Size-Resolved Shape\nEvolution in Inorganic Nanocrystals\nCaptured via High-Throughput Deep Learning-Driven Statistical Characterization
Precise size and shape control in nanocrystal synthesis\nis essential\nfor utilizing nanocrystals in various industrial applications, such\nas catalysis, sensing, and energy conversion. However, traditional\nensemble measurements often overlook the subtle size and shape distributions\nof individual nanocrystals, hindering the establishment of robust\nstructure–property relationships. In this study, we uncover\nintricate shape evolutions and growth mechanisms in Co<sub>3</sub>O<sub>4</sub> nanocrystal synthesis at a subnanometer scale, enabled\nby deep-learning-assisted statistical characterization. By first controlling\nsynthetic parameters such as cobalt precursor concentration and water\namount then using high resolution electron microscopy imaging to identify\nthe geometric features of individual nanocrystals, this study provides\ninsights into the interplay between synthesis conditions and the size-dependent\nshape evolution in colloidal nanocrystals. Utilizing population-wide\nimaging data encompassing over 441,067 nanocrystals, we analyze their\ncharacteristics and elucidate previously unobserved size-resolved\nshape evolution. This high-throughput statistical analysis is essential\nfor representing the entire population accurately and enables the\nstudy of the size dependency of growth regimes in shaping nanocrystals.\nOur findings provide experimental quantification of the growth regime\ntransition based on the size of the crystals, specifically (i) for\nfaceting and (ii) from thermodynamic to kinetic, as evidenced by transitions\nfrom convex to concave polyhedral crystals. Additionally, we introduce\nthe concept of an “onset radius,” which describes the\ncritical size thresholds at which these transitions occur. This discovery\nhas implications beyond achieving nanocrystals with desired morphology;\nit enables finely tuned correlation between geometry and material\nproperties, advancing the field of colloidal nanocrystal synthesis\nand its applications.
Publication Details
- Journal
- Figshare
- Published
- 2024-10-19
- DOI
- https://doi.org/10.1021/acsnano.4c09312.s001
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00