Machine learning-assisted large-scale identical-location electron microscopy enables quantifying nanoparticulate electrocatalyst degradation

Abstract Deep mechanistic insight into electrocatalyst stability is essential to design durable, resource-efficient fuel cells. Nanoparticulate electrocatalysts degrade via diverse nanoscale processes, yet particle-to-particle heterogeneity in structure, support interaction, and local microenvironment make true particle-level quantification and understanding impossible with classical approaches. Here we scale up identical-location scanning transmission electron microscopy to track the structural evolution of hundreds of carbon-supported Pt–Co nanoparticles, a prototypical oxygen reduction reaction electrocatalyst. We present a three-step image analysis workflow comprising segmentation, tracking, and degradation-event classification with progressive automation, including machine-learning-assisted segmentation of overlapping particles. By pairing nanoscale resolution and local history with population-level statistics, the pipeline enables unbiased identification and quantification of degradation pathways across statistically meaningful particle sets. We reveal clear particle size- and shape-dependent effects, showing that smaller and irregular nanoparticles are more prone to detachment. Together, these advances provide a data-driven framework for probing electrocatalyst degradation at scale, informing the rational design of next-generation materials.