Reaching machine learning leverage to advance performance of electrocatalytic CO₂ conversion in non-aqueous deep eutectic electrolytes.

ABSTRACT: Deep eutectic electrolytes (DEEs) show promise for future electrochemical systems due to their adjustable buffer capacities. This study utilizes machine learning algorithms to analyse the carbon dioxide reduction reaction (CO₂RR) in DEEs with a buffer capacity of approximately 10.21 mol/pH. The objective is to minimize undesired hydrogen evolution reactions (HER) and render CO₂RR dominant in a membrane cell. The CO₂RR process was found to be non-adiabatic, as the time of nuclear motion for CO₃^2- in K₂CO₃ product, through CO₂^●- trapping, is 0.368 femtoseconds shorter than the 1.856 × 10^-3s charge transfer relaxation time. Microkinetic analysis reveals that the rate of CO₂

SUBMITTER: Halilu A 

PROVIDER: S-EPMC11494191 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature