Size-Resolved Shape Evolution in Inorganic Nanocrystals Captured via High-Throughput Deep Learning-Driven Statistical Characterization
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<sub>3</sub>O<sub>4</sub> 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.
Authors
- Mary Scott (ORCID: https://orcid.org/0000-0002-9543-6725)
- Luis Rangel DaCosta (ORCID: https://orcid.org/0000-0001-8966-0408)
- Min Gee Cho (ORCID: https://orcid.org/0000-0003-4490-7352)
- Katherine Sytwu (ORCID: https://orcid.org/0000-0003-1125-2943)
- Myoung Hwan Oh (ORCID: https://orcid.org/0000-0002-8338-3881)
- Catherine Groschner (ORCID: https://orcid.org/0000-0001-9996-1124)
Institutions
- Lawrence Berkeley National Laboratory (US)
- Korea Institute of Energy Research (KR)
- Institute for Nuclear Science and Technology (VN)
- Foundry (United Kingdom) (GB)
- Molecular Foundry
- University of California, Berkeley (US)
Publication Details
- Journal
- ACS Nano
- Published
- 2024-10-19
- DOI
- https://doi.org/10.1021/acsnano.4c09312
- Citations
- 7
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.82
Funders
- U.S. Department of Energy
- Office of the Director of National Intelligence
- National Energy Research Scientific Computing Center
- National Research Foundation
- Ministry of Trade, Industry and Energy
- National Research Foundation of Korea
- Office of Science
- Oak Ridge Institute for Science and Education
- Intelligence Community Postdoctoral Research Fellowship Program
- Natural Sciences and Engineering Research Council of Canada
- Korea Institute of Energy Technology Evaluation and Planning
- Basic Energy Sciences
- Advanced Scientific Computing Research
- Lawrence Berkeley National Laboratory