Deep learning morphological distribution analysis of metal alloy catalysts in proton exchange membrane fuel cells

Abstract

Measuring the variation of the morphological distribution of Pt-based catalyst particles on supports using transmission electron microscopy provides crucial information for understanding the performance degradation behaviors of proton exchange membrane fuel cells and for designing more durable electrocatalysts. However, interpretation based on a few micrographs is statistically insignificant, whereas manual analyses of large image datasets are time-consuming and often require subjective human decisions. To address this issue, an efficient method assisted by deep learning is proposed for the automated interpretation of massive image datasets of metal catalyst nanoparticles (NPs). Based on an attention-aided deep convolutional neural network for object detection (that is, Attention U-Net), the proposed approach rapidly measures the changes in the structural parameters of Pt/Co NPs on a porous support and quantitatively evaluates morphological changes of the NPs after cycling, with statistical significance for their sizes and separating distances.