Enhancing Oxygen Reduction Activity on Pt Monolayer Electrocatalysts through Selective Tuning of Ligand and Lattice Effects

Platinum exhibits superior activity toward the oxygen reduction reaction (ORR) compared with other elements, making it an attractive cathode material for hydrogen fuel cell applications. The main problems with pure platinum electro-catalysts are large activation losses, limited stability, and high material cost. These deficiencies have greatly inhibited the development of efficient, reliable, and inexpensive proton exchange membrane fuel cells (PEM FCs). Previous experimental and computational studies have shown that it is possible to improve the performance of platinum cathodes by tuning the chemical reactivity of catalytic sites through geometric and electronic effects induced by alloying. This has led to the development of a number of Pt-based alloy electrocatalysts for this reaction.

Since ORR rates are strongly tied to the near-surface nanostructure, having precise atomistic control over the local site environment enables selective control of chemical ligand and lattice strain effects present in alloys. In this work, we describe the design of a class of Pt monolayer alloy catalysts with a core-shell structure, which enables independent control over these effects that influence ORR activity. Guided by quantum chemical calculations, we prepared Au_x­Cu_1-x@Au@Pt/C nanoparticle electrocatalysts, where OH affinity and catalytic activity are manipulated by changing the composition within the particle core. Extensive characterization and electrochemical testing of these materials was able to determine that alloys with increased Cu content experience a compressive strain that weakens OH binding energy. By tuning the strength of OH adsorption energy, it is possible to identify alloys with superior catalytic activity (up to four-fold increase in rate) compared to pure Pt/C when normalized on a site basis. Additionally, the observed ORR rates across this alloy series were consistent with predicted activity trends forecasted by our computational model.