(513aq) Cohesive Statistically-Rigorous Kinetic Modeling of Electrocatalytic Carbon Dioxide Reduction

Immobilized molecular complexes are an atomically precise platform on which to systematically interrogate and design electrocatalysts. Additionally, many of these complexes, such as cobalt phthalocyanine (CoPc), are known to be highly active and selective for carbon dioxide electroreduction (CO₂RR) to carbon monoxide. However, the mechanism of CO₂RR on CoPc has been widely debated.[1] We show through collecting kinetic data over a wide range of operating conditions, model fitting, and statistical analysis of competing mechanisms, that this controversy is likely due to inherent complexity of the reaction mechanism.[2] Specifically, we find that reactant order dependences and Tafel slopes deviate from commonly expected values and change depending on the testing conditions. We propose a kinetic model, chosen from more than 15 candidate models, that is able to quantitatively fit all of the experimental data. The model invokes several non-idealities including catalyst poisoning via bicarbonate electrosorption and mixed control between concerted proton-electron transfer and sequential proton-electron transfer. These two mechanistic details have not been proposed for CO₂RR at CoPc before, and it may be because both details deviate from simple kinetic extremes. Our model also suggests that dominant reaction kinetics change depending on testing conditions. These insights would not have been illuminated without cohesively fitting data over a wide range of testing conditions. This work illustrates the advantages of using the following strategies in electrocatalytic mechanism studies: (1) cohesively fitting a large amount of kinetic data to complex mechanistic models and (2) using rigorous statistical assessment during data analysis. These strategies have been successfully applied in thermal catalysis, and may also be leveraged in electrocatalysis to enhance the interpretation of data for the development and evaluation of detailed mechanistic hypotheses.

[1] N. Corbin, J. Zeng, et al. Nano Res. 12, 2093-2125 (2019)

[2] J. Zeng, et al. ACS Catal. 10, 7, 4326-4336 (2020)