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电解槽的早期预警:通过在线电化学阻抗谱监测一氧化碳还原

Early Warning for the Electrolyzer: Monitoring CO Reduction via In-Line Electrochemical Impedance Spectroscopy.

作者信息

Warkentin Hugh, O'Brien Colin P, Holowka Sarah, Maxwell Benjamin, Awara Mariam, Bouman Mark, Zeraati Ali Shayesteh, Nicholas Rachael, Ip Alexander H, Elsahwi Essam S, Gabardo Christine M, Sinton David

机构信息

Department of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, ON, Canada, M5S 3G8, Canada.

CERT Systems Inc., 406-501 Alliance Ave, Toronto, ON M6 N 2 J1, Canada.

出版信息

ChemSusChem. 2023 Dec 7;16(23):e202300657. doi: 10.1002/cssc.202300657. Epub 2023 Aug 25.

DOI:10.1002/cssc.202300657
PMID:37535892
Abstract

The electrochemical CO reduction reaction (CO RR) to fuels and feedstocks presents an opportunity to decarbonize the chemical industry, and current electrolyzer performance levels approach commercial viability. However, stability remains below that required, in part because of the challenge of probing these electrolyzer systems in real time and the challenge of determining the root cause of failure. Failure can result from initial conditions (e. g., the over- or under-compression of the electrolyzer), gradual degradation of components (e. g., cathode or anode catalysts), the accumulation of products or by-products, or immediate changes such as the development of a hole in the membrane or a short circuit. Identifying and mitigating these assembly-related, gradual, and immediate failure modes would increase both electrolyzer lifetime and economic viability of CO RR. We demonstrate the continuous monitoring of CO RR electrolyzers during operation via non-disruptive, real-time electrochemical impedance spectroscopy (EIS) analysis. Using this technique, we characterise common failure modes - compression, salt formation, and membrane short circuits - and identify electrochemical parameter signatures for each. We further propose a framework to identify, predict, and prevent failures in CO RR electrolyzers. This framework allowed for the prediction of anode degradation ~11 hours before other indicators such as selectivity or voltage.

摘要

将电化学一氧化碳还原反应(CO RR)应用于燃料和原料生产,为化学工业脱碳提供了契机,目前电解槽的性能水平已接近商业可行性。然而,稳定性仍低于所需水平,部分原因在于实时探测这些电解槽系统存在挑战,以及确定故障根源也存在挑战。故障可能源于初始条件(例如电解槽的过度压缩或压缩不足)、部件的逐渐退化(例如阴极或阳极催化剂)、产物或副产物的积累,或者是诸如膜上出现孔洞或短路等即时变化。识别并减轻这些与组件相关的、渐进的和即时的故障模式,将延长电解槽的使用寿命,并提高CO RR的经济可行性。我们通过非侵入式实时电化学阻抗谱(EIS)分析,展示了对运行中的CO RR电解槽进行连续监测。利用这项技术,我们表征了常见的故障模式——压缩、盐形成和膜短路,并确定了每种故障模式的电化学参数特征。我们还提出了一个框架,用于识别、预测和预防CO RR电解槽中的故障。该框架能够在诸如选择性或电压等其他指标出现之前约11小时预测阳极退化。

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