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

AI Article Synopsis

  • Precise control over the size and shape of nanocrystals is crucial for their use in industries like catalysis, sensing, and energy conversion, but traditional measurement methods often miss important details about individual nanocrystals.
  • This study focuses on CoO nanocrystal synthesis, using deep-learning techniques and high-resolution imaging to explore how different synthetic conditions affect their size and shape evolution at a subnanometer scale.
  • Key findings include the identification of growth regime transitions based on nanocrystal size and the introduction of an "onset radius" concept, which helps link the geometric properties of nanocrystals to their material characteristics, paving the way for better applications.

Article Abstract

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 CoO 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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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11526432PMC
http://dx.doi.org/10.1021/acsnano.4c09312DOI Listing

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