Understanding Inlet Concentration Effects on the Electrocatalytic Conversion of CO₂ to Formic Acid in Gas-Fed...

The electrochemical CO₂ reduction reaction (CO2RR) to produce value-added products remains a developing technology for utilizing waste CO₂ streams. Most device-level CO2RR studies use pure CO₂ gas feeds; however, the effect of dilute CO₂ on the electrolyzer performance is an important consideration for large-scale electrolyzer operation, single-pass conversion, and real-world CO₂ source utilization. This work investigates the effect that the CO₂ concentration has on the performance of formic acid (HCOOH) producing tin oxide (SnO₂) and bismuth oxide (Bi₂O₃) catalysts in an electrolyzer device setting. Surprisingly, SnO₂ demonstrated an approximately 20% increase in HCOOH selectivity (Faradaic efficiency) when the CO₂ concentration decreased from 100 to 20%. In contrast, Bi₂O₃ consistently demonstrated high selectivity toward HCOOH across the same CO₂ concentration range. The effects of the CO₂ concentration on selectivity were further investigated with half-cell experiments and in situ Raman spectroscopy, which revealed dynamic changes in the cathodic overpotential and chemical state of the catalyst that depended on the CO₂ concentration. Density functional theory calculations showed how changes in the surface oxidation state of Sn, varying from fully oxidized SnO₂ to metallic Sn(0), affect the thermodynamic barriers of the three main observed products: HCOOH, CO, and H₂. Our results indicate that dilute CO₂ concentrations required larger cathodic overpotentials to sustain a fixed current density, which, in turn, pushed the Sn-based catalyst toward a more reduced surface that was favorable to HCOOH formation. On the other hand, the Bi-based catalyst remained in a metallic state at CO2RR-relevant potentials and demonstrated a consistent product selectivity regardless of CO₂ concentration. These findings highlight how varying the CO₂ inlet gas concentrations affects the chemical state of catalysts and the resulting performance metrics.