The researchers performed correlational and multivariate data analysis on a pre-processed dataset gathered from the literature regarding electrochemical CO₂ reduction in acetonitrile electrolytes. Applying random forest modelling they were able to shed light on the importance of each tuneable parameter as a function of the product Faradaic efficiency.

They identified three major factors: the water content, the applied potential, and the current density. Furthermore, they show that the formation of formate and oxalate are mostly described by just two parameters, whilst H₂ and CO depend on several parameters.

The results thus show the value of using multivariate models for extracting trends from electrochemical data. The researchers encourage their colleagues in the field to use such methods in their research.

Abstract, as published with the paper

The electrochemical CO₂ reduction reaction (CO₂RR) is a promising technology for the utilization of captured CO₂. Though systems using aqueous electrolytes is the state-of-the-art, CO₂RR in aprotic solvents are a promising alternative that can avoid the parallel hydrogen evolution reaction (HER). While system parameters, such as electrolyte composition, electrode material, and applied potential are known to influence the reaction mechanism, there is a lack of intuitive understanding as to how. We show that by using multivariate data analysis on a large dataset collected from the literature, namely random forest modelling, the most important system parameters can be isolated for each possible product. We find that water content, current density, and applied potential are powerful determinants in the reaction pathway, and therefore in the Faradaic efficiency of CO₂RR products.

Paper details

Connor Deacon-Price, Aleksandra Mijatović, Huub C. J. Hoefsloot, Gadi Rothenberg, Amanda C. Garcia: Parameter Dependency of Electrochemical Reduction of CO2 in Acetonitrile – A Data Driven Approach ChemPhysChem, 10 November 2024 DOI: 10.1002/cphc.20240079