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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. 

In this work we investigate the effect of platinum loading and layer thickness on cathode catalyst degradation by a comprehensive in situ and STEM-EDS characterization. To decouple the effect of the platinum loading and layer thickness from each other, the experiments were categorized in two sets, each with cathode loadings varying between 0.1 and 0.4 mg_Ptcm⁻²: (i) Samples with a constant Pt/C ratio and thus varying layer thickness, and (ii) samples with varying Pt/C ratios, achieved by dilution with bare carbon, to maintain a constant layer thickness at different platinum loadings. Every MEA was subjected to an accelerated stress test, where the cell was operated for 45,000 cycles between 0.6 and 0.95 V. Regardless of the Pt/C ratio, a higher relative loss in electrochemically active surface area was measured for lower Pt loadings. STEM-EDS measurements showed that Pt was mainly lost close to the cathode—membrane interface by the concentration driven Pt²⁺ion flux into the membrane. The size of this Pt-depletion zone has shown to be independent on the overall Pt loading and layer thickness, hence causing higher relative Pt loss in low thickness electrodes, as the depletion zone accounts for a larger fraction of the catalyst layer.