Early Failure Detection for CO2 Reduction Electrolyzers Via in-Line Electrochemical Impedance Spectroscopy

Abstract

The electrochemical CO2 reduction reaction (CO2RR) presents the opportunity to produce chemical feedstocks and fuels directly from CO2. Recent achievements in energy efficiency and current density are approaching the targets required for commercial viability; however, stability remains more than two orders of magnitude below the required 80,000 h. In CO2RR there is a challenge to distinguish the cause of failure among many possibilities. Failure can result from initial setup (e.g. over or under compression of the membrane and electrodes), gradual degradation of components (e.g. cathode and anode catalyst restructuring and dissolution), accumulation of products or byproducts (e.g. alcohols or salt accumulation), or immediate failures (e.g. a hole in the membrane or a short circuit). These failure modes must be addressed to reach the targeted stability needed for the wide-scale adoption of this technology. Early identification and mitigation of these failures would increase the electrolyzer lifetime and the commercial viability of CO2RR electrolyzers.Current techniques to characterize failures of the electrolyzer rely on post-mortem analysis of cell components. The analysis of individual components after operation may not accurately reflect their state in operando. There are techniques that are capable of analyzing the cell in operation (e.g. XAS, Raman, and TEM), but these typically require modified cell architectures and are operated at lower reaction rates. To effectively diagnose failure, the analysis technique should be applied to electrolyzers operating under realistic conditions.Here we implement an in-line real-time electrochemical impedance spectroscopy (EIS) technique to monitor CO2RR electrolyzers during operation. EIS is a non-destructive technique that can provide continuous information on the performance of specific components within the electrolyzer. We characterize common failure modes, such as poor compression, salt formation, catalyst degradation, and short circuits, and their identifying changes to the EIS response. We extract key electrochemical parameters from the EIS response and use them to develop a framework to identify and prevent the most common failure modes. Among other applications, this framework allowed for the detection of anode catalyst degradation 11 h before other indicators, such as cell voltage or product selectivity. This technique and framework can be applied to monitor CO2 electrolyzers as they are scaled and stacked.