring four cytokine biomarkers in melanoma patients undergoing immune inhibitor blockade treatment (Figure 29b). (631) Single cytokine detection was achieved by superdiluting samples to align with the Poisson distribution and labeling cytokines with single-particle active SERS nanotags. Confocal SERS spectral mapping was used for signal collection, and Raman signal pixels were manually counted, achieving attomolar sensitivity. This method was applied to clinical samples and successfully identified elevated cytokine levels in melanoma patients at higher risk of severe immune toxicities. However, the high cost of fabricating Au-topped nanopillars and the labor-intensive sample detection and data analysis processes may limit its clinical applications.
To address these limitations, Lee et al. proposed a more cost-effective and rapid SERS platform for multiplex detection of cardiac biomarkers, aimed at the rapid screening and monitoring of acute myocardial infarction (AMI). (632) The platform involved functionalizing Au substrates with capture antibodies that had been cleaved using tris(2-carboxyethyl)phosphine hydrochloride. Au nanocubes were used as SERS nanotags for protein labeling, and a portable Raman spectrometer was employed for signal acquisition, achieving sensitivity as low as 6.56 fg/mL for creatine kinase muscle brain and 11.81 fg/mL for cardiac troponin I. This platform allowed for rapid (10 min) screening of acute myocardial infarction severity in emergencies and monitoring of both markers post- acute myocardial infarction onset. Similarly, Ouyang et al. utilized an antibody-based SERS immunosensor for duplex detection of serum cytokines to assess tumor microwave ablation outcomes. (633) The sensor demonstrated detection sensitivities of 82 fg/mL for CC chemokine ligand 20 (CCL20) and 0.096 fg/mL for epidermal growth factor (EGF). The consistent results between the SERS immunosensor and ELISA in measuring CCL20 and EGF levels underscore the potential clinical utility of SERS for protein biomarker detection.
Researchers from the same group reported using LFA-SERS for the simultaneous quantification of two serum protein biomarkers, CCL20 and EGF. (634) In this assay, two test lines on nitrocellulose membranes were functionalized with capture antibodies specific to CCL20 and EGF. When human serum samples were loaded onto the sample pad, capillary action drove the sample through the conjugate pad, allowing it to interact with SERS nanotags, and then continued toward the test strip surface. The presence of CCL20 and EGF resulted in the formation of a sandwiched immunocomplex composed of capture antibodies, target proteins, and SERS nanotags, which generated SERS signals upon laser excitation. This LFA-SERS immunosensor achieved detection sensitivities as low as 64 pg/mL for CCL20 and 90 pg/mL for EGF. The performance of LFA-SERS was further validated in serum samples from five healthy donors and 12 hepatocellular carcinoma patients, both before and after tumor microwave ablation treatment. The study found that the tumor microwave ablation complete treatment significantly elevated CCL20 levels and decreased EGF levels in the serum of hepatocellular carcinoma patients; while incomplete treatment caused a slight decrease in CCL20 levels and a significant increase in EGF levels.
To enhance the detection sensitivity of LFA-SERS, Gao et al. redesigned the LFA strip by combining nitrocellulose membranes with a Au nanopyramid array-coated quartz substrate, both mounted on a common adhesive backing layer. (635) The Au nanopyramid array was designed to create “hot spots” and was further functionalized with capture antibodies, serving as the test zone. To facilitate smooth migration across the boundary between the nitrocellulose membrane and the quartz substrate, a surfactant bridge composed of Tween 20 and sucrose was applied to the quartz substrate. Diluted clinical blood plasma and SERS nanotags flowed through the conjugate pad and formed a complex of capture antibodies, target antigens, and SERS nanotags in the test zone. The entire process, from sample loading to data acquisition, took 30 min and was sensitive enough to detect 5.0 pg/mL of S-100β, a traumatic brain injury (TBI) protein biomarker, in blood plasma. The SERS results for quantifying S-100β in blood samples from six clinical traumatic brain injury patients were comparable to ELISA data.
To address signal variations caused by the random distribution of SERS nanotags on nitrocellulose membranes, Fan et al. introduced a plasmonic internal standard-incorporated LFA-SERS method (Figure 29c). (636) Unlike the approach by Su et al., (634) this method achieved multiplex detection of two target proteins on a single test line while also providing internal calibration by measuring in situ signals of Raman reporter molecule-embedded Ag–Au alloy NPs within nitrocellulose membranes. Compared to conventional LFA-SERS, this method significantly improved uniformity (25.22% vs 13.68%) and enhanced the detection sensitivity of carcinoembryonic antigen (CEA) (126 pg/mL vs 39 pg/mL). The SERS results also showed strong correlation with ECLIA measurements of carcinoembryonic antigen and neuron-specific enolase (NSE) in serum samples from 10 early stage lung cancer patients. Furthermore, SERS results effectively discriminated between early stage lung cancer patients and healthy donors.
Interestingly, using plasmonic nanomaterial-coated filter papers as SERS substrates also demonstrated excellent sensitivity in multiplex protein marker detection. Lu et al. developed a paper-based SERS platform using Au nanoflower-coated hydrophobic filter paper as the substrate and Au–Ag nanoshuttles as SERS nanotags. (637) This platform enabled the simultaneous detection of squamous cell carcinoma antigen (SCAA) and osteopontin (OPN) in cervical cancer serum, achieving detection limits as low as 8.628 pg/mL for squamous cell carcinoma antigen and 4.388 pg/mL for osteopontin in human serum samples. The feasibility of this SERS-based immunoassay platform was further validated by testing 150 serum samples, including those from healthy subjects, patients with varying stages of cervical intraepithelial neoplasia (CINI-III), and cervical cancer patients. The results indicated that both protein biomarker levels increased with disease progression.
Lab-on-a-chip technology, which enables automated analysis of complex biochemical reactions in microscale environments, presents an attractive approach for disease screening when combined with SERS immunoassays. Cao et al. proposed a multiplex, pump-free SERS microfluidic chip utilizing a one-step recognition release mechanism (Figure 29d). (638) The chip featured a novel Au nanocrown array as the sensing unit in the platform’s detection zone, enabling rapid and specific detection of protein markers in multiple samples simultaneously. The aptamer-functionalized Au nanocrown array captured specific protein markers, causing the complementary strand of the aptamer carrying the Raman signal molecule to detach, reducing the SERS signal. This platform achieved a 15 min measurement of serum protein biomarkers (hnRNP A1 and S100P) for early colorectal cancer diagnosis, with detection limits of 31 ng/mL for hnRNP A1 and 57 ng/mL for S100P. The SERS microfluidic chip was further validated in serum samples from 30 healthy donors and 30 colorectal cancer patients, showing good agreement with ELISA results, suggesting great potential for clinical applications.

8.5. Metabolite SERS Analysis

Metabolites are among the most important biomarkers in health management, being constantly expressed during natural metabolic pathways and altered in concentration and type during infection, ill-health, and disease. Their intermediate nature and importance in disease control mean that detection methods involving in situ observation by rapid, on-the-spot techniques are highly desired. While typical metabolomics techniques involve lengthy sample preparation and analysis, SERS has become a highly sensitive method for the detection of multiple Raman-active metabolites simultaneously, even within environments containing other biomolecules. Recent focus has been placed on studying cancer and tumor-associated metabolites, secreted upon reprogramming of the native tissue to tumor microenvironment. (639) Different types of plasmonic substrates, e.g. produced by simple methods such as drop-casting or more complex Au NP superlattices, have been used to conduct SERS measurements for metabolite sensing. Examples include the evaluation of purine-based metabolites such as kynurenine or amino acids containing an aromatic ring, such as tryptophan and phenylalanine. These metabolites are highly relevant because amino acids form the building blocks of the extracellular matrix (ECM), (640) and changes in ECM composition via tumor microenvironment remodeling have been associated with various biological processes such as ECM proteolysis and metastatic-associated epithelial-mesenchymal transition (EMT). (641) One such example is the augmented immunomodulatory activity of the indoleamine 2,3-dioxygenase 1 (IDO-1) enzyme, responsible for tryptophan to kynurenine catalysis, detected in the plasma of lung, gynecological, breast, colorectal, and melanoma malignancies. (642) The ratio between tryptophan and kynurenine content can be monitored using the SERS peaks at 760 and 560 cm–1, respectively (Figure 30a). (643) Subtle changes in peak intensity could also be detected in metabolite-spiked cell media, albeit with a reduced limit of detection compared to measurements undertaken in water. It should be considered that the presence of multiple metabolites, and/or biomolecules such as proteins, drastically affects the sensitivity and selectivity of SERS measurements. For example, protein fowling and unwanted metabolite binding to plasmonic substrates hinder their use in microfluidic systems for time-resolved measurements. (644) This issue (known as the SERS memory effect) can be resolved by using substrates where the plasmonic NPs would be covered by a layer of a thermosensitive polymer. (113) Upon irradiation with the same SERS irradiation laser (at a sufficiently high laser power density, e.g., 0.064 mW/μm2), local heating occurs, leading to localized removal of PLGA and exposure of the underlying plasmonic substrate for SERS measurements. Repeated application of this operation can be applied for real-time monitoring of metabolites, with a high degree of spatial and temporal control. By using this and other modifications to the sensing setup, high throughput SERS measurements can be performed to train machine learning algorithms, which can subsequently be used to identify the presence of selected metabolites. This idea has been recently applied to the evaluation of cell death mechanisms. (645)

Figure 30

Figure 30. SERS analysis of metabolites. (a) SERS analysis of the conversion of tryptophan (Trp) into Kynurenine (Kyn), catalyzed by the IDO-1 enzyme. Reproduced from ref (643). Copyright 2020, John Wiley and Sons. (b) Evaluation of cell death mechanisms by application of SERS and machine learning. Reproduced from ref (646). Copyright 2024, The Authors.

Similar substrates have been also applied in more complex biological models. Again based on the activity of the IDO-1 enzyme, alterations in tryptophan to kynurenine conversion were detected in cell media derived from in vitro spheroids mimicking the heterogeneity of pancreas tumors. (646) In this case, spheroids composed of cancerous epithelial PANC-1 cells and cancer-associated pancreas fibroblasts were formed, and cell media were removed for SERS analysis at specific time points. As expected, the cellular interplay in the more representative tumor microenvironment resulted in significantly increased expression of IDO-1, resulting in an upregulation of kynurenine, which was detected by SERS and confirmed via traditional ELISA. Again, the application of machine learning allowed the discrimination between conditions related to promotion or inhibition of the IDO-1 metabolic pathway (Figure 30b). A different metabolic pathway associated with tumor microenvironment alterations is the methionine salvage pathway, in which mutations in the methylthioadenosine phosphorylase (MTAP) enzyme are known to lead to an accumulation of its main substrate, 5′-methylthioadenosine (MTA). Although the role of MTAP in cancer is still not fully understood, it is well-known that many cancers express a deletion of MTAP, in turn leading to alterations in MTA levels. By using Au NP-based SERS substrates, accumulation of MTA was identified in supernatants from cells exhibiting deletion of the MTAP enzyme. (13) Again, cell coculture models, designed to better represent the tumor microenvironment in vitro, effectively showed an intercellular crosstalk involved in metabolic activity, as shown by the ability of fibroblasts to metabolize extracellular MTA. This is an interesting example of the application of high-throughput SERS analysis to discover unknown cellular communication mechanisms that play a significant role in the development of cancer or other diseases.

8.6. Metabolic Phenotyping (SERSome)

Molecular profiling plays a critical role in providing a comprehensive view of complexed biosystems, generally performed in aim of systematic monitoring, biomarker screening and disease diagnosis. Label-free approaches accompanied by related computational methods are used for this purpose. While the challenge is the limited and unrobust information one can acquire from one single spectrum since the molecular composition may emerge stochastically in a certain probe volume and on the electromagnetic hotspots (which also causes signal fluctuations due to differences in electromagnetic fields and molecular orientation). Mean spectrum is among the simplest and most applied way to reduce the fluctuations, while rare events generated by molecules of trace concentration, poorer affinity to metallic surface and/or lower cross sections could be averaged out, leading to information loss (Figure 31a).

Figure 31

Figure 31. SERSome for robust metabolic profiling. (a) The concept of SERSome and the comparison with single spectra and averaged spectra for the detection of complex molecular systems. Reproduced from ref (652). (b) Convolutional neural network for SERSome analysis and SERS-based disease diagnosis. Reproduced from ref (648). (c) SERSome-based biomarker screening and the subsequent biological validation of the biomarker candidates. Reproduced from ref (650).

To this point of view, the concept of SERSome was recently proposed, (647,648) using a whole spectral set rather than a single spectrum or the mean from a handful of spectra to preserve all the molecular information that could be potentially captured (Figure 31a). This approach is intended to facilitate more comprehensive profiling with enhanced capability of identifying biomarkers and classifying different biological states. SERSome has primarily exerted its effectiveness in metabolic profiling as it is sensitive enough for rapid small molecule detection in liquid samples compared to spontaneous Raman scattering-based techniques. Recently, progress has been witnessed in disease diagnosis using human serum (648) and cellular profiling even in the single-cell level using lysates and culture medium. (649−651)
Pretreatment protocols have been optimized for efficient SERSome-based metabolic profiling of different kinds of bioliquids. Specifically, a substantial content of proteins and other large molecules should be avoided, otherwise the detection sensitivity would be reduced due to less available electromagnetic hotspots, for example, by protein corona effect. (648,649) Such a pretreatment step can also primarily ensure the research object to be metabolite species. While it should also be noted that, as for SERS detection, chemicals involved in the pretreatment procedures should be taken keen consideration for minimal interfering signals and influence to SERS substrates. (650)
The method for spectral set analysis should be carefully selected as the molecular signatures are embodied in a 2-dimensional data matrix. Generally, Pearson’s correlation can be used at the system setup stage to roughly estimate the spectrum number for reproducible profiling under the criteria of adequate consistency between the mean spectra from two spectral subsets acquired from one sample. (648) While in regard to the stochastically detected molecular signatures within a spectral set, Wasserstein distance is used to directly compare the similarity between two spectral set by computing the variation between the data distributions. This index can also be utilized to differentiate sample classes. (652) Deep learning methods including convolutional neural network lie in another major route for SERSome analysis given the advantage in spectrogram texture extraction. Multiple analysis tasks including background removal, data augmentation and classification can be incorporated into the system for improved classification accuracy (648) (Figure 31b). As SERSome has revealed richer molecular information with higher robustness, classical statistics and interpretable models have been explored to indicate biomarkers. Typically, Gradient-weighted Class Activation Mapping (Grad-CAM) can be used as an additional deep learning module following the classification module by retrospectively excavating the most important features contributing to the classification. By referring to the spectral bands indicated in the saliency map, biomarkers can be deduced according to their characteristic vibrational signatures. (648) As for any techniques for biomarker screening, subsequent biological experiments are critical for biomarker validation (Figure 31c). (650,653)

8.7. Selective Small-Molecule Metabolite SERS Sensing

SERS substrate offers great flexibility to be tuned by surface engineering for selective small-molecule metabolite capturing and sensing. We identify two main strategies in achieving selective metabolite sensing: 1) analyte manipulation and capturing, and 2) hybrid techniques (Figure 32). The first strategy can be further categorized into chemical analyte capturing and physical confinement, both of which focus on selective enrichment of the target analyte near the hotspot to amplify their SERS signals. Specifically, chemical analyte capturing utilizes chemical interactions between engineered NPs and the target analytes to facilitate electron transfer and facilitate analytes’ access to the NPs’ electromagnetic field, whereas physical confinement selectively concentrates metabolites near the nanosensor surface to enhance intermolecular interactions. On the other hand, hybrid techniques couple SERS with another analytical technique to synergistically enable enhanced performance by accumulating analyte-specific signals to distinguish the target analytes SERS signals from other interferences. It is of note that multiple strategies are usually integrated in the development process of a SERS-based sensor to achieve both desired sensitivity and specificity.

Figure 32

Figure 32. Overview of strategies for selective small metabolites sensing.

8.7.1. Analyte Manipulation and Capturing

To manipulate and enrich the target metabolites at the SERS hotspot, two main approaches are chemical analyte capturing and physical analyte confinement. For selective metabolite sensing, three strategies of chemical analyte capturing are (i) inducing chemical interaction using small probe molecules, (ii) specific capturing using biomolecular probes, and (iii) array-based sensing.
i.

Inducing chemical interaction using small probe molecules. In the chemical analyte capturing strategy, typically a self-assembled monolayer (SAM) of small probe molecules is grafted onto the SERS substrate to selectively form chemical interaction with a specific functional group on the target metabolites. SAMs are thin layers of molecules spontaneously organized into ordered structures on a substrate, with a headgroup binds to the substrate and a tail group that orient to surrounding environments, capable of inducing interactions. (654−657) By tuning the chemistry of the tail group, selective metabolite sensing can be achieved by either selective capture the target metabolite or inducing analyte-specific signal. (658,659) Small probe molecules are able to induce a wide range of interaction depending on the functional group present on the probe, from covalent such as boronic ester and imine chemistry, to noncovalent such as electrostatic, hydrogen bonds, dipole–dipole, pi-pi stacking, and van der Waals interaction. (382,436,660,661) For example, 4-mercaptoboronic acid (4-MPBA) was employed to selectively form boronic ester with analytes containing vic-diol group such as pregnane and tetrahydrocortisone in urine, enabling quantification down to 10–10 M for accurate diagnosis of miscarriage in pregnant women. Notably, the small probe molecule strategy does not require selective interaction with a specific analyte, but it can form interactions with a wide range of analytes, as long as each of them yield analyte-specific spectral changes. In principle, the interaction with analytes should result in probe-analyte interactions with either specific geometry configuration or specific interaction energy to yield analyte-specific SERS peak shift or change in intensity. Particularly, 4-mercaptopyridine (MPY) was employed to differentiate among four chondroitin sulfates, which are isomeric analogs consisting of similar core structures but differed in substitution group position and number (Figure 33a). (662) The molecular recognition capability is achieved by 1) charge complementarity and 2) geometric complementary/mismatch between the chondroitin sulfate’s sulfation patterns and the rigid binding pocket formed by MPY’s pyridine N atom and adjacent aromatic H atom. As a result, a classification accuracy of >97% and multiplex quantification error at <3% is achieved for all 4 chondroitin sulfates at micromolar concentration. In another study, 6-thioguanine was employed to differentiate among 16 glycerol derivatives consisting of at least two H-bond forming functional groups at >95% accuracy. Such high accuracy stemming from different energies of H-bond network in the 6-thioguanine-analyte complexes and the specific interatomic distance between 6-thioguanine’s binding site and the analytes, resulting in an analyte-specific spectral changes. Collectively, these studies illustrate a wide range of use of small molecular probes for selective metabolite sensing, demonstrating a great potential for biomedical applications due to their simplicity of fabrication and high tunability to adapt to various types of metabolites.

ii.

Specific capturing using biomolecular probes. In the scenario where exceptional metabolites selectivity is required, the use of biomolecule probes such as antibodies and aptamers is preferred to form a lock-and-key complex with a perfect receptor-analyte geometry matching, despite being more expensive to produce and require stricter storage conditions. (663−665) It is of note that this strategy does not rely on the intrinsic signal of the analytes, thus requiring an additional SERS nanotag for signal transduction. Antibody offers exceptional selectivity by forming multiple interactions with the target analyte with high geometric complementarity. For instance, a SERS sandwich immunosensor comprising an antibody attached to SERS substrate Au nanopillars was employed to capture and detect three types of mycotoxin: ochratoxin A, fumonisin B, and aflatoxin B1 at sensitivity down to picogram/mL in human serum (Figure 33b). (666) An alternative option for exceptionally selective analyte capturing is aptamer, which has lower production cost and higher stability, thus more suitable for clinical applications. For instance, the simultaneous detection of two mycotoxin Ochratoxin A and Aflatoxin B1 in maize meal at pg/mL level were achieved by using an aptamer-functionalized Ag@Au core–shell NPs. (667) Collectively, biomolecular probe demonstrates a great potential for SERS-based metabolite sensing due to their high selectivity and customizability to adapt to various types of analytes, ranging from small metabolites to macromolecules.

iii.

Array-based strategies. So far, our discussion on chemical analyte capturing is focusing on the specific identification and quantification of a single analyte and/or a class of analyte. However, practical biomedical applications often require multiplexing capabilities to concurrently identify and quantify multiple metabolites or to differentiate complex, multicomponent mixtures without explicitly analyze an individual metabolite. In such scenario, array-based SERS sensors are an excellent candidate because it can accumulate chemical insight from multiple substrates with different chemistry to produce high-dimensional signal patterns unique to the analyte mixture which reflects a specific disease state. The potential of array-based methods in biomedical applications is evidenced by a substantial increase in the number of studies employing them for analysis in breath, urine, saliva, etc. (435,668−671) For example, by accumulating breathomic profile encoded as spectral changes in three small-molecule probes, COVID-19 detection at sensitivity of 96.2% and specificity of 99.9% were achieved (Figure 33c). (435) This is because different small molecule probe consists of different functional group such as pyridine, carboxylate, and amine, to interact with various analytes in breath (alcohols, aldehydes, ketones, aromatic compounds) via a wide range of interactions such as H-bond, π–π interaction, dipole–dipole interaction, thus providing a more comprehensive breath profile. Collectively, the examples discussed in this section demonstrate a huge potential of chemical analyte capturing for selective metabolite identification and quantification for disease diagnosis and monitoring.

Figure 33

Figure 33. Analyte manipulation and capturing strategy for selective small metabolite sensing. (a) Schematic illustration of the use of charge and geometry complementarity/mismatch to direct the probe–analogue coupling toward the chondroitin sulfates’ isomerism sites and induce multidentate interactions for formation of analogue-specific complex geometries when 4-mercaptopyridine (MPY) is selected as the multidentate probe, yielding differentiable SERS spectra as evidenced by distinct clusters on PCA score plot. (b) Detection of mycotoxins using SERS competitive immunoassays. (c) SERS-based sensor chip consisting of multiple SERS nanotags for accurate COVID-19 detection. (d) Mechanism of selective physical confinement using MOF, in which molecules with diameter larger than the pore aperture (2-naphthaldehyde) is separated , whereas 4-ethylbenzaldehyde can diffuse into the MOF, thus provide accurate quantification of 4-ethylbenzaldehyde. Reprinted and adapted with permission from (a) ref (662), (b) ref (666), (c) ref (435), and (d) ref (675). Copyright 2018, 2023 Wiley. Copyright 2022, American Chemical Society.

8.7.2. Selective Physical Confinement

Physical confinement aims to selectively concentrate metabolites based on their physical properties and bring the analytes proximal to the nanosensor surface for enhanced SERS effect, thus specifically boosting SERS intensity and detection sensitivity of the target analyte. A commonly employed strategy for physical confinement is the use of metal organic framework (MOF) to selectively confine the metabolites of interest based on their molecular size. (672−674) MOFs are a class of highly porous crystalline material comprised of metal nodes and organic linkers, capable of selective trapping molecules with molecular size smaller than their pore aperture, referred to as molecular sieving effect. (674) For example, ZIF-8-coated Au superparticles demonstrate selective adsorption of 4-ethylbenzaldehyde, which is a lung cancer biomarker in breath, but not 2-naphthaldehyde (Figure 33d). (675) Another example of selective analyte enrichment is the use of AuNP/MIL-101 for selective adsorption of small molecule such as 4,4′-bipyridine in the presence of bigger molecule such as poly(4-vinylpyridine) as interference. (676) Leveraging the molecular sieving capabilities, the author showcases detection of p-phenylenediamine down to 0.1 ng/mL in the sewage water and river water. Such molecular sieving effect showcase MOF’s potential for practical applications, in which exclusion of macromolecules and cellular species is crucial for accurate detection of small metabolites.

8.7.3. Hybrid Techniques

Hybrid techniques, which couple SERS with another analytical method, enhance the metabolites detection selectivity by 1) combines multiple types of transducing signal to complement the information provided by each other (multimodal), or 2) couples analyte detection methods with additional molecule adsorption enhancement techniques (hyphenated). In a multimodal technique, an additional analytical method is employed, serving as a cross-validation to increase the reliability of SERS analysis, or two complement the limitations of SERS. For example, a combination between SERS and photoacoustic takes the advantage of each other for accurate location of diseased tissue and quantitative analysis of H2O2 for progress tracking of the anti-inflammatory treatment in real-time (Figure 34a). (677) Specifically, while SERS provides excellent sensitivity and specificity for H2O2 detection, its use for real-time and in vivo quantification detection has been limited due to poor tissue penetration. In this aspect, photoacoustic has enhanced tissue penetration depth and accelerated images acquisition to guide SERS in finding the lesion site, highlighting the mutual supplementation of the two modes. Although multimodal techniques offer enhanced analytical performance, it is important to note that a two-in-one instrumental platform must be built, and the SERS platform needs to be compatible with the coupling techniques.

Figure 34

Figure 34. Multimodal techniques for selective small metabolites sensing. (a) Multimodal technique using SERS and photoacoustic for accurate in vivo quantification of H2O2. (b) Hyphenated technique using electrochemical-SERS (EC-SERS) to manipulate the analyte (d- and l-tryptophan) orientation and adsorption onto the Au nanoporous bowl (NPGB), showing superiority of EC-SERS, as evidenced by distinct clusters on the PCA score plot, in contrast to the conventional SERS (no Vapplied) which results in overlapping clusters. Reprinted and adapted with permission from (a) ref (677) and (b) ref (434). Copyright 2021, Wiley. Copyright 2021, American Chemical Society.

Besides multimodal techniques, the hyphenated technique offers enhanced selective small metabolites detection by coupling SERS with a technique capable of performing analyte separation and/or manipulation, such as electrochemical and chromatographic technique. This has an immense potential in biomedical applications because the target analytes often coexist with other interfering biomarkers and macrobiomolecules present in the biological matrices. For example, electrochemical-SERS (EC-SERS) enables differentiation between stereoisomers by altering adsorption orientation of each analyte under applied voltage. (434) This was demonstrated in qualitative and quantitative analysis a mixture of enantiomers (l-/d-tryptophan and R/S-propranolol), using nanoporous Au bowl as the SERS substrate (Figure 34b). Such stereoisomers identification and quantification are extremely important in diverse field ranging from pharmaceuticals to disease diagnosis. Another example of hyphenated techniques is chromatography-SERS for selective metabolites detection by separating it from its complicated matrices, which is demonstrated in the development of a paper chromatography-SERS individual identification of lycopene and β-carotene in a mixture. While many new hyphenated techniques show promising potential, most are still in the early stages of development, with relatively few studies conducted on real sample. This is the same case for multimodal technique, which suggests that more studies should be done to further improve analytical performance and reduce analysis time and cost to accelerate the translation of such hybrid SERS techniques into practical biomedical applications.
Collectively, we demonstrate the potential of SERS for selective small metabolite sensing by using two main strategies: analyte manipulation and capturing, and hybrid techniques. Such selective and accurate analyte identification and quantification at ultratrace levels in the presence of interferences potentially has immense impact on biomedical applications due to the small metabolites’ significant roles in many biological processes, indicating disease state and progress. However, to further drive the translation of SERS-based technologies for small metabolites sensing into practical usage, we envisage three main aspects that need to be tackled. First, it is required to benchmark SERS against gold standard methods such as chromatography and immunoassays to ensure the robustness and reliability of SERS platform. This is critical when moving from lab-based and in vitro sample to practical samples due to the presence of interferences and the samples’ heterogeneity, meaning that the target metabolites does not distribute evenly within the sample. Second, although there are various methods for enhancing selective small metabolites sensing at high sensitivity and selectivity, it is important to note that there remain factors that need to be considered for the practicality of the method, such as reproducibility, portability, time and cost for manufacturing, storage, sample preparation and measurement. For instance, upscaling can significantly alter the fabrication process because the synthesis of metal NPs for SERS-based sensor is strongly influenced by mass and heat transfer, thus requiring additional cost to maintain the sensors’ quality. In some cases, SERS sensors that require cheaper and less sample processing have higher fabrication cost and vice versa, thus balancing between these factors need to be considered. Finally, collaboration between scientists, industry, and clinicians is crucial to establish standardized protocols for sensor fabrication and storage, sample preparation and measurement, and data analysis. We envisage that through such concerted efforts, we can fully realize the potential of SERS-based technologies for small metabolites sensing and deploy them in practical settings for biomedical applications.

8.8. Single-Cell Metabolite SERS Monitoring

Single-cell metabolomics directly indicates the phenotypic diversity of single cells and offers immediate insight into how cells respond to their environments. Cells and the extracellular environment interact dynamically through a variety of molecules, including metabolites. (678) However, measuring metabolites secreted from a single cell is challenging due to their rapid metabolic dynamics and wide molecular diversity. Additionally, small-molecule metabolites cannot be readily amplified. Thus, monitoring these rare and low-concentration metabolites requires highly specific and accurate analytical techniques.
Single-cell metabolomics involves manipulating minute volumes of materials, which creates a high demand for sample handling and transfer techniques. Droplet microfluidics is a feasible high-throughput single-cell technology. (679) It encapsulates individual cells into water droplets of a picolitre volume using immiscible oil–water two phases, providing independent and closed chambers for long-term cell culture. Rapidly accumulated cell secretions in such a small volume can arrive at a detectable level. In addition, the consumption of probed reagents is significantly reduced. The combination of SERS and droplet microfluidics provides a robust platform for studying single cells and addresses several critical issues of SERS detection. For instance, the integrated platform can precisely control the aggregation extent of plasmonic NPs and the mixing efficiency of metal colloids and analytes accurately via the flow rate regulation, which can maintain coincident probability for hotspots and improve reproducibility in quantitative analysis. In addition, the droplet microfluidic system can accomplish high-throughput, consistent droplet preparation, which not only provides homogeneous conditions for robust and reliable analysis, but also allows for high-dimensional computational tools (e.g., machine learning) to handle the rich and big data sets, enabling exploratory analysis of complicated biosamples, such as exploiting biological component interaction networks or patterns to reveal latent cellular subpopulations. Therefore, the combination of SERS and droplet microfluidic technology is expected to be a promising and efficient bioanalytical tool, especially for single-cell metabolomics studies.
Aiming at tracing the single-cell metabolites, Xu et al. developed a series of microdroplet-based SERS analytical platforms to achieve the in-drop SERS assays, and the sensitivity of which can satisfy the single-cell detection level. In 2019, they introduced the SERRS effect into the droplet microfluidics to achieve ultrasensitive analysis of a minimal amount of alkaline phosphatase (ALP) in a single cell. (680) The cell suspension, ALP-catalyzed substrate BCIP (5-bromo-4-chloro-3-indole phosphate), and SERS-active AuNPs were spiked into the water phase to produce water-in-oil droplets. The colorless BCIP experienced a hydrolysis reaction catalyzed by ALP, and the oxidized intermediate compound formed blue BCI (5, 5′-dibromo-4,4′-dichloro-1H,1H-[2, 2′]biindolylidene-3,3′-dione), which is a resonant Raman-active species. Thus, the single-cell ALP activity was evaluated by monitoring the SERRS intensity of BCI enhanced by AuNPs, and the lowest detectable ALP concentration was as low as 1.0 × 10–15 M.
To detect the small amount of cytokines secreted by a single cell, a droplet technique was integrated with the magnetic field amplification SERS strategy for the simultaneous detection of two cytokines secreted by the single cell. (681) Immuno-magnetic beads (iMBs) and immuno-Ag NPs were packaged with single cells in water-in-oil droplets. Cytokines produced by the cells would bridge these immuno-probes to form nanoaggregates, switching on the SERS signals of reporters presettled above the iMBs. This method achieved the highly sensitive (up to 1.0 fg/mL), repeatable, and selective detections of vascular endothelial growth factor (VEGF) and IL-8 at the single-cell level. Later, Xu and co-workers modified this in-drop immunoassay with an elaborate bioconjugation strategy above the cell membrane of the probed cell surface. (682) By grafting the membrane proteins with Sulfo-NHS-LC-biotin (sulfosuccinimidyl-6-[biotin-amido]hexanoate), the capture probes and SERS nanotags were anchored to the cell membranes with secreted proteins as bridges, resulting in cytokine enrichment above the membrane and SERS signal amplification effect. This secreted protein analytical platform was further extended until a unique interfacial oriented aggregation (IOA) phenomenon was observed, in which the iMBs could accumulate to the cell membrane surface spontaneously, achieving a new interface ready for in-drop sandwich immunoassays. (683) The spontaneous collection of immuno-probes contributes dramatically to the SERS signal amplification according to the SERS hotspot effect. Besides, this IOA-based microdroplet-SERS platform eliminates the need for complex pretreatment relative to the cell membrane grafting strategy, resulting in minimal interference with cell metabolic function. The levels of three extracellular vesicle proteins (CD9, CD63, and FAK) related to metastasis in two breast cancer cell lines, and the single-cell SERS spectra were further analyzed and classified using supervised/unsupervised machine learning algorithms, acquiring feature subpopulations for different breast cancer subtypes. Following this strategy, a pH-responsive molecule (e.g., 4-mercaptopyridine) was used as a Raman reporter probe to decorate the immune-SERS nanotag to identify the extracellular microenvironmental pH along with the in-drop VEGF assay, (684) which will help understand the impacts of microenvironments on cell cytokines and vital for assessing early cancer diagnosis and prognosis.
Different from the secreted protein analysis, identifying small molecule metabolites is more challenging because there are no corresponding antibodies. A multifunctional magnetic/plasmonic (Fe3O4@AgNPs) composite nanotag was employed in a microdroplet-SERS platform, to realize the label-free in-drop SERS assays of various metabolites secreted by single cells. (685) This versatile magnetic SERS substrate was made by decorating magnetic microspheres (about 400 nm) with Ag NPs (about 30 nm), which could form multiscale hotspot structures due to the spontaneous aggregation in a droplet. Single-cell metabolites (pyruvate, lactic acid, and adenosine triphosphate) were directly adsorbed on the Fe3O4@Ag NPs (Figure 35), which significantly enhances the Raman signals of metabolites. Simultaneous monitoring of three cellular metabolites was realized according to their instinct Raman fingerprints.

Figure 35

Figure 35. Workflow of microfluidic droplet-SERS platform for single-cell encapsulation and simultaneous detection of three metabolites produced by a single cell. Reproduce with permission from ref (685).

The analysis of single-cell microRNA is essential for uncovering genetic-level cell heterogeneity. A high demand for tracing single-cell microRNAs is that they are highly similar and small in size and feature low-level expression. Sun and Xu et al. proposed the integration of SERS and fluorescence imaging technology into a microdroplet platform, enabling in situ, nondestructive, and highly sensitive detection of a small number of microRNA-21 in a single intact living cell. (686) A multifunctional plasmonic nanotag was created by decorating a Au NP with a fluorescent dye (ROX)-labeled DNA hairpin structure, which could lead to the dual-signal switching of fluorescence-off and SERS-on of ROX in response to microRNA-21 in droplets. The fluorescence-SERS dual-responsive strategy was able to mutually corroborate test results, improving the reliability of determining low-level microRNA expression. This advancement facilitates the exploration of single-cell miRNA-related biomedical studies.

8.9. Extracellular Vesicles SERS Monitoring and Diagnosis

Extracellular vehicles (EVs) have emerged as key biological messengers with diagnostic potential across various diseases, especially cancer. (687) In particular they may offer value as disease indicators in peripheral circulation (688) in even the earliest stages, including in multicancer detection. (445,689−691) Due to this potential, EV-SERS has been widely adopted in preclinical liquid biopsy diagnostics, where EVs are isolated from biological fluids such as blood, urine, or saliva and analyzed for early stage, noninvasive cancer diagnostics. (692) Compared to conventional omics-based molecular analysis, SERS offers a simple, quick approach to detect and profile these vesicles.
SERS substrates are typically used to enhance signals directly from EV biomarkers themselves. For example, nanoplasmonic-based detection can measure epidermal growth factor receptor (EGFR) mutations, common in several cancer types, from circulating EVs, which could be applied to screen for over 30 cancers with high sensitivity. (690) Another recent study applied EV-SERS for early stage cancer detection and tumor organ classification across six cancer types, with an impressive sensitivity of 90.2% and specificity of 94.4%.
Beyond direct disease detection, SERS has been increasingly applied to understand and assess the heterogeneity of EV subtypes, (693) for example, to distinguish melanoma EVs from red blood cell EVs, even at single vesicle resolution using only their intrinsic molecular profiles. (694) Machine learning-assisted classification has also been demonstrated to minimize the effects of lipoprotein contamination while still distinguishing between disease states, regardless of EV isolation method. (695)
Sensor architectures, especially ones tailored to the unique properties of EVs as a biomaterial, continue to improve as well. (696) While studies leverage simple surfaces of deposited nanoparticles as the primary materials for SERS-active surfaces, recent innovations beyond this include 3D plasmonic nanobowls, (693) hybrid nanoplasmonic scaffolds, (697) and nanobowtie-embedded microfluidic devices. (698) These substrates include the potential for analysis of EVs directly from biofluids. Another recent trend is the examination of the particular surface moieties of EVs in the near-field of the plasmonic “hotspots”. For example, enzymatic treatment of EV surfaces significantly altered SERS sensitivity, suggesting the importance of extraluminal domains in EV diagnostic platforms. (697) There has also recently been a surge in the analysis of extraluminal glycan moieties present at the EV surface. (699,700) For example, a recent study demonstrated a EV Glycan phenotype assay that utilized a SERS microfluidic platform, and completed the multiplex profiling of small EV glycans in nonsmall cell lung cancer in a clinical evaluation study on 40 patients. (701)
Yet, as a result of EV heterogeneity in size, origin, and cargo, their SERS spectra can be complex, such that differentiating between overlapping features is difficult. SERS signals are also sensitive to small fluctuations in environmental changes (temperature, pH, humidity) and inhomogeneities in the SERS substrates because of their manufacturing, all leading to variability in analyzed results. This complexity has led to advancements in data analytics to interpret results and extract meaningful conclusions, such as integrating machine learning techniques like supervised classification algorithms. The use of automated tools for processing has become more prevalent, driven by the need for more efficient and scalable data handling as the volume and complexity of SERS data increases. Several studies employ custom-built pipelines for EV spectral analysis, including advancements to more sophisticated model architectures like deep autoencoders. (702)
While EV-SERS research has found a strong foothold in cancer diagnostics, several gaps remain that must be addressed to further improve the utility for clinical applications. First, a lack of standardization across EV isolation techniques leads to inconsistencies in purity, which can affect downstream SERS analysis. Developing universal protocols for EV isolation is critical for achieving reproducible and reliable diagnostic results. (703) Second, the quantitative capabilities of EV-SERS are limited, an issue often exacerbated by the underlying heterogeneity of EVs and SERS substrates. Finally, as machine learning are becoming ubiquitous to enhance classification and analysis of complex SERS data sets, integrating models with increased explainability could greatly improve clinical adoption. (704)
Despite these hurdles, some emerging trends are likely to shape future research in this subfield. Chiefly, integration with microfluidics for real-time, on-chip analysis of EVs is an exciting direction, offering more streamlined and automated workflows with reduced sample handling and faster analysis times. (705) Several studies are also beginning to explore the integration of SERS with other omics techniques, such as proteomics, to provide a more comprehensive profile of EVs. (706) Finally, new interlaboratory studies to validate findings across unified protocols are also being carried out, which will certainly improve clinical potential. (707) In summary, continued advancements in standardization, quantitative capabilities, and integration with complementary technologies like microfluidics are essential for the broader clinical adoption of EVs.

8.10. SERS Endomicroscopic Imaging

SERS endoscopy is a method of integrating SERS imaging into the clinical endoscope, simultaneously provide biochemical information and anatomical tissue structure, allows real-time discrimination of pathologic changes on the internal surface of hollow organs. (708) The advantage of SERS endoscopy is the improved detection of small-sized pathologic changes that may not be visible with traditional white light endoscopy due to complex surface topography, mucosal folds, or visceral peristalsis in the intestinal tract cavity. (709) During the procedure, NPs targeting specific biomarkers, i.e., SERS nanotags equipped with a Raman reporter molecule and recognition element such as antibodies, are injected locally into the body during standard endoscopy. To minimize background signals and improve the accuracy of the analysis, unbound biomarker-targeted NPs are washed away. A spectrally imaging endoscope then emits laser light that induces Raman scattering, which is enhanced by the plasmonic effect on the metal surface of the NPs and produces spectral data. This data is used to determine the location and type of pathological cells based on the unique spectral fingerprint characteristic of bound SERS nanotags. (109,709)
The uniqueness of the spectral data obtained allows SERS endoscopy to be used for the most important steps in the diagnosis and treatment of cancer. Due to the high sensitivity and specificity of the method, several studies have demonstrated the ability of SERS endoscopy in identifying and classifying heterogeneous esophageal (710) and bladder (Figure 36a–c) (711) cancers by detecting biomarkers of carcinogenesis expressed on the cell surface. In addition, other studies have demonstrated the ability of enhanced SERS/SERRS signals in surgical navigation to delineate the boundaries of the pathologic focus (cancerous and precancerous tissue changes) and its complete resection in animal (709) and phantom (712) models. Early diagnosis and complete resection of the tumor are the most important factors affecting the patient’s survival and quality of life after treatment.

Figure 36

Figure 36. SERS imaging and endoscopic imaging of tumors. (a) Photograph and distribution of SERS nanotags related with s420-CA9, s421-IgG4, and s440-CD47 in bladder tumor. (b) Stem plot to illustrate tumor and normal tissue. (c) Binding of s421-IgG4 in normal and tumor tissue samples. Reproduced with permission from ref (711). (d) The estimation of the pH value employing a SERS-based endoscope equipped with a pH sensitive Raman reporter molecule in HeLa cells treated with cisplatin. (e) Variation of fluorescence intensity in HeLa cells treated with cisplatin and (f) the related fluorescence images. The incubation time with cisplatin was 0, 1, 3, 9, and 24 h. The scale bar in (f) represents 20 μm. Reproduced with permission from ref (713).

In addition, advanced technologies, such as confocal laser microendoscopy (CLE), allow for highly magnified cross-sectional images of the gastrointestinal epithelium and histologic diagnosis without biopsies. The principle of CLE is similar to SERS endoscopy, however, due to a focusing lens at the end of the optical fiber and a confocal aperture, the device registers the signal from the focus point in the tissue and makes possible the reconstruction of high quality microscopic images of the tissue. (709)
As promising tool in intracellular SERS endoscopy, Ag nanoneedles with atomically smooth surface properties having a length of several micrometers were developed as plasmonic waveguide structure to effectively compress the propagating plasmon modes at the sharp edges of these needles. (714) These nanofocusing capabilities are of high interest to record SERS spectra from a small volume compartment within the submicrometer range. As an alternative to Ag-based needle-like architectures, which are prone to oxidation processes, accompanied by a loss of their plasmonic field enhancement properties, Au NPs are applied to decorate nanofibers with a potential application in SERS-based cell endoscopic investigations. (715) Nanopipettes are modified with a block copolymer template to allow for tune the density of the attached Au NPs and their distance, i.e. gap size, for an efficient enhancement of the Raman signal from small cell compartments. The SERS activity of these Au NP-modified glass nanofibers could be even improved by a subsequent in situ growth of asymmetric Au NPs showing their potential in H2O2 and pH sensing. (716) In terms of cancer diagnosis, the change of the pH value of the intracellular environment could be used as an early predictor on single cell level. As an alternative, the density of hotspots along the SERS-active endoscopic probe was improved by the formation of Au nanostructures on smooth Ag nanowires. (717) Thus, by focusing the excitation laser on the Au-coated nanowires, SERS spectra of dyes labeling cell compartments were recorded within the nucleus and on the cell membrane in HeLa cells, with a high resolution and site specificity. Finally, Ag nanowires with a length of approximately 100 μm were fabricated being flexible and thin enough to penetrate through several cells and their membranes. (718) By doing so, SERS spectra could be recorded in 2D and 3D cell cultures and the SERS-active cell endoscope applied for investigating of cell compartments. By applying machine learning, distinguishing nucleic acid-rich and protein-rich regions was demonstrated. To sum up, the development of metallic nanowires or comparable SERS approaches for cell endoscopy illustrates their potential in the investigation of cell compartments, identifying molecular interactions in cells as well as dynamics with cells or cell cultures.
As an application scenario of SERS endoscopic approaches in cell investigations, the pH value was measured intracellular to investigate cytosolic and nuclear pH variations with high resolution and sensitivity as the change in pH value is important to shed light on physiological and pathological conditions. (713) Here, Au coated Ag nanowires were modified with 4-mercaptobenzoic acid, a pH sensitive Raman reporter molecule. The SERS-active endoscopic probe was incorporated into the nucleus as well as cytosol of HeLa cells and the variations in pH value were monitored in the presence of a hypoxia mimetic agent and an anticancer drug. In Figure 36d-f, the variations in pH value over time is illustrated for anticancer drug, i.e. cisplatin, treated HeLa cells. The advantage of endoscopic probes over classical SERS nanotags applied in intracellular pH sensing is associated with the limitations in applications of NPs which are trapped in endolysosomes. Thus, only endoscopic SERS approaches allow for site-specific investigations within different cell compartments and might be translated to other biological systems in future work. Moreover, a similar SERS endoscopic approach is applied to investigate the intracellular dynamics and distribution of drugs, using the example of doxorubicin, a chemotherapeutic drug, and its distribution in A549 cancer cells. (719) By penetrating Au-coated Ag nanowires into cell compartments, the localization of doxorubicin in the cell nucleus, the complexation of the drug molecule with other components and finally, the intercalation of doxorubicin with DNA was investigated. To be molecular-selective within the intracellular SERS measurements, Ag nanowires applied as endoscopic probe were coated with a metal–organic framework to selectively enrich the metabolite SN-38 of the common anticancer drug irinotecan toward the metallic surface. (720) Here, the conversion of the drug molecule could be detected within the cell for localization and as a function of time. All the presented examples underlie the potential of SERS endoscopic detection schemes in investigations of cell compartments to measure the pH value, drug or metabolite concentration site-specifically and shedding light into variations within cells due to outer influences such as the presence of a medical drug molecule or other agents. The current state of the art of further examples in SERS nanosensing employing endoscopic SERS approaches is beautifully summarized, (721) to which the interested reader is referred, to receive an overview about SERS nanoendoscopy and its application is opto physiological experiments accompanied by machine learning approaches to analyze large data sets. By penetrating SERS-active nanofibers into tissue or organs, the site specific release of biomarker or signaling molecules might be detectable with a high sensitivity, opening a new dimension of SERS-based approaches in medical and biological research. (722)
Advantages and features of SERS (nano)endoscopy has excellent prospects for clinical application, creating competition with already used gold standards diagnostics. We expect that soon SERS (nano)endoscopy may find its place in noninvasive diagnosis of cancer of the urinary, gastrointestinal and respiratory systems, and show excellent results in the navigation and classification of changes in tissues in the nervous system under different pathological conditions. However, further research is needed to optimize and develop advanced fiber optic probes, increase SERS NP sensitivity and expand clinical applications for wider deployment and greater diagnostic accuracy.

8.11. Deep Raman Spectroscopy for In Vivo Detection and Imaging I: SESORS

Noninvasive detection of in vivo lesions, such as tumors, is of great significance in biomedicine and clinic. Due to the strong scattering and absorption of photons in biological tissues, the typical detection of backscattering Raman spectroscopy is limited to hundreds of microns to a few millimeters. (218) Surface enhanced spatially offset Raman spectroscopy (SESORS) (723,724) is a potent technique well suited to biomedical and clinical applications (Figure 37a). The method combines the unique power of SERS to probe low concentration bioanalytes, through its high sensitivity and chemical and biological specificity, with the noninvasive probing capability of spatially offset Raman spectroscopy (SORS). (725) It enables the probing of SERS targets deep inside biological tissues.

Figure 37

Figure 37. Concepts and applications of deep Raman spectroscopy. (a) SESORS concept: SERS signal is recovered noninvasively using SORS concept in which the illumination and collection zones are spatially separated from each other on sample surface. SETRS concept: The configuration separates the excitation laser and the Raman detector on opposite sides of the sample to collect the SERS signals. (b) SETRS detections through 7.1 cm-thick biological tissues. Reproduced from ref (743). (c) SETRS detections through 14 cm thick ex vivo porcine tissues. Reproduced from ref (226). (d) The home-built TRS system with ultrabright SERS nanotags, a fiber-optic Raman probe, a 785 nm laser with a diffuse beam. Adapted from ref (226). (e) Scheme of ratiometric Raman spectroscopy to calculate the depth of SERS in biological tissues. (f) Noninvasive in vivo detection and localization of 6.5 mm deep SLNs in rats using ratiometric TRS. Adapted from ref (747). (g) Tomographic TRS enables the rapid three-dimensional localization of SERS nanotags in thick tissues. Adapted from ref (749). (h) Raman spectral projection tomography system to the imaging and visualization of Raman molecules in phantoms. Reproduced from ref (750).

SORS is a method based on illuminating the sample surface with laser light at a given spatial location and detecting Raman signal at a different, spatially offset position. The separation of the two zones from each other is termed spatial offset (Δs). The larger the spatial offset, the deeper the probed zone: this is due to the statistical nature of photon diffusion in tissue favoring deeper paths to near surface ones as the proximity of surface-air interface leads to a major loss of photons that tend to leak across the boundary. The dependence of probed depth on the spatial offset was studied by Mosca et al. (726) There are multiple illumination and collection geometries one could utilize (727) ranging from a point illumination one to probing on the opposite side of the sample, named as transmission Raman spectroscopy (TRS), which will be elaborated in the next section. Inverse SORS presents another very attractive option for biological and clinical applications. In this configuration, the laser light illuminates the sample in a ring pattern and Raman light is collected from the center of the ring, with the radius of the ring representing the spatial offset. This configuration enables spreading of the light across much wider area compared, for example, to a point illumination. This is highly beneficial in biological applications in vivo where safety laser intensity limits need to be adhered to. The SORS technique is applicable to both sensing native matrix Raman signals as well as SERS signals, known as SESORS.
There are numerous biological applications of SESORS under investigations. For example, the technique is under development for noninvasively detecting neurotransmitters. Here, the detection of clinically relevant levels through the skull have been recently demonstrated ex vivo. (728) SESORS was also applied to detecting subsurface cancer in the brain tissue of mice. (729) This study represented the first use of SESORS in vivo in cancer diagnosis.
Apart from detecting the presence of SERS NPs, one can also sense their local environment. For example SESORS was shown to be capable of monitoring noninvasively local pH levels. (730,731) This is beneficial in cancer diagnosis as the cancer microenvironment typically exhibits lower pH levels to those of healthy tissue, due to the Warburg effect. (732) SESORS can also sense physical properties such as temperature at depth using both the Stokes and anti-Stokes spectral regions. (733) Such thermal reading is not possible using traditional thermometry. This property is particularly attractive in photothermal treatment of cancers where it can provide unique temperature feedback on the temperature of lesion at depth to enable the therapy optimization for highest efficacy while minimizing the collateral damage to healthy tissue. Other SESORS applications include the localization of a cancer lesion, including the detection of bulk tumor, subsequent delineation of tumor margins, and the identification of a deeper secondary region of interest. (734) The lateral position of the lesion can be determined by mapping signals on sample surface and its depth by monitoring spectral changes to the detected SESORS signals. (735−738)
Recently, SESORS was also combined with ultrasound to further boost its sensitivity. (739) This permits lower detection levels to be achievable, opening prospects for detecting diseased conditions at still earlier stages and with less NPs, the latter presenting lower toxicity risks to the patient. In this concept, ultrasound is used to induce an intensity change to SERS signal and SESORS measurement is performed before and after the application of ultrasound yielding, after their subtraction, a higher contrast signature of NPs by effectively eliminating tissue matrix signal and any associated artifacts.
We should note that, although the signal detection is noninvasive, the delivery of SERS NPs to the target zone is, naturally, required. This can be accomplished, for example, by intravenous delivery or by direct injection into the probed site. The issue of potential toxicity of SERS NPs can be also addressed by developing a new class of Au NPs that disintegrate inside body after their use into smaller entities (e.g., sub 5 nm) for safe excretion from body. (740,741) Although the SERS enhancement of the self-disintegrating NPs is still not as high as with traditional SERS, this is anticipated to further improve with ongoing research into these concepts in the coming years.

8.12. Deep Raman Spectroscopy for In Vivo Detection and Imaging II: SETRS

Surface-enhanced transmission Raman spectroscopy (SETRS) is an advanced technique derived from TRS. As previously mentioned, in TRS the excitation laser and the Raman detection zones are on the opposite sides of the sample. Along with SORS, TRS is often classified as a “deep Raman spectroscopy” method. The TRS configuration significantly suppresses background signals from superficial tissues near the excitation site and enhances the detection of deep-embedded targets in scattering media. This approach has been widely utilized in industrial applications, such as pharmaceutical drug monitoring. (742) From a biological standpoint, TRS stands out as a valuable variant of SORS, particularly in cases where the anatomical site of interest is accessible from both sides of the sample and falls within a suitable tissue thickness range. Compared to conventional SORS, (727) TRS offers relatively uniform sensitivity across depths, making it well-suited for screening analytes at unknown depths.
When combined with surface-enhanced Raman scattering (SERS) nanotags, SETRS offers significant advantages in the biomedical detection of deeply embedded lesions. In standard TRS measurements, detection performance depends on several factors, including tissue scattering and absorption properties, as well as the brightness of the SERS nanotags, the depth of the buried lesion, and the overall tissue thickness. Zhang et al. conducted theoretical calculations on Raman photon propagation for in vivo TRS experiments, analyzing the effect of these parameters. (226) They found that the most effective way to increase detection depth and tissue penetration is by using brighter SERS nanotags; In addition, due to the strong scattering and diffusion of laser light within biological tissues, both focused and diffuse beams (with equal power) demonstrate similar detection capabilities. (226) This provides new insights for the photosafe TRS detection, as the diffuse beam with a larger spot size can significantly reduce power density to below the clinical maximum permissible exposure (MPE) limits.
SETRS has been shown to detect SERS NPs in biological tissues at depths of 7 to 14 cm (Figure 37b, c), (226,743) far exceeding the typical tissue thickness (a few millimeters) achieved using conventional backscattering mode. (218,712) These depths are comparable to those achieved by fluorescence molecular tomograph using multiple coupled laser-probe systems. For example, Zhang et al. developed a home-built SETRS system featuring ultrabright SERS nanotags, (744) a micro-SORS fiber-optic Raman probe with deeper light detection ability, (712,745) and a 785 nm laser with a large beam radius of 0.85 cm (yielding a low power density of 0.264 W/cm2, below the MPE of 0.296 W/cm2) (Figure 37d). (226) Using this system, they achieved safe detection of SERS nanotags buried in 14 cm of ex vivo tissue (Figure 37c) and noninvasive in vivo detection of lesion phantoms in live, fur-covered mice with the penetration thickness of 1.5 cm. (226) A more recent study utilized this TRS setup for in vivo detection in a live rat whose total thickness was of 2.6 cm. (197)
In addition to detection, localization of hidden lesions is also crucial. Zhang et al. demonstrated a linear relationship between the natural logarithm of the intensity ratio of two Raman peaks and lesion depth, allowing depth prediction using Raman peak ratios, named as ratiometric TRS (Figure 37e). (746) By leveraging multiple Raman peaks, depth prediction in heterogeneous tissues can be achieved by integrating information from multiple peak pairs, for example, the accurate depth prediction of lesions in 5 cm thick heterogeneous tissues (comprising skin, fat, and muscle) was reported using ratiometric TRS, with a root mean squared error (RMSE) as low as 8.35%. (746) Wu et al. further applied ratiometric TRS to live rat models, using the popliteal sentinel lymph nodes (SLNs) as deep lesions (Figure 37f). (747) They successfully conducted perioperative surgery for SLNs, including preoperative noninvasive detection, intraoperative localization, and postoperative examination. In vivo detection and accurate localization of 6.5 mm deep SLNs in rats were performed using TRS and SERS nanotags under clinically safe laser irradiation (0.21 W/cm2). (747) This study was the first to demonstrate the localization and perioperative surgery guidance of deep lesions in a live animal model using SETRS. Even if in complex in vivo environments containing heterogeneous tissues and blood flow, SETRS exhibits excellent detection depth and localization accuracy, highlighting its potential for clinical applications.
An exciting future direction for SETRS would be the development of 3D imaging or reconstruction of target lesion structures. The ability to image 3D tissues using Raman labels or label-free molecular contrast is highly valuable for biomedical research. This opens the possibility of practical small-animal imaging systems, similar to established techniques like fluorescence molecular tomography or diffuse optical tomography. Raman tomography was previously reported by using multiple laser-receptor pairs in a ring-arranged fiber setting. (748) By using transmission configuration, a two-step TRS tomography was reported to localize SERS nanotags in a two-dimensional plane (Figure 37g, i), enabling rapid three-dimensional localization of one or multiple SERS nanotags in 4.5 cm thick ex vivo tissues (Figure 37g, ii). (749) Stepula et al. constructed a Raman spectral projection tomography system based on transmission geometry, combined with a computational pipeline for multivariate reconstruction to extract label-free spatial molecular information from Raman projection data (Figure 37h, i). (750) They demonstrated the imaging and visualization of Raman molecules in phantoms (Figure 37h, ii), as well as the extracellular matrix heterogeneities in living tissue-engineered constructs. (750) Future advancements in SETRS tomography could integrate topographic surface Raman imaging to improve the localization of SERS NP-targeted lesions or combine Raman imaging with histological analyses. (751)
Overall, the rapid development of deep Raman techniques, including SORS and TRS, is closing the gap between ex vivo demonstrations and in vivo biomedical applications. To facilitate the clinical translation of SETRS, its efficacy and safety should first be validated in preclinical large animal models, and the detection systems are anticipated to be optimized to be more feasible, user-friendly, and accessible for clinical practitioners.

9. Conclusion and Perspective

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For perspective and outlook, there are a number of emerging trends and opportunities. One is to exploit the intrinsically label-free capabilities of SERS to detect and differentiate complex biomedical biomarkers such as nucleic acid, protein and metabolite molecules in blood and tissue biopsies, to be aided by artificial intelligence or single-molecule counting as enabled by digital SERS. Also, there is a great promise in translating biocompatible SERS-encoded nanotags for in vivo tumor imaging and spectroscopy-guided surgery. Here, an urgent need is to develop sensitive and compact devices of noninvasive, rapid and high-resolution spectroscopic measurements at deep tissue penetration.
There are also fundamental or intrinsic challenges in developing SERS for medical applications. First, since a wavelength-resolved Raman spectrum is acquired at each spatial point, SERS is excellent for zoomed-in measurements at specific tissue sites or locations. This limitation can be overcome or mitigated by combining Raman spectroscopy with wide-field imaging modalities (such as CT, MRI, PET, ultrasound, photoacoustics, or fluorescence) that provide anatomical, functional, and molecular information with limited sensitivity or specificity. In this case, the addition of SERS would provide ultrahigh sensitivity and specificity, even at the level of single tumor cells or single molecules. Another fundamental challenge is the long-term fate in vivo and safety of Au NPs, which are largely not biodegradable. Upon rapid uptake by the liver, spleen, and other reticuloendothelial system (RES) organs, these particles are often retained in the body for very long periods of time (months to years). The developments in “excretable” Au nanoclusters look promising, but the associated materials used for surface coating and tagging still need to be systematically evaluated for long-term clinical safety and efficacy.

9.1. Translation and Commercialization of SERS

Compared to in vivo use that is limited by the strict regulations, translation toward SERS POCT application is more promising. This advancement hinges on portable and user-friendly instruments, devices, and detection systems. For example, SERS-based LFAs have shown significant promise in diagnostics, as they offer superior sensitivity compared to traditional colorimetric LFAs. (752,753) Although commercially available LFAs still predominantly rely on colorimetric or fluorescent methods due to challenges in reproducibility, stability, and cost-efficiency that currently limit SERS LFAs, SERS stands out with its multiplexing capability. This enables simultaneous detection of multiple biomarkers on a single test strip. This is particularly advantageous for scenarios such as detecting multiple bacteria, (752) pathogens, (754) or cardiac markers. (755) SERS can also complement fluorescence or colorimetric methods to improve detection accuracy. (756) Also, achieving widespread adoption will require the development of low-cost, miniaturized Raman systems for diverse healthcare environments. Transitioning SERS from laboratory setups to portable hand-held devices is essential for commercialization, especially in resource-limited environments where traditional diagnostics are impractical.
One of the challenges in SERS translation is the reproducibility and standardization of plasmonic substrates, which are critical for consistent SERS signals. Variability in signal enhancement due to differences in substrate quality, sample preparation, or environmental factors undermines clinical reliability. Advances in substrate fabrication, including self-assembled NP arrays, 3D scaffolds, and MOFs, are expected to provide improved uniformity and reproducibility. The digital (nano)colloid-enhanced Raman spectroscopy (as mentioned in Section 7.2) was proposed for the reproducible, quantitative detection of a wide range of target molecules at low concentrations; this shows promise for application to various metabolites related to human health. In a recent study, plasmonic metasurfaces, consisting of alternately stacked metal-dielectric pyramidal meta-atoms, were proposed as a strategy to deal with the trade-off between “reproducibility” and “enhancement”. (757) The nanomechanical perturbations of this metasurface are harnessed and transduced in the form of SERS frequency shifts that are not directly affected by the SERS uncertainty principle, thus enhancing the signal reproducibility.
Another issue is the long-term stability of plasmonic substrates during storage and transport. Spherical substrates are relatively stable, whereas irregularly shaped NPs (e.g., nanostar or nanorod) may undergo shape changes over time, significantly altering their optical properties. Optimization of surface coatings, such as long-chain stabilizers or biomimetic membrane coatings, is expected to better preserve particle morphology and optical properties. In a recent work, the highly dispersible Au nanorod powder was reported using an octadecyltrimethylammonium bromide (C18TAB)-assisted lyophilization method, which preserved the optical and chemical sensing properties of NPs for over 4 months. (758) This work marks an effective strategy to advance the use of nanorods as standard analytical reagents.

9.2. In Vivo Deep Sensing: Spontaneous Raman versus SERS

As reviewed in this article, in vivo deep sensing has made substantial strides in recent years and shows promise as a detection method capable of penetrating human tissue thickness. However, a significant challenge remains to be the safety of SERS nanotags and effective delivery of them to the target lesion site. NPs must be precisely delivered to the desired location within the body. This often involves sophisticated delivery mechanisms, such as systemic injections or localized administration, to ensure the probes reach the target tissues without being prematurely cleared by the immune system. Currently, local injections, such as those used for sentinel lymph node imaging, offer a potential breakthrough, as they limit the systemic circulation of NPs. (263,759) Achieving nontoxic, biodegradable, and easily clearable SERS nanotags without adverse effects remains critical for clinical translation. In a recent study, a series of Raman-active nanodots that exhibit ultrabrightness and compact sizes (∼20 nm) was developed. (760) These particles yield high Raman emission through the molecular stacking inside the polymer NPs, instead of using metal substrates; they are thus promising to offer better biocompatibility than metallic SERS NPs.
In addition, the integration of SORS/TRS with SERS endoscopy is a promising avenue for deep tissue imaging, as endoscopy reduces the tissue thickness through which light must pass, allowing signals to be transmitted more efficiently from inside the body. This approach is particularly well suited for hollow organs such as the gastrointestinal (GI) tract and bronchi. Further research is needed to optimize fiber optic probes and increase the sensitivity of SERS NPs. For example, the use of a miniaturized Raman device inserted into the ex vivo pig bronchoscope channels has demonstrated the detection of SERS nanotags through a bronchial wall (approaching the fourth generation airway) and adjunct lung tissue. (712) In an anticipated SORS/TRS setup, the diffuse laser could illuminate the skin from the outside, and the endoscopic probe could collect the signal from the inside, expanding the detection area with improved detection depth for broader use.
Alternatively, spontaneous Raman spectroscopy detection of intrinsic biomolecules using deep Raman spectroscopy has great potential. Noninvasive blood glucose detection using the SORS configuration has been successfully demonstrated on pig ears. (761) To further enhance the tissue penetration depth of SORS/TRS, the use of tissue clearing agents is expected. Recently, Ou et al. reported an inspiring study that strongly absorbing molecules (such as tartrazine) can achieve optical transparency in living animals because these molecules dissolving in water can change the refractive index of the aqueous medium to match that of high-index tissue components such as lipids. (762) This transparency process is reversible and exhibits excellent biocompatibility. The search for similar optical clearing agents with powerful and prolonged efficacy could significantly advance the in vivo application of SORS/TRS.

9.3. Dual-Modal SERS and Fluorescence

A key innovation that holds significant promise for both research and commercial applications is the development of dual-modal systems that combine SERS with fluorescence imaging. Fluorescence is widely used in biological imaging due to its high sensitivity, fast acquisition times, and compatibility with a range of biomolecules. However, it often suffers from issues such as photobleaching and background autofluorescence, particularly in complex biological samples. By integrating SERS, which offers high specificity and resistance to photobleaching, these dual-modal systems can overcome the limitations inherent in fluorescence. Dual-modal SERS-fluorescence platforms offer complementary information: fluorescence provides rapid localization, while SERS offers molecularly specific identification. (763)
Among various possible benefits of combining fluorescence and SERS, the most appealing is perhaps based on the consideration that fluorescence transduction is the most widely employed in biology and medicine, which could facilitate in the future the translation of SERS to the clinic. By leveraging dual modality fluorescence-SERS sensors, an initial introduction to the medical and biological fields can first be proposed employing fluorescence, to then shift to the use of SERS as well, once the method can be validated by these communities.
The regulatory landscape for SERS technologies remains an ongoing challenge. Dual-modal SERS-fluorescence systems, however, may ease this transition by building on the well-established clinical use of fluorescence imaging. Incorporating SERS as a supplementary modality can be framed as an enhancement to existing diagnostic techniques, potentially shortening the regulatory approval process.

9.4. AI for SERS in Biomedicine

The rapid advancement of AI in image analysis and natural language processing is also revolutionizing the field of SERS. (445,764−766) We believe AI holds tremendous promise in biomedicine, including design of SERS chip, data analysis, and disease diagnosis. AI optimizes processes such as NP functionalization, spectral preprocessing, and sample pretreatment, reducing trial and error and enabling precise molecular analysis even in complex biological matrices. Moreover, the integration of AI with SERS facilitates high-throughput screening of multiple biomarkers, making diagnostics more accurate and responsive. (764)
With the growing amount of SERS data, one of the important directions would be the extraction of key latent features from massive data sets to facilitate the reverse optimization of SERS detection and analysis. The intensity and peak shape of SERS spectra can be influenced by a multitude of known and unknown factors in complex scenarios. (767−769) It is therefore in need to delve deeper into the nonlinear behaviors of SERS by harnessing the power of explainable AI. (770,771) Particularly, since the conventional data-driven AI can approximate but fails to explicitly tell the underlying physics of SERS, the development of mathematical knowledge-driven AI models is beneficial to fill the gap between the interpretable machine learning results and the actual SERS data.
Despite these advancements, it remains demanding in data quality control and universal standardization across instruments, sample types, and institutions. Additionally, ethical issues are increasingly recognized as central to the responsible adoption of AI particularly in clinical settings. Safeguarding patient privacy, mitigating biases, ensuring transparency, and adhering to regulatory frameworks like general data protection regulation (GDPR) are critical. By prioritizing these ethical considerations, AI-SERS technologies are poised to transition from laboratory proof-of-concepts to transformative clinical tools, advancing diagnostics while upholding equity, accountability, and trust.

Author Information

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  • Corresponding Authors
    • Xing Yi Ling - School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 21 Nanyang Link, Singapore 637371Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, International Joint Research Laboratory for Nano Energy Composites, School of Chemical and Material Engineering, Jiangnan University, Wuxi 214122, P. R. ChinaOrcidhttps://orcid.org/0000-0001-5495-6428 Email: [email protected]
    • Jian Ye - Sixth People’s Hospital, School of Medicine & School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, P. R. ChinaOrcidhttps://orcid.org/0000-0002-8101-8362 Email: [email protected]
  • Authors
    • Linley Li Lin - Sixth People’s Hospital, School of Medicine & School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, P. R. ChinaOrcidhttps://orcid.org/0000-0002-8079-5768
    • Ramon Alvarez-Puebla - Departamento de Química Física e Inorganica, Universitat Rovira i Virgili, Tarragona 43007, SpainICREA-Institució Catalana de Recerca i Estudis Avançats, Barcelona 08010, SpainOrcidhttps://orcid.org/0000-0003-4770-5756
    • Luis M. Liz-Marzán - CIC biomaGUNE, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián 20014, SpainIkerbasque, Basque Foundation for Science, University of Santiago de nCompostela, Bilbao 48013, SpainCentro de Investigación Cooperativa en Red, Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Donostia-San Sebastián 20014, SpainCinbio, University of Vigo, Vigo 36310, SpainOrcidhttps://orcid.org/0000-0002-6647-1353
    • Matt Trau - Centre for Personalized Nanomedicine, Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD 4072, AustraliaSchool of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD 4072, AustraliaOrcidhttps://orcid.org/0000-0001-5516-1280
    • Jing Wang - Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou 350117, ChinaOrcidhttps://orcid.org/0000-0001-6080-7998
    • Laura Fabris - Department of Applied Science and Technology, Politecnico di Torino Corso Duca degli Abruzzi 24, 10129 Torino, ItalyOrcidhttps://orcid.org/0000-0002-7089-5175
    • Xiang Wang - State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Department of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, ChinaOrcidhttps://orcid.org/0000-0003-1937-0725
    • Guokun Liu - State Key Laboratory of Marine Environmental Science, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry and Toxicology, College of the Environment and Ecology, Xiamen University, Xiamen 361005, ChinaOrcidhttps://orcid.org/0000-0003-2501-7178
    • Shuping Xu - State Key Laboratory of Supramolecular Structure and Materials, College of Chemistry, Jilin University, Changchun 130012, PR ChinaOrcidhttps://orcid.org/0000-0002-6216-6175
    • Xiao Xia Han - State Key Laboratory of Supramolecular Structure and Materials, College of Chemistry, Jilin University, Changchun 130012, PR ChinaOrcidhttps://orcid.org/0000-0002-0544-4351
    • Liangbao Yang - Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui 230031, P. R. ChinaDepartment of Pharmacy, Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, Anhui 230031, P. R. ChinaOrcidhttps://orcid.org/0000-0002-6559-6947
    • Aiguo Shen - School of Bioengineering and Health, Wuhan Textile University, Wuhan 430200, P. R. China
    • Shikuan Yang - School of Materials Science and Engineering, Zhejiang University, Hangzhou 310027, P. R. ChinaOrcidhttps://orcid.org/0000-0001-6662-3057
    • Yikai Xu - Key Laboratory for Advanced Materials and Feringa Nobel Prize Scientist Joint Research Center, Frontiers Science Center for Materiobiology and Dynamic Chemistry, School of Chemistry and Molecular Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, P. R. ChinaOrcidhttps://orcid.org/0000-0003-3881-8871
    • Chunchun Li - School of Materials Science and Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, P. R. ChinaOrcidhttps://orcid.org/0000-0001-9501-0313
    • Jinqing Huang - Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong 999077, China
    • Shao-Chuang Liu - Molecular Sensing and Imaging Center, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, P. R. ChinaOrcidhttps://orcid.org/0000-0002-1010-2321
    • Jian-An Huang - Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Aapistie 5 A, 90220 Oulu, FinlandResearch Unit of Disease Networks, Faculty of Biochemistry and Molecular Medicine, University of Oulu, Aapistie 5 A, 90220 Oulu, FinlandBiocenter Oulu, University of Oulu, Aapistie 5 A, 90220 Oulu, FinlandOrcidhttps://orcid.org/0000-0002-6564-5972
    • Indrajit Srivastava - Department of Mechanical Engineering, Texas Tech University, Lubbock, Texas 79409, United StatesTexas Center for Comparative Cancer Research (TC3R), Amarillo, Texas 79106, United StatesOrcidhttps://orcid.org/0000-0002-6864-0202
    • Ming Li - School of Materials Science and Engineering, Central South University, Changsha, Hunan 410083, ChinaOrcidhttps://orcid.org/0000-0002-2289-0222
    • Limei Tian - Department of Biomedical Engineering, and Center for Remote Health Technologies and Systems Texas A&M University, College Station, Texas 77843, United StatesOrcidhttps://orcid.org/0000-0002-1931-8567
    • Lam Bang Thanh Nguyen - School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 21 Nanyang Link, Singapore 637371
    • Xinyuan Bi - Sixth People’s Hospital, School of Medicine & School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, P. R. China
    • Dana Cialla-May - Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745 Jena, GermanyInstitute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743 Jena, Germany
    • Pavel Matousek - Central Laser Facility, Research Complex at Harwell, STFC Rutherford Appleton Laboratory, UKRI, Harwell Campus, Oxfordshire OX11 0QX, United KingdomDepartment of Physics and Astronomy, University of Exeter, Exeter EX4 4QL, United KingdomOrcidhttps://orcid.org/0000-0003-0912-5339
    • Nicholas Stone - Department of Physics and Astronomy, University of Exeter, Exeter EX4 4QL, United KingdomOrcidhttps://orcid.org/0000-0001-5603-3731
    • Randy P. Carney - Department of Biomedical Engineering, University of California, Davis, California 95616, United StatesOrcidhttps://orcid.org/0000-0001-8193-1664
    • Wei Ji - College of Chemistry, Chemical Engineering and Resource Utilization, Northeast Forestry University, Harbin 145040, ChinaOrcidhttps://orcid.org/0000-0001-6391-9768
    • Wei Song - State Key Laboratory of Supramolecular Structure and Materials, College of Chemistry, Jilin University, Changchun 130012, PR ChinaOrcidhttps://orcid.org/0000-0001-9814-419X
    • Zhou Chen - Sixth People’s Hospital, School of Medicine & School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, P. R. ChinaOrcidhttps://orcid.org/0000-0002-3018-8169
    • In Yee Phang - Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, International Joint Research Laboratory for Nano Energy Composites, School of Chemical and Material Engineering, Jiangnan University, Wuxi 214122, P. R. China
    • Malou Henriksen-Lacey - CIC biomaGUNE, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián 20014, SpainCentro de Investigación Cooperativa en Red, Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Donostia-San Sebastián 20014, SpainOrcidhttps://orcid.org/0000-0003-3544-5846
    • Haoran Chen - Sixth People’s Hospital, School of Medicine & School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, P. R. China
    • Zongyu Wu - Sixth People’s Hospital, School of Medicine & School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, P. R. China
    • Heng Guo - Department of Biomedical Engineering, and Center for Remote Health Technologies and Systems Texas A&M University, College Station, Texas 77843, United States
    • Hao Ma - State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Department of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, ChinaOrcidhttps://orcid.org/0000-0001-8341-4003
    • Gennadii Ustinov - Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745 Jena, GermanyInstitute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743 Jena, Germany
    • Siheng Luo - State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Department of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
    • Sara Mosca - Central Laser Facility, Research Complex at Harwell, STFC Rutherford Appleton Laboratory, UKRI, Harwell Campus, Oxfordshire OX11 0QX, United KingdomOrcidhttps://orcid.org/0000-0001-9479-5614
    • Benjamin Gardner - Department of Physics and Astronomy, University of Exeter, Exeter EX4 4QL, United KingdomOrcidhttps://orcid.org/0000-0002-7223-9585
    • Yi-Tao Long - Molecular Sensing and Imaging Center, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, P. R. ChinaOrcidhttps://orcid.org/0000-0003-2571-7457
    • Juergen Popp - Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745 Jena, GermanyInstitute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743 Jena, GermanyOrcidhttps://orcid.org/0000-0003-4257-593X
    • Bin Ren - State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Department of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, ChinaOrcidhttps://orcid.org/0000-0002-9821-5864
    • Shuming Nie - Department of Bioengineering, University of Illinois at Urbana−Champaign, 1406 W. Green Street, Urbana, Illinois 61801, United StatesOrcidhttps://orcid.org/0000-0002-7328-1144
    • Bing Zhao - State Key Laboratory of Supramolecular Structure and Materials, College of Chemistry, Jilin University, Changchun 130012, PR ChinaOrcidhttps://orcid.org/0000-0002-0044-9743
  • Notes
    The authors declare no competing financial interest.

Acknowledgments

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We thank Qing He, Yaxuan Lu, and Han Shen for their help in preparing and organizing the manuscript. The authors are thankful for the following grants. Jian Ye, Linley Li Lin, and Xinyuan Bi: National Key Research and Development Program of China (No. 2024YFF1502600), National Natural Science Foundation of China (Nos. 82272054, 82372016, 623B2070), Science and Technology Commission of Shanghai Municipality (No. 24DIPA00300), Shanghai Jiao Tong University (Nos. YG2024LC09, YG2024QNA15). Aiguo Shen: National Natural Science Foundation of China (Nos. 22074109, 2247040793). Ramon Alvarez-Puebla: Projects PID2020-120306RB-I00 and PID2020-113704RB-I00 (funded by MCIN/AEI/10.13039/501100011033), PDC2021-121787-I00 (funded by MCIN/AEI/10.13039/501100011033 and European Union Next Generation EU/PRTR), 2017SGR883 (funded by Generalitat de Cataluña), and 2021PFR-URV-B2-02 (funded by Universitat Rovira i Virgili). Xing Yi Ling: Singapore National Research Foundation Investigatorship (NRF-NRFI08-2022-0011), Competitive Research Programme (NRF-CRP26-2021-0002). Limei Tian: Funding from the National Science Foundation (No. 1648451) and the National Institutes of Health (No. R35 GM147568). Luis M. Liz-Marzán: European Research Council (ERC Advanced Grant 787510, 4DbioSERS). Chunchun Li: Shanghai Pujiang Program (No. 23PJ1409000). Shuping Xu: National Natural Science Foundation of China (No. 22373041), Science and Technology Development Program Projects of Jilin Province (No. 20220101046JC). Jian-An Huang: Academy Research Fellow project, TwoPoreProSeq (No. 347652), and DigiHealth project (No. 326291), a strategic profiling project at the University of Oulu that is supported by the Academy of Finland and the University of Oulu. Xiao Xia Han and Bing Zhao: National Natural Science Foundation of China (Nos. 22073014, 22074051). Yikai Xu: Fundamental Research Funds for the Central Universities (No. JKJ01231812). Jinqing Huang: Research Grant Council of Hong Kong (No. 16309721). Yitao Long: National Natural Science Foundation of China (No. 2220407). Matt Trau: ARC Australian Laureate Fellowship (No. FL220100059). Jing Wang: National Natural Science Foundation of China (No. 12074069). Liangbao Yang: National Natural Science Foundation of China (No. 22474143). Guokun Liu: National Natural Science Foundation of China (No. 22272139). Pavel Matousek: “RaNT: Raman Nanotheranostics” EPSRC Programme Grant (EP/R020965/1) and EP/P012442/1. Randy P. Carney: National Institutes of Health (NIH) R01CA241666. Jurgen Popp: The Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) (No. 465289819).

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  • Abstract

    Figure 1

    Figure 1. Schematic illustration of the direct and indirect adsorption.

    Figure 2

    Figure 2. Examples of solution-phase NP-based application. (a) Digital SERS analysis of paraquat in lake water and thiram in bean sprouts extract by using chloride-capped Ag NPs as the enhancing substrates. Reproduced from ref (16). Copyright 2024, The Authors, under exclusive license to Springer Nature Limited. (b) SERS spectra of CV, doxorubicin (DOX), niraparib (NI) obtained on pristine (i), hedroxyethyl-cellulose stabilized (ii), and PVP-capped Au nanostars. Reproduced from ref (17). Copyright 2021, The Authors. (c) SERS spectra of negatively charged dye labeled DNA using Ag NPs modified by spermine (blue), spermidine (red), and ethylenedioxy-diethylamine (black) as the enhancing substrates, respectively. Reproduced from ref (18). Copyright 2012, Royal Society of Chemistry. (d) SERS spectra of MDMA detected by Ag NPs modified with different feedstock proportion of mixed thiols, as well as the normal Raman spectrum of MDMA pow der. Reproduced from ref (19). Copyright 2012, Royal Society of Chemistry. (e) SERS analysis of methanol by using CB modified Au NPs as the enhancing substrates. Reproduced from ref (20). Copyright 2017, Royal Society of Chemistry.

    Figure 3

    Figure 3. Methods and application of self-assembled NPs. (a–d) Schematic illustrations of the different methods for overcoming interparticle electrostatic repulsion in interfacial NP self-assembly. Panels a–d were adapted with permission from ref (46). Copyright 2021, The Royal Society of Chemistry. (e) Schematic illustrations of interfacial plasmonic arrays used as the enhancing substrate for SERS detection of norepinephrine in rat brain microdialysates. Reproduced with permission from ref (67). Copyright 2022, Wiley-VCH-GmbH. (f) Schematic illustration of in situ biphasic SERS performed using interfacial arrays as the enhancing substrate. (g) SERS spectra of (i) Au interfacial arrays, (ii–iii) Au interfacial arrays containing 10–3 and 10–5 M naphthalene introduced from the oil phase, (iv–vii) Au interfacial array containing 10–5 M naphthalene introduced from water, hexane, or chloroform phase of the interfacial array, respectively. Panels f–g were adapted with permission fromref (21). Copyright 2022, The Authors. (h) Examples of plasmonic interfacial arrays deposited on various supporting materials. The scalebars corresponds to 0.5 cm. Adapted with permissions from ref (68). Copyright 2021, The Authors. (I) Schematic illustration and photograph showing bacterial headspace probed in situ via SERS using interfacial arrays deposited on quartz as the enhancing substrate. Headspace SERS spectra of the time dependent adsorption from broth spiked with 10–2 M dimethyl disulfide. Adapted with permissions from ref (69). Copyright 2028, Wiley-VCH-GmbH.

    Figure 4

    Figure 4. Dynamic SERS method. (a) Dark-field optical microscopy for real-time tracing of the morphological alterations of a specific aggregate in the evaporation process of a 1 mL sample. This sample of Ag sols was 10-fold diluted with 0.01 mM citrate solution to avoid overly strong light scattering. Reproduce from ref (88). Long-period and high-stability 3D hotspots for the detection of different molecules. SERS spectra obtained at different concentrations of (b1) NOD and (c1) TBZ in 5% glycerol system. Time-dependent Raman spectra of (b2) NOD and (c2) TBZ based on the long period and high-stability 3D hotspot matrix constructed with the assistance of 5% glycerol. (88,89) A liquid-phase SERS method. (d) Reversible O/W encasing for self-assembly of metal liquid-like Au nanorod arrays is realized in a common cuvette. (e) SERS spectra of TBZ with concentrations of 1, 101, 102, 103, and 104 ppm, respectively. (f) A linear plot of r780/662 against logarithmic concentration of pesticide. Reproduce from ref (14). Capillary capturing target molecules into small gaps method. Schematic diagram of the assembled NP film (g1–i1) and high-speed camera picture (g2–i2) from the initial wet stage to final dry stage of the monolayer NP film. (j) SERS spectra collected from a single A549 cell during the cell death process caused by photothermal stimulation. (k) Schematic diagram of the capillary SERS method for in situ monitoring of the single-cell death process under photothermal stimulation. Reproduced from ref (93).

    Figure 5

    Figure 5. Scheme of enrichment process of dye molecules. (a) Schematic of the multianalyte and multiphase enrichment using the acoustic levitating platform and the consequent multiplex and multiphase SERS detection. (b) Enrichment process of dye molecules from 10 μL of ethanol solutions. Reproduced from ref (90). Copyright 2022, The Authors.

    Figure 6

    Figure 6. Raman imaging of SERS hydrogels. (a) 2D imaging of the SERS intensity at 1083 cm–1 in hydrogels with different Au nanorod concentrations: (i) 0.1 mM, (ii) 0.5 mM, (iii) 1 mM, and (iv) 1.5 mM, respectively. Adapted with permission from ref (105). Copyright 2020, John Wiley and Sons. (b) Comparison between the CFM imaging and high-spatial-resolution SERS imaging of the hydrogel scaffold supporting SERS nanotag-labeled tumoroids. Adapted with permission from ref (106). Copyright 2024, The Authors.

    Figure 7

    Figure 7. Fabrication of MOFs. (a) TEM images of Au spherical particles coated with a homogeneous silica shell. Of 98.5 ± 7.0 nm. Adapted with permission from ref (119). Copyright 2009, American Chemical Society. (b) TEM images of a single Au nanostar mesoporous silica coated yolk shell. Adapted with permission from ref (122). Copyright 2019, Royal Society of Chemistry. (c) SEM and TEM/XED analysis of mesoporous silica shells containing Au island films in their interior. Adapted with permission from ref (123). Copyright 2019, John Wiley and Sons. (d) SEM image of core–shell Au nanostars coated with MOF (ZIF-8). Adapted with permission from ref (133). Copyright 2019, John Wiley and Sons. (e) TEM image of yolk shell Au nanostars coated with MOF (ZIF-8) after dissolution of the inner ZIF-67. Adapted with permission from ref (128). Copyright 2023, John Wiley and Sons. (f) STEM images (bright and dark field) for PS@Ag, Ag@ZIF-thin, and thick plasmonic-MOFs. Adapted with permission from ref (135). Copyright 2024, John Wiley and Sons.

    Figure 8

    Figure 8. Applications of SERS MOFs. (a) (i) SERS spectra of different concentrations of bathocuproine adsorbed on PS@Ag@ZIF-8. (ii) SERS spectrum of the sediments obtained after several washing cycles. (iii) Comparison between the SERS intensities of bathocuproine (band at 1377 cm–1) on PS@Ag@ZIF-8 and PS@Ag after the same washing samples. (iv) SERS spectra of bathocuproine on PS@Ag@ZIF-8 upon immersion into Cu(II) solutions in PBS buffer at different copper concentrations (from 38 to 254 ppb). Adapted with permission from ref (134). Copyright 2023, American Chemical Society. (b) (i) SERS spectral kinetics acquired on a nanostar coated with MOF and loaded with a nucleus dye (Hoechst 33258, HOE) with and without NIR illumination (785 nm); solid and dashed lines represent the time points 0 and 8 h, respectively. Kinetics was built with the SERS intensity of the ring breathing mode of HOE (980 cm–1). (ii) White light image (top) and SERS images of a single cell with the MOF composite time 0 (middle) and after 8 h of irradiation (down). (iii) Confocal microscopy image of cells incubated with core–shell containing HOE before and after NIR treatment. Blue and orange colors represent HOE and cell membrane staining (CellMask Deep Red), respectively. Adapted with permission from ref (133). Copyright 2019, John Wiley and Sons. (c) Photocatalytical degradation curves of rhodamine B under green illumination. Intensity corresponds to band 1202 cm–1. SERS spectra are presented as a 3D graph to guide the diminishing of the intensity across the full spectrum. Adapted with permission from ref (135). Copyright 2024, John Wiley and Sons.

    Figure 9

    Figure 9. ZnO superstructures and related optical properties. (a) Diagram that illustrates the microstructure of a ZnO superstructure comprised of closely packed nanocrystallites. (b) The comparison of enhancement factor and near-field scattering efficiency observed on ZnO superstructures with different diameters under the 532 nm excitation. Inset shows the corresponding SEM and electric-field distribution images of ZnO superstructures. (c) Potential CT transition pathway and vibronic coupling mechanism. Adapted with permission from ref (168). Copyright 2019, Wiley-VCH Verlag.

    Figure 10

    Figure 10. Typical fabrication process of SERS nanotags: noble metal NPs, Raman reporter molecule adsorption, protective layer coating, and modification layer attachment.

    Figure 11

    Figure 11. Characterization and formation mechanism of GERTs. (a) Geometrical illustration of a GERT. (b) The molecular structures, (c) TEM images, and (d) SERS spectra of GERTs with different organic molecules embedded. Reproduced from ref (194). Copyright 2020, The Authors. (e) GERTs with interior gaps of different thickness. Reproduced from ref (211). Copyright 2018, American Chemical Society. (f) Growth mechanism illustration using molecular dynamics simulation. Reproduced from ref (212). Copyright 2021, American Chemical Society. (g) Different core morphologies of GERTs. (i) Petal-like shell GERTs. Reproduced from ref (203). Copyright 2019, The Authors. (ii) Au bipyramid GERTs. Reproduced from ref (214). Copyright 2024, The Authors. (iii) Au nanotriangle GERTs. Reproduced from ref (215). Copyright 2019, American Chemical Society. (iv) Dual-gap nanodumbbell GERTs. Reproduced from ref (216). Copyright 2023, Wiley. (v) Multishell nanorod GERTs. Reproduced from ref (217). Copyright 2016, Elsevier. (vi) Au petal-like core with Ag shell GERTs. Reproduced from ref (218). Copyright 2023, The Authors.

    Figure 12

    Figure 12. Design and application of orthogonal Raman molecules. (a) Three-color SERS imaging of HeLa cells using SERS nanotags of an alkyne SERS palette; the SERS nanotags were modified with OPE0 (red), OPE1 (green), and OPE2 (blue). Reproduced from ref (239). (b) (i) “Click” SERS for 10-plex synchronous biomarkers detection. Reproduce from ref (240). (ii) “Mixing” SERS for multiplex detection of liver cancer antigens. Reproduce from ref (241). (iii) “Combined” SERS emissions for high-throughput optical labels on microscale objects, such as bacterium. Reproduce from ref (242).

    Figure 13

    Figure 13. Representative NIR-II SERS nanotags. (a) Quantitative enhancement factor study of NIR-II resonant nanorods. Reproduced from ref (257). Copy 2022, American Chemical Society. (b) Design and optical properties of Au@Au–Ag dot-in-cubic nanoframes. They show high SERS intensity to penetrate thick phantoms. Reproduced from ref (261). Copyright 2021, Wiley. (c) NIR-II SERS nanotags made with pc-AuAg NSs and its application for in vivo SERS imaging of tumors. (i) Schematic illustration of the synthetic process of pc-AuAg NSs. (ii) SEM image of Ag and Au of pc-AuAg NSs. (iii) Schematic illustration of the application of NIR-II SERS nanotags for in vivo imaging of tumors in a 4T1 tumor-bearing mouse. Reproduced from ref (262). Copyright 2022, The Authors. (d) The core–shell nanotags with IR-1064 molecules embedded. (i) Schematic illustration of the synthetic process of the core–shell nanotags. (ii) Absorbance spectra of IR-1064 molecules and core–shell nanotags. (iii) They were applied for sentinel lymph node (SLN) detection. Reproduced from ref (263). Copyright 2024, Elsevier.

    Figure 14

    Figure 14. RBCm coated SERS NPs. (a) Schematic diagram showing preparation of RBCm vesicles and successful coating on anisotropic Au NPs, leading to RBCM-coated AuNP-RBCm with enhanced dispersibility and biofunctionalization properties. Physicochemical characterization of anisotropic RBCM-coated AuNPs. Negative-stained transmission electron micrographs of (b) bare AuNP1, (c) RBCm-coated AuNP1, (e) bare AuNP2, (f) RBCm-coated AuNP2, (h) bare AuNP3, and (i) RBCm-coated AuNP3. The membrane coating in (c, f, i) are highlighted with white lines. (d, g, j) UV–vis absorbance spectra of AuNPs and AuNP-RBCMs, with the inset boxes zoomed. The inset shows the intrinsic plasmonic absorbance peak shift for different anisotropic AuNPs upon RBCM coating. Reproduced from ref (280). Copyright 2022, American Chemical Society.

    Figure 15

    Figure 15. Illustration of optical tweezers-coupled SERS platforms. (a) SERS-active satellite transport by laser tweezers to the surface of a cell. (b) Raman tweezing. (c) Autoenhanced Raman spectroscopy and time-dependent Raman signals after laser exposure. (d) Optical tweezers-coupled Raman spectroscopic platform. SERS spectra and intensity as a function of the distance between two Ag NP-coated beads from beads approaching to beads separating. (e) Optical plasmonic tweezer-coupled SERS platform. SERS spectra recorded at a Ag NP-coated bead dimer with the switching of trapping laser between on (red) and off (black) states. Adapted with permission from refs (284−288). Copyrights 2015, Royal Society of Chemistry; 2011, American Chemical Society; 2016, American Chemical Society; 2021, The Author(s); and 2023, The Authors, respectively.

    Figure 16

    Figure 16. Plasmonic nanopores and related applications. (a) Plasmonic nanopore-based single molecule electrical and optical measurement. Reproduced from ref (309). (b) Integrated system for the optical and electrical detection of nanopore analysis. Reproduced from ref (309). (c) In situ electrochemical modification of metallic nanopore. Reproduced from ref (309). (d) Schematic of a solid-state nanopore with an integrated bowtie-antenna structure for DNA translocation experiments. Reproduced from refs (310and311). (e) Schematic of the bowl-shaped plasmonic nanopore system for Raman detection of single DNA molecules. Reproduced from ref (316). (f) Schematic diagram for the dynamic SM-SERS detection by plasmonic nanopipettes. Reproduced from refs (318and319).

    Figure 17

    Figure 17. Plasmonic nanopore for biomolecule sequencing. (a) Unfolding a protein for plasmonic nanopore sequencing. (b) The schematic of the plasmonic bowl-shaped nanopore system in which the DNA (green line) would be first uncoiled by the hydrogel (blue circles) and then passed through the hot spot. (c) The cross-sectional distribution of the surface charge of the bowl-shaped nanopore under electric bias. (d) The simulated EO flow distribution in the bowl-shaped nanopore and path of the DNA movement under electric bias (white line). (e) Schematic of the particle-in-pore sensor that could trap single Au NP for tens of seconds. (f) The molecule would be adsorbed on the NP before trapping. (g) The generated gap-mode hot spot with size of 1–3 amino acids. (h) The time series of collected single-molecule SERS spectra of the vasopressin peptide in the electro-plasmonic nanopore. (i, j, k) Single-molecule SERS spectra at different times from (h) that represent the neighboring amino acids. Panels (b–d) are adapted with permission from ref (316). Copyright 2023, American Chemical Society, and panels (e–k) are adapted with permission from ref (283). Copyright 2022, American Chemical Society.

    Figure 18

    Figure 18. Wearable SERS-based sensing devices. SEM images of (a) Ag nanomushroom array, (b) Au nanosphere cone array, (c) Au-coated electrospun thermoplastic polyurethane fibers, and (d) Au nanomesh. (e) TEM image of Ag nanocube superlattice. Scale bar: 50 nm. SEM images of (f) plasmonic nanovoids array (scale bars: 1 μm (top) and 200 nm (bottom)), (g) Au nanorods on chromatography paper, and (h) Au nanospheres on alkalized V2C MXene membrane. Inset: optical image of the Au NP-modified MXene membrane. (i) Optical image of a SERS sensor within PDMS microfluidics and (j) corresponding continuous SERS measurements of lactate with varying concentrations. (k) Optical image of SERS sensors distributed along a paper microfluidic channel and (l) continuous SERS measurements of uric acid with varying concentrations. (m) Optical image of a portable Raman spectrometer used in on-body measurements. (n) Optical image of a multiplex sensor array with a sweat extraction system. Scale bar: 1 cm. (a, i, j) Reproduced from ref (341). Open access. (b) Reproduced with permission from ref (335). Copyright ACS. (c) Reproduced with permission from ref (339). Copyright ACS. (d) Reproduced from ref (340). (e, n) Reproduced from ref (337). Open access. (f) Reproduced with permission from ref (342). Copyright Wiley. (g, k, l, m) Reproduced from ref (336). Open access. (h) Reproduced with permission from ref (345). Copyright ACS.

    Figure 19

    Figure 19. Widefield and holography Raman imaging. (a) Schematic widefield microscope extended with “beam homogenization” and “imaging and spectroscopy” modules. The beam homogenization module ensures a flat illumination profile over the entire field of observation to allow quantitative SERS intensity measurements. The imaging and spectroscopy unit simultaneously records an image of the sample plane alongside a spectrally dispersed copy of said image on the same sCMOS camera. This combination ultimately allows extracting single-particle spectra from the camera recordings. Adapted with permission from ref (369). Copyright 2022, John Wiley and Sons. (b) Optical image and single shot SERS imaging of large plasmonic surfaces of 3 mm × 3 mm and the corresponding and SERS spectra taken along the blue lines marked. The bold spectrum represents the mean spectrum. Adapted with permission from ref (368). Copyright 2022, American Chemical Society. (c) Schematic of the spectrally resolved holographic widefield microscope composed of a Michelson and a shearing interferometer enabling simultaneously spectrally resolved imaging and image-phase measurements. The sample is widefield illuminated and the red-shifted SERS signal separated from residual laser light with a dichroic beamsplitter and a long-pass filter. After propagating through a Michelson interferometer, a conjugate image is formed that is relay-imaged onto an sCMOS camera. A 2D 0-π phase grating is placed slightly offset with respect to the conjugate image plane and generates multiple diffraction orders which propagate through the relay imaging system. A hard aperture placed into the Fourier plane isolates the four first diffraction orders which ultimately self-interfere on the camera. The insets highlight the grating-induced change in PSF (left vs right) alongside a schematic of the Fourier filter. (d) Live cell SERS particle tracking. The SERS signal (pink), recorded at a fluence of 1.8 kW/cm2 and an integration time of 250 ms, is superimposed onto a brightfield image, recorded using Koehler illumination with a 470 nm fiber-coupled LED, of the cells. The trajectories of individual SERS particles are color-coded to show the respective z-positions which are obtained by 3D localizing the particles from image stacks generated via numerical propagation. 40 time-points are recorded per minute. (c, d) Adapted with permission from ref (371). Copyright 2020, Springer-Nature.

    Figure 20

    Figure 20. Conventional SERS frequency analysis strategies. (a) Summary of conventional steps for SERS frequency analysis. (b) Schematic illustration of spectral preprocessing strategies. (c) Peak identification and deconvolution to unravel the reaction events at Ag-MP@ZIF by deconvolution of SERS spectra from 1280 to 1400 cm–1. Reprinted and adapted with permission from (c) ref (385). Copyright 2020, American Chemical Society.

    Figure 21

    Figure 21. Standardization and calibration of SERS frequency shift among samples. (a) (i) Scheme illustration of external standard calibration method and (ii) calibration for R6G ranging from 10–7 to 10–16 M using 610 cm–1 intensity. (b) (i) Scheme illustration of internal standard calibration method and (ii) calibration for Cu2+ quantification showing SERS spectra and ratio (I2223/I1378) as Cu2+ concentration ranging from 0 to 18 mM. (c) (i) Scheme illustration of standard addition method and calibration of nitroxoline in urine and SERS spectra upon spiking different concentration of nitroxoline. Reprinted and adapted with permission from (a) ref (406), (b) ref (408), and (b) ref (412). Copyrights 2013, 2016, 2021, American Chemical Society.

    Figure 22

    Figure 22. Unsupervised machine learning for SERS frequency shift analysis. (a) 3D PCA score plot of the ternary mixture (FE: 0.1 μg/mL, HER: 10 μg/mL, KET: 10 μg/mL) and its single components and the corresponding LLI SERS spectra. (b) UMAP plot showing an overlaid density contour plot and pixels reordered in correct spatial correlation and colored by clusters identified in UMAP. (c) Hierarchical clustering analysis of measured MPY SERS, which categorizes the bacteria species on three main levels using ECM surface chemotype-based classification factors, namely, surface charge, overall ECM chemical characteristics, and type and number of interacting functional groups. Reprinted and adapted with permission from (a) ref (424), (b) ref (429), and (c) ref (432). Copyright 2023, American Chemical Society.

    Figure 23

    Figure 23. Supervised machine learning for SERS frequency shift analysis. (a) PLS regression plot of measured against actual mol % L-TRP. The calibration curve is constructed from spectra collected in varying mol % L-TRP mixtures at 10 mM total [TRP] and comparison of measured vs actual mol % L-TRP in different test samples using the NPGB/EC-SERS platform. (b) Support vector machine classification for differentiation between SERS spectra collected from healthy control and prenatal disease. (c) Confusion matrix of averaged classification outcomes across 100 model iterations using a random forest (RF) classifier. The F1 scores are indicated in brackets. Reprinted and adapted with permission from (a) ref (434), (b) ref (437), and (c) ref (432). Copyrights 2018, 2021, 2023, American Chemical Society.

    Figure 24

    Figure 24. Enhancing machine learning model interpretability. (a) Feature importance scores extracted from SHAP analysis for classification among SERS spectra of with high, medium, and low concentration of biomarker 2,3-DHBA. (b) Correlation between PCA score plot and PCA loading plot unravel the spectral changes induced by different types of interactions from different probe molecules. (c) Density function theory (DFT) simulation reveal ring complexation between APDS and SO2 to confirm the identified SERS spectral changes. Reprinted and adapted with permission from (a) ref (457), (b) ref (425), and (c) ref (426). Copyright 2021, American Chemical Society. Copyright 2024, Elsevier. Copyright 2022, Wiley.

    Figure 25

    Figure 25. Development of denoising strategies in Raman spectrum (1D) and Raman imaging (3D), from conventional signal processing algorithms to supervised and self-supervised deep learning ones. For spectral denoising: (a) PEER, a signal processing algorithm integrating peak extraction and retention, (470) (b) a supervised algorithm based on instrumental noise learning, (471) and (c) P2P, a zero-shot self-supervised spectral denoising algorithm. (472) For Raman imaging denoising: (d) a signal processing algorithm integrating Fourier transform denoising with imaging moving average filter, (473) (e) a two-stage supervised algorithm integrating both spectral and imaging processing, (474) and (f) S2S, a zero-shot self-supervised hyperspectral imaging denoising algorithm. (475)

    Figure 26

    Figure 26. Emerging techniques for SERS quantification. (a) Different factors causing SERS intensity fluctuations including different electromagnetic enhancement due to variable distance between the analyte and the substrate surface (i, ii) and variable adsorption site (i, iii), different adsorption orientations (iv), atomic mobilization (v), NP aggregation (vi), and heterogeneous substrate morphology (vii). (b) Signal calibration by the internal standard. The signal intensity of the analytes can be interfered by a lot of uncontrollable factors while the ratio of analyte signal to internal standard signal can correct these variations, realizing more robust quantification. Three typical strategies of internal standards: (c) co-adsorption of the internal standard molecules with the analytes on the SERS substrate and (d) embedment of the internal standard molecules in the core–shell NP and sensing of the analytes on the outer layer via direct adsorption, specific capturing or analyte-induced chemical change of the surface functionalized molecules. (e) Intrinsic internal standards originated from the substrate and the solvent. (f) The concept of digital colloid-enhanced Raman spectroscopy by single-molecule counting. (g) Dual-mode SERS and electrochemical detection of miRNA. Adapted from ref (523).

    Figure 27

    Figure 27. Multiplex SERS detection of nucleic acids. (a) An LFA-SERS sensor combined with CHA for multiplex detection of miRNAs on a single test line. Adapted from ref (575). (b) Magnetic pull-down of target miRNAs for multiplex SERS labeling, forming core–satellite complexes in the presence of target miRNAs. Adapted from ref (576). (c) Identification of nucleic acid targets using acetylene-tagged Hoechst molecules binding to DNA-tethered Au NPs. Adatped from ref (578). (d) Locker probes-assisted RPA combined with proximity-programmed SERS nanotags for the detection of fusion genes. Adapted from ref (582).

    Figure 28

    Figure 28. Prediction and modeling of protein molecular species. (a) Three different fields involved in SERS: Raman spectroscopy, nanosurface science, and nanoplasmonics. The most representative examples for modeling nanoplasmonics (left) and nanosurface science (right) in the very beginning are illustrated. (601,610). (b) Models for protein in different environment: powder, solution, adsorbed protein, and free protein in the hotspots. Adapted from ref (602). (c) MD simulation can be used for screening orientations of peptides or proteins. (d) Quantum and classical electrodynamics indicate the distance-dependent phenomenon for SERS. (e) SERS trajectory in Euler space can be described as different orientations in the hotspots. (f) Schematic diagram of SPARC for calculating SERS spectra of proteins. Adapted from ref (626).

    Figure 29

    Figure 29. SERS immunoassays for liquid biopsy-based protein marker detection. (a) The Au NP array functionalized with IL-6 aptamers for detecting IL-6 through changes in the aptamer’s SERS signal. Adapted from ref (630). (b) A digital single-molecule nanopillar SERS platform for parallel counting of four types of cytokines. Adapted from ref (631). (c) A plasmonic internal standard-embedded LFA-SERS platform for the duplex detection of protein markers. Adapted from ref (636). (d) A multiplex, pump-free SERS microfluidic chip for the duplex detection of protein biomarkers across multiple samples. Adapted from ref (638).

    Figure 30

    Figure 30. SERS analysis of metabolites. (a) SERS analysis of the conversion of tryptophan (Trp) into Kynurenine (Kyn), catalyzed by the IDO-1 enzyme. Reproduced from ref (643). Copyright 2020, John Wiley and Sons. (b) Evaluation of cell death mechanisms by application of SERS and machine learning. Reproduced from ref (646). Copyright 2024, The Authors.

    Figure 31

    Figure 31. SERSome for robust metabolic profiling. (a) The concept of SERSome and the comparison with single spectra and averaged spectra for the detection of complex molecular systems. Reproduced from ref (652). (b) Convolutional neural network for SERSome analysis and SERS-based disease diagnosis. Reproduced from ref (648). (c) SERSome-based biomarker screening and the subsequent biological validation of the biomarker candidates. Reproduced from ref (650).

    Figure 32

    Figure 32. Overview of strategies for selective small metabolites sensing.

    Figure 33

    Figure 33. Analyte manipulation and capturing strategy for selective small metabolite sensing. (a) Schematic illustration of the use of charge and geometry complementarity/mismatch to direct the probe–analogue coupling toward the chondroitin sulfates’ isomerism sites and induce multidentate interactions for formation of analogue-specific complex geometries when 4-mercaptopyridine (MPY) is selected as the multidentate probe, yielding differentiable SERS spectra as evidenced by distinct clusters on PCA score plot. (b) Detection of mycotoxins using SERS competitive immunoassays. (c) SERS-based sensor chip consisting of multiple SERS nanotags for accurate COVID-19 detection. (d) Mechanism of selective physical confinement using MOF, in which molecules with diameter larger than the pore aperture (2-naphthaldehyde) is separated , whereas 4-ethylbenzaldehyde can diffuse into the MOF, thus provide accurate quantification of 4-ethylbenzaldehyde. Reprinted and adapted with permission from (a) ref (662), (b) ref (666), (c) ref (435), and (d) ref (675). Copyright 2018, 2023 Wiley. Copyright 2022, American Chemical Society.

    Figure 34

    Figure 34. Multimodal techniques for selective small metabolites sensing. (a) Multimodal technique using SERS and photoacoustic for accurate in vivo quantification of H2O2. (b) Hyphenated technique using electrochemical-SERS (EC-SERS) to manipulate the analyte (d- and l-tryptophan) orientation and adsorption onto the Au nanoporous bowl (NPGB), showing superiority of EC-SERS, as evidenced by distinct clusters on the PCA score plot, in contrast to the conventional SERS (no Vapplied) which results in overlapping clusters. Reprinted and adapted with permission from (a) ref (677) and (b) ref (434). Copyright 2021, Wiley. Copyright 2021, American Chemical Society.

    Figure 35

    Figure 35. Workflow of microfluidic droplet-SERS platform for single-cell encapsulation and simultaneous detection of three metabolites produced by a single cell. Reproduce with permission from ref (685).

    Figure 36

    Figure 36. SERS imaging and endoscopic imaging of tumors. (a) Photograph and distribution of SERS nanotags related with s420-CA9, s421-IgG4, and s440-CD47 in bladder tumor. (b) Stem plot to illustrate tumor and normal tissue. (c) Binding of s421-IgG4 in normal and tumor tissue samples. Reproduced with permission from ref (711). (d) The estimation of the pH value employing a SERS-based endoscope equipped with a pH sensitive Raman reporter molecule in HeLa cells treated with cisplatin. (e) Variation of fluorescence intensity in HeLa cells treated with cisplatin and (f) the related fluorescence images. The incubation time with cisplatin was 0, 1, 3, 9, and 24 h. The scale bar in (f) represents 20 μm. Reproduced with permission from ref (713).

    Figure 37

    Figure 37. Concepts and applications of deep Raman spectroscopy. (a) SESORS concept: SERS signal is recovered noninvasively using SORS concept in which the illumination and collection zones are spatially separated from each other on sample surface. SETRS concept: The configuration separates the excitation laser and the Raman detector on opposite sides of the sample to collect the SERS signals. (b) SETRS detections through 7.1 cm-thick biological tissues. Reproduced from ref (743). (c) SETRS detections through 14 cm thick ex vivo porcine tissues. Reproduced from ref (226). (d) The home-built TRS system with ultrabright SERS nanotags, a fiber-optic Raman probe, a 785 nm laser with a diffuse beam. Adapted from ref (226). (e) Scheme of ratiometric Raman spectroscopy to calculate the depth of SERS in biological tissues. (f) Noninvasive in vivo detection and localization of 6.5 mm deep SLNs in rats using ratiometric TRS. Adapted from ref (747). (g) Tomographic TRS enables the rapid three-dimensional localization of SERS nanotags in thick tissues. Adapted from ref (749). (h) Raman spectral projection tomography system to the imaging and visualization of Raman molecules in phantoms. Reproduced from ref (750).

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