Quantitative Delineation of the Morphological Heterogeneity of Anisotropic Nanostructures via Machine-Learning-Augmented Electron Microscopy

Abstract Precise synthesis and engineering of homogeneous anisotropic nanostructures are essential to realize their shape-dependent physicochemical properties for different applications. However, due to the difficulty in controlling factors influencing nucleation and growth, many reported anisotropic nanostructures exhibit substantial heterogeneity, particularly in their size and shape distributions. Current methods for assessing nanostructure heterogeneity, whether ensemble- or single-particle-based techniques, are limited in the types of readout information, resolution, and throughput. Although machine-learning-assisted microscopy approaches have facilitated nanomaterial heterogeneity characterization to a certain extent, strategies that transform microscopy images into quantitative morphology feature spaces for population-level heterogeneity analysis remain underexplored. Herein, a quantitative approach combining transmission electron microscopy (TEM) imaging with machine learning is demonstrated for delineating the projected morphological heterogeneity of anisotropic nanostructures. Following supervised pixel classification and thresholding, TEM images are converted to probability maps and image masks, from which a series of interpretable projected size- and shape-related morphological descriptors are extracted. Dimensionality reduction and unsupervised clustering are subsequently employed to analyze the acquired morphological feature space and identify putative nanostructure subpopulations based on morphological similarities or differences. Feature scoring is further performed to identify descriptors associated with variations among nanostructure subpopulations while considering correlations among related geometric parameters. This study establishes a machine-learning-augmented framework for quantitative and objective analysis of anisotropic nanostructure heterogeneity and provides a foundation for future integration with synthesis metadata and predictive modeling approaches.