Scientific Events

Topological defects—dislocations, grain boundaries, and related features—play an essential role in determining the properties of crystalline materials. When crystallite or functional domain sizes shrink to the nanometer scale, these defects become dominant. To date, however, neither bottom‑up nor top‑down synthesis has provided a reliable means of controlling them. Here, we demonstrate delicate control over shell epitaxy on nanocrystals within thin films, producing three‑dimensionally organized nanocrystallites with uniform grain boundaries and associated defects. In these structures, the resulting 3D‑patterned strain field can be mapped with atomic precision and tuned to introduce targeted dislocations or disclinations. Using multiscale crystallography and spectroscopy, we show that the uniformity and discreteness of these defects provide a clear correlation between local structure and collective electrochemical performance—specifically, catalytic activity in oxygen evolution and reduction reactions. Finally, we outline how this nanocrystallite‑engineering approach is guiding the design of next‑generation functional materials for energy nanotechnology [more]

Understanding the geometry of nanomaterials at the atomic scale provides critical insights into local structural heterogeneities and their impact on functional properties. Since shapes vary from particle to particle, detailed analysis at the single-particle level is essential. In this talk, I will present a high-throughput pipeline that integrates deep learning-based segmentation with quantitative shape analysis of individual nanoparticles from high-resolution transmission electron microscopy (HRTEM) images. First, I will describe the application of convolutional neural networks (CNNs) to segment 727 HRTEM micrographs of cubic Co₃O₄ nanoparticles, enabling the extraction of shape descriptors from 441,067 particles. This automated workflow allows for population-wide statistical characterization, bridging local structural detail with large-scale analysis. Second, I will present a size-resolved shape analysis at subnanometer precision, highlighting a critical threshold, “onset radius”, that marks transitions in particle shape, such as surface faceting and a shift from thermodynamic to kinetic growth regimes. This bottom-up approach illustrates how machine learning and data-driven analysis can reveal previously unquantified trends, offering a generalizable framework for high-throughput materials characterization. [more]