Impact of Potential and Active-Site Environment on Single-Iron-Atom Catalyzed Electrochemical CO2 Reduction from Accurate Quantum Many-Body Simulations

05 October 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Single iron atoms supported on nitrogen-doped graphene (Fe–N–C) have shown promise in catalyzing electrochemical CO2 to CO reduction with low overpotential and high selectivity. However, the nature of its rate-limiting step and the effect of active-site environment on catalytic activity are still under debate. Previous theoretical studies exclusively rely on density functional theory (DFT), but their predictions are limited by inherent errors in DFT functionals, leading to diverging conclusions on catalytic mechanisms. Herein, we employ an efficient quantum embedding strategy to enable high-level coupled-cluster (CCSD(T)) simulations of the thermodynamics of Fe–N–C catalyzed CO2 reduction reaction (CO2RR) and its competing hydrogen evolution reaction. Our calculations accurately predict experimental CO binding energy, onset potential, and potential of maximal Faradaic efficiency (FE) with FeN4 as catalytic active site. We find that the rate-limiting step is the formation of *COOH intermediate at low applied potential, which becomes CO2 adsorption and CO desorption at higher potential. Our simulation reveals that the potential-dependent high selectivity of FeN4 originates from higher charge capacity of *COOH compared to *H. Furthermore, our calculations elucidate distinct roles of active-site environments including vacancy defect and nitrogen doping. Particularly, graphitic nitrogen doping simultaneously lowers CO2RR onset potential and allows a wider potential range for high CO FE. This work highlights the importance of robust many-body quantum chemical simulations in achieving quantitative understanding of multi-step electrocatalytic reaction mechanisms.

Supplementary materials

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Supporting information
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Additional computational details, local embedding benchmark results on basis set and finite size convergences, corrections for free energies and potential dependence, and DFT free energy pathways.
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