Surface Coverage as an Important Parameter for Predicting Selectivity Trends in Electrochemical CO₂ Reduction.

Area of Science:

• Electrochemistry
• Materials Science
• Computational Chemistry

Background:

• Electrochemical CO2 reduction (CO2RR) is vital for sustainability.
• Density functional theory (DFT) provides insights but struggles with selectivity trends.
• Current DFT models often overlook crucial parameters like surface coverage.

Purpose of the Study:

• To investigate the impact of surface coverage on CO2RR selectivity using DFT.
• To develop a more accurate DFT approach for predicting electrocatalyst performance.
• To resolve discrepancies between DFT calculations and experimental observations in CO2RR.

Main Methods:

• Incorporating surface coverage of reacting species into DFT models.
• Utilizing surface coverage-dependent adsorption energies for CO2RR intermediates (COOHads and HCOOads).
• Analyzing adsorption energy ratios to predict catalyst selectivity.

Main Results:

• Surface coverage is a critical, previously unconsidered parameter in DFT studies of CO2RR.
• The new DFT approach accurately predicts selectivity for various monometallic catalysts (Pt, Pd, Au, Ag, Zn, Cu, Rh, W, Pb, Sn, In, Cd, Tl).
• Selectivity trends, including those for challenging catalysts like Ag and Zn, are now explainable.
• The study clarifies the role of Tafel and Heyrovsky reactions in COOHads reduction.

Conclusions:

• Explicitly treating surface coverage in DFT models significantly improves CO2RR selectivity predictions.
• This work bridges the gap between theoretical DFT studies and experimental findings.
• The findings highlight the importance of surface coverage for designing efficient CO2RR electrocatalysts.