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Research Article

Full cell mathematical models of air cathode microbial fuel cells

Wei Yang1,2,3( ), Min Du1, Hongtao Liu1, Jingjing Bao1, Jiguo Tang1( ), Jun Li3
State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource & Hydropower, Sichuan University, Chengdu 610065, China
Key Laboratory of Advanced Reactor Engineering and Safety, Ministry of Education, Tsinghua University, Beijing 100084, China
Key Laboratory of Low-grade Energy Utilization Technologies and Systems, Ministry of Education, Chongqing University, Chongqing 400044, China
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Abstract

Microbial fuel cells (MFCs) as a renewable energy conversion technology have been attracting increasing attention in the past decades. However, a deeper understanding of bioelectrochemical reaction in electrodes is urgent to improve the cell performance towards practical applications. In this paper, a mathematical model of air cathode MFCs was proposed by coupling mass transport and charge conservation with bioelectrochemical/electrochemical reactions. The model was validated based on experimental results and further used to predict the performance of MFCs. The effect of mass transport including oxygen and substrate on electrode kinetics was studied based on the model. The results showed that enhancing mass transport in both anode and cathode remarkably facilitated the electrode current and hence the cell performance, and oxygen transfer in catalyst layer of cathode is the dominating factor limiting the cell performance. The proposed model can provide a facile avenue to capture the interdependence of electrode variables and help guide electrode design for optimizing the performance of MFCs in practical applications.

Keywords

microbial fuel cell (MFC)modelelectrodekineticsmass transfer

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Experimental and Computational Multiphase Flow
Volume 5 Issue 1,
March 2023
Pages 111-121
DOI: 10.1007/s42757-021-0117-3
Cite this article:
Yang W, Du M, Liu H, et al. Full cell mathematical models of air cathode microbial fuel cells. Experimental and Computational Multiphase Flow, 2023, 5(1): 111-121. https://doi.org/10.1007/s42757-021-0117-3

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Received: 28 March 2021
Revised: 10 June 2021
Accepted: 06 July 2021
Published: 25 August 2021
© Tsinghua University Press 2021
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