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Accurate measurement of internal resistance in microbial fuel cells by improved scanning electrochemical impedance spectroscopy

•The effectiveness of the Rint methodology has explored and identified.•Rint is variable under various of working condition.•The scanning EIS is proposed to verified variation of Rint.•The cause of power-overshoot has been discovered via the Scanning EIS.•The scanning EIS should be developed to elec...

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Bibliographic Details
Published in:Electrochimica acta 2021-01, Vol.366, p.137388, Article 137388
Main Authors: Kim, Bongkyu, Chang, In Seop, Dinsdale, Richard M., Guwy, Alan J.
Format: Article
Language:English
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Summary:•The effectiveness of the Rint methodology has explored and identified.•Rint is variable under various of working condition.•The scanning EIS is proposed to verified variation of Rint.•The cause of power-overshoot has been discovered via the Scanning EIS.•The scanning EIS should be developed to electrochemically understand for MFCs. In this study, a scanning electrochemical impedance spectroscopy (EIS) technique is proposed to confirm the variation of internal resistance (Rint) including charge transfer resistance (Rct) and ohmic resistance (Rohm) under various working conditions of microbial fuel cells (MFCs). In order to establish a scanning EIS method, overall Rint of the system and Rct of anode and cathode were measured by EIS under various external resistance conditions. Using this method, it was confirmed that the Rohm and Rct of each electrode changed variably. Based on the use of scanning EIS, the rapid increase in the Rct of anode was found to be the main cause of a power-overshoot. As such, the use of scanning EIS is a methodology that can provide data to understand undesirable phenomena such as power-overshoot and voltage reversal which are generated by the typical operational characteristics of MFCs when used in scale up experiments. The use of scanning EIS should be applied as a novel measurement technique that improves on the use of the open circuit mode based EIS methods.
ISSN:0013-4686
1873-3859
DOI:10.1016/j.electacta.2020.137388