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A systematic analysis and optimization of bicarbonate electrolysis based on a bipolar membrane through multiscale simulation

Author

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  • Tian, Di
  • Wu, Ruobing
  • Qu, Zhiguo
  • Wang, Hui

Abstract

Bicarbonate electrolysis facilitated by bipolar membrane has emerged as a compelling alternative to gaseous CO2 electrolysis, primarily because of its enhanced product concentration and streamlined processes. However, despite increasing attention, a comprehensive understanding of bicarbonate electrolysis has been eclipsed by the complexity of the intertwined electrode reactions and water dissociation promotion mechanisms of bipolar membranes. A physics-based modeling approach has substantial potential to untangle the impact of intertwined physicochemical processes and expedite the optimization of bicarbonate electrolysis. In this study, a multiscale model coupled with a 1D bipolar membrane and a 2D cathode model is presented to explore the effects of bipolar membranes on cathode reactions and performance optimization using H+ flux as a bridge. For a bipolar membrane, the attenuation mechanism of water dissociation involving ohmic and concentration losses is presented and described using mathematical eqs. A quantification metric, the water dissociation efficiency of a bipolar membrane, is proposed to denote the degree of promoted water dissociation. Two noteworthy parameters related to the fixed functional group concentration are determined and detailed: the onset current density required to stimulate water dissociation and the limited water dissociation efficiency caused by attenuation. For the cathode, a synergistic enhancement mechanism among the operating parameters, membrane properties, and cathode properties is identified. The combined effect of these parameters on the electrolytic performance surpasses the sum of their individual enhancements. Following parameter optimization, there is a notable increase in the current density and Faraday efficiency of bicarbonate electrolysis by 68.6 mA/cm2 and 18.3%, respectively. This study provides a holistic understanding of bicarbonate electrolysis and substantial guidance for its future design.

Suggested Citation

  • Tian, Di & Wu, Ruobing & Qu, Zhiguo & Wang, Hui, 2024. "A systematic analysis and optimization of bicarbonate electrolysis based on a bipolar membrane through multiscale simulation," Applied Energy, Elsevier, vol. 364(C).
    • Handle: RePEc:eee:appene:v:364:y:2024:i:c:s0306261924005270
    DOI: 10.1016/j.apenergy.2024.123144
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    1. Miao Zhong & Kevin Tran & Yimeng Min & Chuanhao Wang & Ziyun Wang & Cao-Thang Dinh & Phil De Luna & Zongqian Yu & Armin Sedighian Rasouli & Peter Brodersen & Song Sun & Oleksandr Voznyy & Chih-Shan Ta, 2020. "Accelerated discovery of CO2 electrocatalysts using active machine learning," Nature, Nature, vol. 581(7807), pages 178-183, May.
    2. Zhang, Hao & Wang, Huizhi & Jiao, Kui & Xuan, Jin, 2020. "pH-differential design and operation of electrochemical and photoelectrochemical systems with bipolar membrane," Applied Energy, Elsevier, vol. 268(C).
    3. Chen, Hao & Dong, Sheying & Zhang, Yaojun & He, Panyang, 2022. "A comparative study on energy efficient CO2 capture using amine grafted solid sorbent: Materials characterization, isotherms, kinetics and thermodynamics," Energy, Elsevier, vol. 239(PD).
    4. J. Stuart B. Wyithe & Abraham Loeb, 2002. "Magnification of light from many distant quasars by gravitational lenses," Nature, Nature, vol. 417(6892), pages 923-925, June.
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