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Abstract Performance and durability of electrodes in proton exchange membrane fuel cells (PEMFCs), as one of the most promising zero‐emission power generation technologies, depend on the composition, microstructure, and distribution of its components—metal catalyst, carbon support, and ionomer. Their improvement requires an in‐depth understanding of the electrodes’ structure‐property‐performance relationship, for which transmission electron microscopy (TEM) has been an invaluable tool. However, the conventional TEM sample preparation, namely epoxy‐embedding ultramicrotomy, poses impediments in imaging ionomer and distinguishing it from carbon. Therefore, in this research, an epoxy‐free ultramicrotome technique is implemented on beginning‐of‐life (BOL) and end‐of‐life (EOL) PEMFC samples. For the first time, TEM and electron tomography‐TEM images reveals fascinating details of the ionomer network, carbon particles’ structure, and Pt distribution in BOL, as well as their structural changes after the cell degradation. Finally, the structural descriptors, extracted by a proprietary quantification method, are correlated with visual observations. 

3D imaging of porous materials in polymer electrolyte membrane (PEM)-based devices, coupled with in situ diagnostics and advanced multi-scale modelling approaches, is pivotal to deciphering the interplay of mass transport phenomena, performance, and durability. The characterization of porous electrode media in PEM-based cells encompassing gas diffusion layers and catalyst layers often relies on traditional analytical techniques such as 2D scanning electron microscopy, followed by image processing such as Otsu thresholding and manual annotation. These methods lack the 3D context needed to capture the complex physical properties of porous electrode media, while also struggling to accurately and effectively discriminate porous and solid domains. To achieve an enhanced, automated segmentation of porous structures, we present a 3D deep learning-based approach trained on calibrated 3D micro-CT, focused ion beam-scanning electron microscopy datasets, and data from physical porosity measurements. Our approach includes binary segmentation for porous layers and a multiclass segmentation method to distinguish the microporous layers from the gas diffusion layers. The presented analysis framework integrates functions for pore size distribution, porosity, permeability, and tortuosity simulation analyses from the resulting binary masks and enables quantitative correlation assessments. Segmentations achieved can be interactively visualized on-site in a 3D environment. 

Computer-aided data acquisition, analysis, and interpretation are rapidly gaining traction in numerous facets of research. One of the subsets of this field, image processing, is most often implemented for post-processing material microstructural characterization data to understand better and predict materials’ features, properties, and behaviors at multiple scales. However, to tackle the ambiguity of multi-component materials analysis, spectral data can be used in combination with image processing. The current study introduces a novel Python-based image and data processing method for in-depth analysis of energy dispersive spectroscopy (EDS) elemental maps to analyze multi-component agglomerate size distribution, the average area of each component, and their overlap. The framework developed in this study is applied to examine the interaction of Cerium Oxide (CeO x ) and Palladium (Pd) particles in the membrane electrode assembly (MEA) of an Anion-Exchange Membrane Fuel Cell (AEMFC) and to investigate if this approach can be correlated to cell performance. The study also performs a sensitivity analysis of several parameters and their effect on the computed results. The developed framework is a promising method for semi-automatic data processing and can be further advanced towards a fully automatic analysis of similar data types in the field of clean energy materials and broader.