Self-Correcting Operando Impedance Spectroscopy Enables Dynamic Adaptability and Mechanistic Insights of Electrochemical CO2 Reduction

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Electrochemical systems are inherently dynamic, often leading to unreliable performance assessments and even degradation. To address this challenge, we developed a self-correcting operando electrochemical impedance spectroscopy (EIS) method, regulated by a custom Python script, enabling non-destructive, real-time, and adaptive control over electrochemical processes. The methods' applicability was demonstrated on copper-catalyzed electrochemical CO2 reduction (ECO2R), a system known for its instability. By dynamically adjusting electrochemical parameters based on live EIS feedback, we effectively neutralized bubble-induced artifacts and achieved reliable tracking of activity and selectivity evolution. Additionally, the approach enabled direct observation of catalyst surface changes under true reaction conditions, yielding accurate operando insights into how interfacial alterations drive ECO2R performance shifts. Altogether, the results presented in this letter establish our method as a versatile operando framework to uncover, correct, and ultimately eliminate artifacts arising from the dynamic behavior of electrochemical systems.

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