The oxygen reduction reaction (ORR) is known to limit performance in low-temperature hydrogen fuel cells.  Pt-based catalysts are generally required not only for sufficient activity, but also for stability in strongly acidic electrolytes. Recent development of stable hydroxide-conducting membranes has made fuel cell operation at high pH feasible and vastly expanded the number of available electrode materials. Most notably, in basic medium Ag is redox stable and shows comparable ORR activity to Pt on a cost basis.   In this work, we couple cyclic voltammetry (CV) and rotating disk electrode (RDE) experiments with quantum chemical density functional theory (DFT) calculations and microkinetic modeling to compare ORR mechanisms on Pt and Ag.  Our DFT calculations account for the potential-dependent interactions of reactive intermediates and co-adsorbed species on the catalyst surface in an aqueous environment. Predicted surface structures agree well with CV adsorption measurements, and predicted overpotentials agree with the onset of catalytic activity measured with the RDE method. With the addition of microkinetic modeling, we are able to predict not only trends in activity, but also potential-dependent shifts in experimentally measured kinetic parameters (e.g. the Tafel slope and the reaction order with respect to pH). These kinetic variations prove to be a useful diagnostic in assigning a reaction mechanism on each surface. Our studies indicate that optimization of an ORR catalyst is not simply a tradeoff between the rates of O2 activation and removal of hydroxyl groups in the form of water. Rather, there is often a thermodynamically controlled regime, in which hydroxyl species can poison the surface, even if weakly bound, due to a high surface coverage induced by equilibrium with water. Using these insights we propose a set of catalyst design criteria for optimizing the rate of the ORR.

Understanding the kinetic behavior of the oxygen reduction reaction through   density functional theory calculations and microkinetic modeling

244th National Meeting (2012)

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