Machine learning-driven 3D reconstruction of PEMFC catalyst layers from FIB-SEM imaging

The microstructure of the polymer electrolyte membrane fuel cell (PEMFC) cathode catalyst layer (CCL) critically influences key transport phenomena, directly impacting fuel cell performance. Accurate three-dimensional (3D) reconstruction of the CCL is essential for understanding structure-property relationships and optimizing material design. This study presents a machine learning-driven framework for 3D reconstruction of PEMFC catalyst layers from focused ion beam-scanning electron microscopy (FIB-SEM) images, addressing common challenges such as slice-wise segmentation artifacts and imaging resolution trade-offs. The framework integrates a two-stage segmentation process—beginning with a 2D DeepLabv3+-based network (CatalVis2D) followed by a 3D U-Net-based model (CatalVis3D)—to enhance segmentation accuracy and spatial continuity. To further refine structural fidelity, a regression network (CatalReVis3D) is developed for super-resolution reconstruction, enabling high-resolution 3D structures to be inferred from low-resolution inputs. The approach is validated through segmentation metrics and gas transport simulations. The trained networks also exhibit strong generalization capability across unseen datasets and a different catalyst layer sample composition. This machine learning-driven framework offers a scalable and accurate tool for PEMFC microstructural analysis, contributing to data-driven catalyst layer optimization and advancing electrochemical material research. • Machine-learning for accurate 3D reconstruction of catalyst layer from FIB-SEM images. • Predicting missing catalyst layer structure when using longer FIB-SEM pitch. • Publicly available dataset for catalyst layer segmentation and generation.