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General Method for the Identification of Crystal Faces Using Raman Spectroscopy Combined with Machine Learning and Application to the Epitaxial Growth of Acetaminophen

  • Tharanga K. Wijethunga
    Tharanga K. Wijethunga
    Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
  • Jelena Stojaković
    Jelena Stojaković
    Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
  • Michael A. Bellucci
    Michael A. Bellucci
    Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
  • Xingyu Chen
    Xingyu Chen
    Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
    More by Xingyu Chen
  • Allan S. Myerson
    Allan S. Myerson
    Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
  • , and 
  • Bernhardt L. Trout*
    Bernhardt L. Trout
    Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
    *E-mail: [email protected]
Cite this: Langmuir 2018, 34, 33, 9836–9846
Publication Date (Web):July 27, 2018
https://doi.org/10.1021/acs.langmuir.8b01791
Copyright © 2018 American Chemical Society

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    Abstract

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    Crystal morphology is one of the key crystallographic characteristics that governs the macroscopic properties of crystalline materials. The identification of crystal faces, or face indexing, is an important technique that is used to get information regarding a crystal’s morphology. However, it is mainly limited to single crystal X-ray diffraction (SCXRD) and it is often not applicable to products of routine crystallizations becasue it requires high quality single crystals in a narrow size range. To overcome the limitations of the SCXRD method, we have developed a robust and convenient Raman face indexing method based on work by Moriyama et al. This method exploits small but detectable differences in Raman spectra of crystal faces caused by different orientations of the crystallographic axis relative to the direction and polarization of the excitation laser beam. The method requires the compilation of a Raman spectral library for each compound and must be built and validated by SCXRD face indexing. Once the spectral library is available for a compound, the identity of unknown crystal faces (from any crystal that is larger than laser beam) can be inferred by collecting and comparing the Raman spectra to spectra within the library. We have optimized this approach further by developing a machine-learning algorithm that identifies crystal faces by performing a statistical comparison of the spectra in the Raman library and the Raman spectra of the unknown crystal faces. Here, we report the development of the Raman face indexing method and apply it to three different epitaxial systems: Acetaminophen (APAP) grown as an overlayer crystal on d-mannitol (MAN), d-galactose (GAL), and xylitol (XYL) substrates. For each of these epitaxial systems, the crystals were grown under various experimental conditions and have a wide range of sizes and quality. Using the Raman face indexing method, we were able to perform high-throughput indexing of a large number of crystals from different crystallization conditions, which could not be achieved using SCXRD or other analytical techniques.

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    The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.langmuir.8b01791.

    • SCXRD face indexing data, Raman spectral analysis and microscopic images of analyzed crystals (PDF)

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    Cited By

    This article is cited by 3 publications.

    1. Christos Xiouras, Fabio Cameli, Gustavo Lunardon Quilló, Mihail E. Kavousanakis, Dionisios G. Vlachos, Georgios D. Stefanidis. Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization. Chemical Reviews 2022, 122 (15) , 13006-13042. https://doi.org/10.1021/acs.chemrev.2c00141
    2. Ke-Jun Wu, Edmund C.M. Tse, Congxiao Shang, Zhengxiao Guo. Nucleation and growth in solution synthesis of nanostructures – From fundamentals to advanced applications. Progress in Materials Science 2022, 123 , 100821. https://doi.org/10.1016/j.pmatsci.2021.100821
    3. Tharanga K. Wijethunga, Xingyu Chen, Allan S. Myerson, Bernhardt L. Trout. The use of biocompatible crystalline substrates for the heterogeneous nucleation and polymorphic selection of indomethacin. CrystEngComm 2019, 21 (13) , 2193-2202. https://doi.org/10.1039/C8CE01517A

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