Nano-structural analysis of effective transport paths in fuel-cell catalyst layers by using stochastic material network methods

Abstract

A two-dimensional material network model has been developed to visualize the nano-structures of fuel-cell catalysts and to search for effective transport paths for the optimal performance of fuel cells in randomly-disordered composite catalysts. Stochastic random modeling based on the Monte Carlo method is developed using random number generation processes over a catalyst layer domain at a 95% confidence level. After the post-determination process of the effective connectivity, particularly for mass transport, the effective catalyst utilization factors are introduced to determine the extent of catalyst utilization in the fuel cells. The results show that the superficial pore volume fractions of 600 trials approximate a normal distribution curve with a mean of 0.5. In contrast, the estimated volume fraction of effectively inter-connected void clusters ranges from 0.097 to 0.420, which is much smaller than the superficial porosity of 0.5 before the percolation process. Furthermore, the effective catalyst utilization factor is determined to be linearly proportional to the effective porosity. More importantly, this study reveals that the average catalyst utilization is less affected by the variations of the catalyst’s particle size and the absolute catalyst loading at a fixed volume fraction of void spaces.