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Microcrystal Growth Pathways Investigated with Machine Learning Segmentation and Classification in Scanning Electron Microscopy

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    Microcrystal Growth Pathways Investigated with Machine Learning Segmentation and Classification in Scanning Electron Microscopy
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    • Rachel R. Chan
      Rachel R. Chan
      Department of Chemistry, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208, United States
      International Institute for Nanotechnology, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208, United States
      More by Rachel R. Chan
      Orcidhttps://orcid.org/0000-0001-7034-9513
    • Jacob Pietryga
      Jacob Pietryga
      Department of Chemistry, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208, United States
      International Institute for Nanotechnology, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208, United States
      Department of Materials Science and Engineering, Northwestern University, 2220 Campus Drive, Evanston, Illinois 60208, United States
      More by Jacob Pietryga
    • Kaitlin M. Landy
      Kaitlin M. Landy
      Department of Chemistry, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208, United States
      International Institute for Nanotechnology, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208, United States
      More by Kaitlin M. Landy
      Orcidhttps://orcid.org/0000-0002-8275-8469
    • Kyle J. Gibson
      Kyle J. Gibson
      Department of Chemistry, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208, United States
      International Institute for Nanotechnology, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208, United States
      More by Kyle J. Gibson
    • Chad A. Mirkin*
      Chad A. Mirkin
      Department of Chemistry, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208, United States
      International Institute for Nanotechnology, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208, United States
      Department of Materials Science and Engineering, Northwestern University, 2220 Campus Drive, Evanston, Illinois 60208, United States
      *E-mail: [email protected]
      More by Chad A. Mirkin
      Orcidhttps://orcid.org/0000-0002-6634-7627
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    ACS Nano

    Cite this: ACS Nano 2024, 18, 48, 33073–33080
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    https://doi.org/10.1021/acsnano.4c08955
    Published November 19, 2024
    Copyright © 2024 American Chemical Society
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    Abstract

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    Advances in electron microscopy have revolutionized material characterization on the nano- and microscales, providing important insights into local ordering, structure, and size and quality distributions. While shape and size can be rigorously quantified through microscopy, it is often limited to local structure analysis and fails to describe bulk sample quality. Herein, a flexible machine learning (ML) tool is described that can segment and classify faceted crystals in scanning electron microscopy (SEM) micrographs to determine sample quality through the crystal size and product distribution. As a case study, this tool was applied to investigate crystal growth pathways (classical nucleation and growth compared to nonclassical growth) in DNA-mediated nanoparticle assembly through size and product (single crystal, fused crystal, or noncrystal) distribution of samples containing over 13000 colloidal crystal products. Strong DNA bond strengths (controlled by DNA sequence) lead to fast nucleation that exhausts the monomer concentration, resulting in smaller colloidal crystals. Alternatively, increased thermal energy and crystallization time lead to nonclassical crystallization pathways (coalescence) that result in larger colloidal crystals. This tool is useful since experimental conditions can now be deliberately identified to control colloidal crystal size and size distribution, important considerations for researchers interested in designing and synthesizing colloidal crystal metamaterials.

    Copyright © 2024 American Chemical Society

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    Supporting Information

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsnano.4c08955.

    • SEM images (ZIP)

    • Additional discussion on segmentation and classification design and workflow, hyperparameter fitting, feature engineering, model validation, applet structure, agitated and stationary crystallization setups, DNA sequence design, SAXS Williamson-Hall analysis, additional crystal size distributions (PDF)

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    ACS Nano

    Cite this: ACS Nano 2024, 18, 48, 33073–33080
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acsnano.4c08955
    Published November 19, 2024
    Copyright © 2024 American Chemical Society
    Request reuse permissions

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