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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. 

Increased energy demand and environmental concerns are driving the search for cleaner, more efficient power generation. Fuel cells (FCs) offer a solution by converting chemical energy from fuel and oxidant directly into electricity with high efficiency (40-70%) and zero carbon emissions. Enhancing the performance and durability of polymer electrolyte membrane fuel cells (PEMFC) while reducing costs remains a significant challenge. The current planar FC design presents various issues, including high costs and weight due to metal or graphite bipolar plates, high-pressure drop-in reactant gas diffusion channels, reactant gas transport losses in the agglomerate-type catalyst layers, water flooding, to name just a few. Exploring novel biomimetic designs may offer solutions to these challenges. This research is inspired by vascular plant structures to improve mass transport and efficiency. The cathode uses carbon nanofibers (CNF) made via electrospinning, with Pt nanorod catalyst on the surface in an open electrode layout. This CNF-type cathode was used to create a membrane electrode assembly (MEA), using a commercial membrane and typical anode. A number of parameters were varied, including overall Pt loading, Pt concentration on the fibers, design with and without gas diffusion layer, using CNF mat. The MEAs were tested to establish links between structure, properties, and performance. Performance was evaluated through polarization curves and electrochemical impedance spectroscopy, while beginning of test and end of test structure was investigated using microscopy. CNF-based MEAs achieved a higher current density compared to the commercial MEA at lower Pt loading. This suggests that reduced loading still maintained acceptable Pt utilization, reducing the Pt amount in the MEA. In summary, the CNF catalyst support displayed improved durability, mass transport, Pt utilization, and efficiency thanks to its porous mesh structure. 

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.