Electrochemical CO2 Reduction over Metal-/Nitrogen-Doped Graphene Single-Atom Catalysts Modeled Using the Grand- Canonical Density Functional Theory

Renewably driven, electrochemical conversion of carbon dioxide into value-added products is expected to be a critical tool in global decarbonization. However, theoretical studies based on the computational hydrogen electrode largely ignore the nonlinear effects of the applied potential on the calculated results, leading to inaccurate predictions of catalytic behavior or mechanistic pathways. Here, we use grand canonical density functional theory (GC-DFT) to model electrochemical CO2 reduction (CO2R) over metal-and nitrogen-doped graphene catalysts (MNCs) and explicitly include the effects of the applied potential. We used GC-DFT to compute the CO2 to CO reaction intermediate energies at -0.3, -0.7, and -1.2 VSHE catalyzed by MNCs each doped with 1 of the 10 3d block metals coordinated by four pyridinic nitrogen atoms. Our results predict that Sc-, Ti-, Co-, Cu-, and Zn-N4Cs effectively catalyze CO2R at moderate to large reducing potentials (-0.7 to -1.2 V-SHE). ZnN4C is a particularly promising electrocatalyst for CO2R to CO both at low and moderate applied potentials based on our thermodynamic analysis. Our findings also explain the observed pH independence of CO production over FeN4C and predict that the rate-determining step of CO2R over FeN4C is not *CO2- formation but rather *CO desorption. Additionally, the GC-DFT-computed density of states analysis illustrates how the electronic states of MNCs and adsorbates change non-uniformly with applied potential, resulting in a significantly increased *CO2- stability relative to other intermediates and demonstrating that the formation of the adsorbed *CO2- anion is critical to CO2R activation. This work demonstrates how GC-DFT paves the way for physically realistic and accurate theoretical simulations of reacting electrochemical systems.

Automated summary

In this study, electrochemical CO2 reduction (CO2R) over metal-and nitrogen-doped graphene catalysts (MNCs) was modeled using grand canonical density functional theory (GC-DFT), explicitly including the effects of the applied potential. The results predict that certain MNCs effectively catalyze CO2R at moderate to large reducing potentials, with ZnN4C identified as a promising electrocatalyst across a range of potentials. Thermodynamic analysis reveals insights into the pH independence of CO production and the rate-determining step of CO2R over specific catalysts.