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

IF 15.8 1区 材料科学 Q1 CHEMISTRY, MULTIDISCIPLINARY ACS Nano Pub Date : 2024-10-19 DOI:10.1021/acsnano.4c09312
Min Gee Cho, Katherine Sytwu, Luis Rangel DaCosta, Catherine Groschner, Myoung Hwan Oh, Mary C. Scott
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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 Co3O4 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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通过高通量深度学习驱动的统计表征捕捉无机纳米晶体的尺寸分辨形状演变
要在催化、传感和能量转换等各种工业应用中利用纳米晶体,就必须在纳米晶体合成过程中精确控制尺寸和形状。然而,传统的集合测量往往忽略了单个纳米晶体微妙的尺寸和形状分布,从而阻碍了稳健的结构-性能关系的建立。在本研究中,我们利用深度学习辅助统计表征技术,揭示了亚纳米尺度 Co3O4 纳米晶体合成过程中错综复杂的形状演变和生长机制。通过首先控制钴前驱体浓度和水量等合成参数,然后使用高分辨率电子显微镜成像来识别单个纳米晶体的几何特征,本研究深入揭示了合成条件与胶体纳米晶体尺寸依赖性形状演变之间的相互作用。利用涵盖超过 441,067 个纳米晶体的全群体成像数据,我们分析了它们的特征,并阐明了以前未观察到的尺寸分辨形状演变。这种高通量统计分析对于准确代表整个群体至关重要,并能研究纳米晶体成型过程中生长机制的尺寸依赖性。我们的研究结果提供了基于晶体尺寸的生长机制转变的实验量化,特别是 (i) 面化和 (ii) 从热力学到动力学,从凸多面体晶体到凹多面体晶体的转变就是证明。此外,我们还引入了 "起始半径 "的概念,它描述了发生这些转变的临界尺寸阈值。这一发现的意义不仅在于实现了具有理想形态的纳米晶体,而且还实现了几何形状与材料特性之间的微调关联,推动了胶体纳米晶体合成及其应用领域的发展。
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来源期刊
ACS Nano
ACS Nano 工程技术-材料科学:综合
CiteScore
26.00
自引率
4.10%
发文量
1627
审稿时长
1.7 months
期刊介绍: ACS Nano, published monthly, serves as an international forum for comprehensive articles on nanoscience and nanotechnology research at the intersections of chemistry, biology, materials science, physics, and engineering. The journal fosters communication among scientists in these communities, facilitating collaboration, new research opportunities, and advancements through discoveries. ACS Nano covers synthesis, assembly, characterization, theory, and simulation of nanostructures, nanobiotechnology, nanofabrication, methods and tools for nanoscience and nanotechnology, and self- and directed-assembly. Alongside original research articles, it offers thorough reviews, perspectives on cutting-edge research, and discussions envisioning the future of nanoscience and nanotechnology.
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