Statistical prediction of fuel cell catalyst effectiveness: Quasi-random nano-structural analysis of carbon sphere-supported platinum catalysts

In this study, a three-dimensional material network model is developed to visualize the nanoscale structures of carbon sphere-supported platinum (Pt/C) catalysts and to examine the effective transport paths to optimize the performance of randomly disordered, ternary-phase fuel cell catalysts. The catalyst layer domain is modeled using a quasi-random stochastic Monte Carlo-based method that utilizes random number generation processes. Successful interconnections of the three catalyst components are identified, and the catalyst effectiveness is defined to statistically estimate the fraction of the fuel cell catalysts that are utilized. Various fuel cell catalyst compositions are simulated to elucidate the effects of the electron, ion, and mass transport paths on the catalyst effectiveness. The statistical data show that at low ionomer contents, the accessible pore ratio is maximized, enhancing mass transport, and the effective ionomer configuration therefore significantly affects the catalyst effectiveness. In contrast, at high ionomer volume fractions, the ionomers form agglomerate chains that effectively transport ions, whereas the average accessible pore ratios are relatively low. More importantly, this study reveals that the maximum effectiveness depends strongly on the accessible pore ratio and the optimal ionomer volume fraction is inversely proportional to the Pt/C volume fraction.