Prediction of Highly Selective Electrocatalytic Nitrogen Reduction at Low Overpotential on a Mo-Doped g-GaN Monolayer

Lesheng Li, J. Mark P. Martirez, Emily A. Carter

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Identifying efficient electrocatalysts with low overpotential and high selectivity for producing ammonia from nitrogen gas is essential for any future electrocatalytic nitrogen reduction reaction (NRR)-based ammonia synthesis. Via density functional theory calculations and the computational hydrogen electrode model, we systematically examine the prospect of using a single-transition-metal (TM)-atom-doped graphene-like GaN (g-GaN) monolayer as an electrocatalyst for artificial nitrogen reduction. Among 15 TMs investigated, the Mo-doped g-GaN (Mo@g-GaN) monolayer is the only electrocatalyst predicted to be feasible for the NRR. The Mo@g-GaN monolayer satisfies all screening criteria considered for activating the inert NN triple bond effectively, including stabilization of the adsorbed (*) NRR intermediate *NNH and destabilization of the *NH2 species. This monolayer also possesses sufficient overall stability. A complete analysis of the likely mechanisms involved in the NRR on this catalyst suggests that the Mo@g-GaN monolayer could exhibit promising NRR catalytic activity. It achieves this via one specific (distal) pathway, which has a very low onset potential of -0.33 V vs the reversible hydrogen electrode (RHE), corresponding to a low overpotential of 0.42 V vs the RHE, defined using the measured equilibrium potential for NRR of 0.09 V vs the RHE. The potential-determining step, conversion of *NH2 to *NH3, also exhibits a surmountable barrier of 0.42 eV, suggesting kinetics will be facile. Finally, the Mo@g-GaN monolayer is predicted to exhibit substantial selectivity (∼31%) toward ammonia synthesis over the competing hydrogen evolution reaction. These findings may open a potential route for artificial ammonia synthesis using a single-atom catalyst under ambient conditions.

Original languageEnglish (US)
Pages (from-to)12841-12857
Number of pages17
JournalACS Catalysis
Volume10
Issue number21
DOIs
StatePublished - Nov 6 2020

All Science Journal Classification (ASJC) codes

  • Catalysis
  • Chemistry(all)

Keywords

  • density functional theory calculations
  • gallium nitride monolayer
  • nitrogen reduction reaction
  • selectivity
  • single-atom catalysts

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