3D hybrid stochastic reconstruction of catalyst layers in proton exchange membrane fuel cells from 2D images

Proton exchange membrane fuel cells show promise in a variety of applications, aiding global efforts to achieve net-zero emissions. Its catalyst layer is a source of significant efficiency loss due to resistance to transport and reaction mechanisms in its porous structure. A computational reconstruction model of the porous microstructure is required to study the applicable pore-scale phenomena in the catalyst layer. A hybrid stochastic reconstruction technique is presented which reconstructs a catalyst layer from 2D scanning electron microscope experimental images, combining a sphere-packing approach with sphere-based simulated annealing and simultaneously optimizing the microstructure using the two-point correlation and the lineal path distribution. This technique (Method 2) is compared to a conventional sphere-based simulated annealing method (Method 1) and to a fabrication-based reconstruction (Method 0). Pore size distributions, surface area, porosity, mean pore diameter and mean chord lengths are used to validate the model and evaluate the accuracy of each reconstruction method. Both Method 1 and 2 result in porosity values within approximately 0.0001% of the reference porosity. Method 2 achieves higher accuracy than Methods 1 and 0 in the mean chord lengths and surface area, and both Methods 1 and 2 improve in the mean pore size over Method 0.