The product selectivity zones in gas diffusion electrodes during the electrocatalytic reduction of CO2

Abstract

Here we report on the most prominent factors influencing the performance of a Cu-based CO₂ electrolyzer operating at high currents. Using a flow-electrolyzer design where CO₂ gas feed passes directly through the electrode interacting with the Cu catalyst layer, we observed that the selectivity of the electrochemical CO₂ reduction in (bulk) pH neutral media can greatly be influenced by adjusting the structure of the electrode. In this, the variations in catalyst loading and ionomer content can profoundly affect the selectivity of CO₂RR. We explore the hypothesis that this originates from the overall mass transport variations within the porous catalytic layer of the gas diffusion electrode. As further evidence for this, apart from the CO₂ electrolysis results, we propose a special method to benchmark the reactant mass transport in flow-cells using oxygen reduction reaction (ORR) limiting current measurements. Our analysis suggests that a restriction of mass transport is highly desirable due to its connection to a local alkalization and corresponding suppression of pH-dependent reaction products, given the absence of local CO₂ concentration limitations. We further show how the electrode structure can be used to push the observed catalytic CO₂ reduction selectivity either towards C₁ or C₂₊ products, dependent on the ionomer content and catalyst loading in a cathodic current range of 50 to 700 mA cm⁻². Measurements at various KHCO₃ electrolyte concentrations agree with the notion of the local pH dictating the overall selectivity and point towards the presence of pronounced concentration gradients within the system. Overall, our work suggests that the differences in electrocatalytic CO₂ reduction selectivity at high currents (in a range of pH neutral buffering electrolytes) largely originate from the local concentration gradients defined by the initial catalyst ink formulation and architecture of the catalytic layer, both of which represent a powerful tool for optimization in the production of selected value-added products.