Large-scale identical-location electron microscopy enables quantifying nanoparticulate electrocatalyst degradation

Fuel cells convert chemical energy into electricity, and their performance relies on electrocatalysts whose durability during operation remains poorly understood. 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, which repeatedly images the very same nanoparticles, to track the structural evolution of hundreds of carbon-supported platinum-cobalt 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 detection 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 particle size- and shape-dependent effects, showing that smaller and irregular nanoparticles are more prone to detachment, and provide a data-driven framework for probing electrocatalyst degradation at scale.