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

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