Imaging study demonstrates automated quantification of morphological heterogeneity in anisotropic nanostructures, indicating improved quality control for nanomaterial synthesis.
Key Points
To establish a machine-learning-augmented framework that quantitatively delineates the population-level morphological heterogeneity of anisotropic nanostructures from electron microscopy images.
Applied supervised pixel classification and thresholding to transmission electron microscopy (TEM) micrographs to construct probability maps and image masks.
Extracted interpretable projected size- and shape-related geometric descriptors to construct a high-dimensional morphological feature space.
Performed dimensionality reduction, unsupervised clustering, and correlation-aware feature scoring to identify nanostructure subpopulations and key morphological drivers of variation.
Successfully converted TEM micrographs into segmented probability masks and extracted quantitative morphology feature spaces across nanostructure populations.
Identified putative nanostructure subpopulations based on structural variations using unsupervised clustering of projected shape and size descriptors.
Isolated specific geometric descriptors driving subpopulation differences while accounting for inter-parameter correlations.