Reducing the crossover of carbonate and liquid products during carbon dioxide electroreduction

Abstract

Summary: 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.

Keywords

• CO2 electroreduction
• catalysis
• membrane electrode assembly
• computational modeling
• machine learning
• carbon utilization