Predicting PEMFC performance from a volumetric image of catalyst layer structure using pore network modeling

Abstract

A pore-scale model of a PEMFC cathode catalyst layer was developed using the pore network approach and used to predict polarization behavior. A volumetric image of a PEMFC catalyst layer was obtained using FIB-SEM with 4 nm resolution in all 3 directions. The original image only differentiated between solid and void, so a simple but effective algorithm was developed to insert tightly packed, but non-overlapping carbon spheres into the solid phase, which were then decorated with catalyst sites. The resultant image was a 4-phase image containing void, ionomer, carbon, and catalyst, each in proportion to the known Pt loading, carbon-to-ionomer ratio, and porosity. A multiphase pore network model was extracted from this image, and multiphysics simulations were conducted to predict the polarization behavior of an operating cell. It was shown that not only can beginning of life polarization performance be predicted with minimal fitting parameters, but degraded performance 30 k cycles was also well captured with no additional fitting. This latter result was accomplished by deleting catalyst sites from the network in proportion to the experimentally observed distribution of electrochemical surface area loss, obtained from TEM image of catalyst loading. The model included partitioning of oxygen into the ionomer phase, explicitly incorporating the oxygen transport resistance which dominates cell performance at higher current density. Although Knudsen diffusion is present at the scales present (<100nm), it represented a negligible fraction of the total transport resistance, which was dominated by the low solubility and slow diffusivity in the ionomer phase. This work showed that the performance of a typical PEMFC is highly dependent on the structural details of the catalyst layer, to the extent that polarization curves can be well predicted by direct inspection of an image of the catalyst layer. This work paves the way for a deeper understanding of the structure-performance relationship in these complex materials and the search for optimized catalyst layer designs.