journal article·2024-10-29Open access

Size-Resolved Shape Evolution in Inorganic Nanocrystals Captured via High-Throughput Deep Learning-Driven Statistical Characterization

Min Gee Cho, Katherine Sytwu, Luis Rangel DaCosta, Catherine Groschner, Myoung Hwan Oh, Mary C. Scott

ACS Nano· Volume 18· Issue 43

Abstract

High Resolution Image Download MS PowerPoint Slide Precise size and shape control in nanocrystal synthesis is essential for utilizing nanocrystals in various industrial applications, such as catalysis, sensing, and energy conversion. However, traditional ensemble measurements often overlook the subtle size and shape distributions of individual nanocrystals, hindering the establishment of robust structure–property relationships. In this study, we uncover intricate shape evolutions and growth mechanisms in Co 3 O 4 nanocrystal synthesis at a subnanometer scale, enabled by deep-learning-assisted statistical characterization. By first controlling synthetic parameters such as cobalt precursor concentration and water amount then using high resolution electron microscopy imaging to identify the geometric features of individual nanocrystals, this study provides insights into the interplay between synthesis conditions and the size-dependent shape evolution in colloidal nanocrystals. Utilizing population-wide imaging data encompassing over 441,067 nanocrystals, we analyze their characteristics and elucidate previously unobserved size-resolved shape evolution. This high-throughput statistical analysis is essential for representing the entire population accurately and enables the study of the size dependency of growth regimes in shaping nanocrystals. Our findings provide experimental quantification of the growth regime transition based on the size of the crystals, specifically (i) for faceting and (ii) from thermodynamic to kinetic, as evidenced by transitions from convex to concave polyhedral crystals. Additionally, we introduce the concept of an “onset radius,” which describes the critical size thresholds at which these transitions occur. This discovery has implications beyond achieving nanocrystals with desired morphology; it enables finely tuned correlation between geometry and material properties, advancing the field of colloidal nanocrystal synthesis and its applications.
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Research context

OpenAlex topics: Machine Learning in Materials Science · Quantum Dots Synthesis And Properties · X-ray Diffraction in Crystallography

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

National Center for Electron Microscopy, Molecular Foundry

Lawrence Berkeley National Laboratory

Department of Materials Science and Engineering

University of California Berkeley

Department of Energy Engineering, KENTECH Institute for Environmental and Climate Technology

Korea Institute of Energy Technology

Funding

Oak Ridge Institute for Science and Education · Basic Energy Sciences · National Research Foundation of Korea · Ministry of Trade, Industry and Energy · Korea Institute of Energy Technology Evaluation and Planning · Office of the Director of National Intelligence · Office of Science · U.S. Department of Energy · Natural Sciences and Engineering Research Council of Canada

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