Nitrate ions produced from industrial and agricultural processes have deeply imbalanced the global nitrogen cycle. Electrocatalytic reduction is a sustainable route to remediate nitrate while generating useful products such as NH₃ or N₂. Here we report density functional theory predictions of intrinsic nitrate reduction rates on PtRu random surface alloy models of compositions from Pt₁₀₀ through Pt₅₀Ru₅₀. Density functional theory calculations predict increasing nitrate binding strength with increasing Ru composition and a maximum in nitrate reduction activity at 25 at% Ru surface composition. The calculations are consistent with experimental activity measurements of nitrate reduction on synthesized platinum-ruthenium catalysts (Pt_xRu_y/C, x = 48–100%). These experiments show that Pt_xRu_y/C alloys are more active than Pt/C, with Pt₇₈Ru₂₂/C the most active of them all (six times more active than Pt/C at 0.1 V vs. RHE). This maximum in activity arises from a transition from nitrate dissociation as the rate-determining step to a new rate-determining step at higher Ru content. Linear adsorbate scaling and Brønsted-Evans-Polanyi relationships exist on the model alloys with N and O binding energies as the descriptors. That such relationships exist with the same descriptors as used in our previous computational study of nitrate reduction on pure metals suggests that prior nitrated reduction microkinetic models developed for pure metals can be extended to alloys. This finding will greatly accelerate the search for performant nitrate reduction electrocatalysts through high-throughput screening and machine learning. Our work demonstrates that electrocatalyst performance for the nitrate reduction reaction is tunable by changing the adsorption strength of the reacting species through alloying. Beyond catalysis, our findings can also be applied to accelerate the discovery of other energy-relevant materials, such as photovoltaics, superconductors, and thermoelectrics.

Zixuan Wang

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