Deciphering Distinct Overpotential Dependent Pathways for Electrochemical CO2 Reduction Catalyzed by an Iron-Terpyridine Complex

[Fe(tpyPY2Me)]2+ ([Fe]2+) is a homogeneous electrocatalyst for converting CO2 into CO featuring low overpotentials of <100 mV, near-unity selectivity, and high activity with turnover frequencies faster than 100,000 s–1. To identify the origins of its exceptional performance and inform future catalyst design, we report a combined computational and experimental study that establishes two distinct mechanistic pathways for electrochemical CO2 reduction catalyzed by [Fe]2+ as a function of applied overpotential. Electrochemical data shows the formation of two catalytic regimes at low (hTOF/2 of 160 mV) and high (hTOF/2 of 590 mV) overpotential plateaus. We propose that at low overpotentials [Fe]2+ undergoes a two-electron reduction, two-proton transfer mechanism (electrochemical-electrochemical-chemical-chemical, EECC), where turnover occurs through the dicationic iron complex, [Fe]2+. Computational analysis supports the importance of the singlet ground state electronic structure for CO2 binding and that the rate-limiting step is the second protonation in this low-overpotential regime. When more negative potentials are applied, an additional electron transfer event occurs through either a stepwise or proton-coupled electron transfer (PCET) pathway, enabling catalytic turnover from the monocationic iron complex ([Fe]+) via an electrochemical-chemical-electrochemical-chemical (ECEC) mechanism. Comparison of experimental kinetic data obtained from variable controlled potential electrolysis (CPE) experiments with direct product detection with calculated rates obtained from the energetic span model support the PCET pathway as the most likely mechanism. Moreover, we build upon this mechanistic understanding to propose the design of an improved ligand framework that is predicted to stabilize the key transition states identified from our study and explore their electronic structures using an energy decomposition analysis. Taken together, this work highlights the value of synergistic computational/experimental approaches to decipher mechanisms of new electrocatalysts and direct the rational design of improved platforms