Atomic-Scale Understanding of Selectivity Control in Nitrate Reduction on Cu(100) Under Acidic and Alkaline...

We investigate the nitrate reduction reaction (NO₃ RR) on the Cu(100) surface using grand-canonical density functional theory (GC-DFT) under constant electrode potential. Ionic correction schemes are applied to both reactants and products to ensure an accurate representation of their physical states, and gas-phase reference error corrections are included to address known limitations of the generalized gradient approximation (GGA) in DFT simulations. The role of pH in modulating the binding energies of key intermediates and transition states governing the elementary steps of nitrate conversion is analyzed as a function of applied electrode potential. To validate the computational approach, DFT-based molecular dynamics simulations with explicit water molecules and activation energy calculations for proton-electron transfer steps are performed. Based on the modeling framework and computational strategy presented here, the results show that under acidic conditions, NO₃RR on Cu(100) favors the formation of nitric oxide and ammonium at cathodic potentials (U_RHE < 0.1 V), whereas under alkaline conditions at comparable potentials, nitrite and hydroxylamine dominate. These findings are consistent with experimentally reported potential- and pH-dependent selectivity trends and suggest that the approach provides a general computational framework for modeling pH-dependent electrocatalytic reactions and predicting potential-dependent selectivity.