First-principles density functional theory studies of electrochemical reactions and catalysts

Abstract

First-principles density functional theory (DFT) is a powerful and cost-effective tool to explore mechanisms of electrocatalytic reactions and to calculate material properties of electrocatalysts in energy storage and conversion devices. In this study, DFT simulations are used to investigate the in-depth mechanisms and potential electrocatalysts for electrochemical reduction of nitrate (NO₃RR) to produce NH₃ and N₂, and its co-reduction with CO₂(CO₂NO₃RR) to produce urea. To unveil the mechanism of NO₃RR on Cu, a hybrid DFT model was developed to incorporate the effects of pH and crystal facet. The results demonstrate that the selectivity of NO₃RR shifts from NH₃ to NH₂OH on the (100) facet as pH increases, while NH₃ is formed on the (111) facet regardless of pH but with slower kinetics than on the (100) facet. In another study, a theoretical screening was conducted to predict high-performance single transition metal atom and nitrogen co-doped graphene (TM-N₄/C) catalysts for NO₃RR. As a result, Cu- and Pt-N₄/C are found to be highly active for NO₃RR owing to the optimal adsorptions of NO and N, where Pt-N₄/C can selectively produce N₂, and Re-N₄/C is highly selective for NH₃ formation. The mechanism of CO₂NO₃RR on a Cu-based catalyst, Cu₂(OH)₂CO₃ nanobars (Cu-NBs), was elucidated by DFT simulations. The crucial intermediates for C-N coupling to produce urea are found to be *COOH and *NHO adsorbed on dual active sites of Cu-NBs, whose self-reductions are energetically less favorable than the coupling process. To capture the spectator and solvent effects, a hybrid DFT model was developed for the screening of transition metal doped Cu-NB (TM-Cu-NB) catalysts for electrochemical CO₂NO₃RR. As a result, Ti-, V- and Cr-Cu-NBs are promising candidates for urea generation with high activities and selectivity for C-N coupling, attributed to the relatively weak *CO adsorption and the moderately strong *NO adsorption.

Date of Award	2023
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology
Supervisor	Minhua SHAO (Supervisor)