Cho Min Gee, Sytwu Katherine, Rangel DaCosta Luis, Groschner Catherine, Oh Myoung Hwan, Scott Mary C
National Center for Electron Microscopy, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States.
Department of Materials Science and Engineering, University of California Berkeley, Berkeley, California 94720, United States.
ACS Nano. 2024 Oct 29;18(43):29736-29747. doi: 10.1021/acsnano.4c09312. Epub 2024 Oct 19.
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.
在纳米晶体合成中实现精确的尺寸和形状控制对于在各种工业应用（如催化、传感和能量转换）中利用纳米晶体至关重要。然而，传统的总体测量往往忽略了单个纳米晶体细微的尺寸和形状分布，这阻碍了建立稳固的结构-性能关系。在本研究中，我们通过深度学习辅助的统计表征，揭示了氧化钴纳米晶体合成中亚纳米尺度下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，本研究深入了解了合成条件与胶体纳米晶体中尺寸依赖性形状演变之间的相互作用。利用包含超过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表征整个群体至关重要，并能够研究纳米晶体成型过程中生长模式的尺寸依赖性。我们的研究结果提供了基于晶体尺寸的生长模式转变的实验量化，具体而言，（i）对于刻面，以及（ii）从热力学模式到动力学模式的转变，从凸多面体晶体到凹多面体晶体的转变证明了这一点。此外，我们引入了“起始半径”的概念，它描述了这些转变发生时的临界尺寸阈值。这一发现的意义不仅在于获得具有所需形态的纳米晶体；它还能实现几何形状与材料性能之间的精细调节相关性，推动胶体纳米晶体合成及其应用领域的发展。
