Reducing the crossover of carbonate and liquid products during carbon dioxide electroreduction
Membrane electrode assembly (MEA) electrolyzers can perform stable, high-rate carbon dioxide (CO2) electroreduction for renewable fuels and chemicals, thereby realizing effective carbon utilization to mitigate anthropogenic CO2 emissions. Here, we present a numerical, multiphysics model, computation...
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Veröffentlicht in: | Cell reports physical science 2021-08, Vol.2 (8), p.100522, Article 100522 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Membrane electrode assembly (MEA) electrolyzers can perform stable, high-rate carbon dioxide (CO2) electroreduction for renewable fuels and chemicals, thereby realizing effective carbon utilization to mitigate anthropogenic CO2 emissions. Here, we present a numerical, multiphysics model, computationally intensified 60-fold with a machine learning analysis of computational and experimental data, to address the most urgent systems challenges in CO2 MEA electrolyzers: mitigating carbonate and liquid product crossover to increase CO2 utilization and energy efficiency. We explore the effect of varying the applied potential, CO2 partial pressure, ion-exchange membrane thickness, membrane porosity, and membrane charge on these three metrics. By selectively tuning these physical system parameters, we identify conditions that realize negligible CO2 reactant loss, a 2-fold enhancement in CO2 utilization, and a 2-fold decrease in Nernstian overpotential, corresponding to a multi-carbon, full-cell energy efficiency of 21%. These results may direct future MEA system designs and motivate thin anion-exchange membrane structures.
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•Computational model for CO2 reduction in a membrane electrode assembly is developed•Low-carbonate crossover achieved at low CO2 partial pressure and high current density•Thin anion-exchange membranes promote nearly 100% CO2 utilization
Electrocatalytic CO2 reduction (CO2R) offers renewable energy storage in the form of fuels and chemicals, realizing carbon utilization to mitigate anthropogenic CO2 emissions. McCallum et al. report a computational model, coupled with machine learning, to efficiently explore the broad parameter space of CO2R to multi-carbon products in a membrane electrode assembly. |
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ISSN: | 2666-3864 2666-3864 |
DOI: | 10.1016/j.xcrp.2021.100522 |