Electrochemical CO₂ Reduction: A Classification Problem.

Area of Science:

• Electrochemistry
• Materials Science
• Computational Chemistry

Background:

• Electrocatalytic CO2 reduction yields diverse products, complicating selectivity prediction.
• The Sabatier principle explains simple reactions but not complex product distributions.
• Understanding product selectivity is crucial for designing efficient CO2 electroreduction catalysts.

Purpose of the Study:

• To develop a descriptor-based model for predicting product distribution in CO2 electroreduction.
• To identify key intermediate binding energies that govern catalytic selectivity.
• To classify and explain the formation of various reduction products, including H2, CO, formic acid, hydrocarbons, and alcohols.

Main Methods:

• Utilized density functional theory (DFT) to calculate binding energies of key intermediates (H*, COOH*, CO*, CH3O*).
• Employed a logistical classification approach to group metals based on experimental CO2 electroreduction products.
• Integrated experimental formaldehyde formation data with literature to analyze alcohol and hydrocarbon production.

Main Results:

• Identified three primary descriptors (adsorption energies of H*, COOH*, and CO*) for classifying major CO2 electroreduction products.
• Found that the adsorption energy of CH3O* is a crucial descriptor for differentiating alcohol formation.
• Demonstrated that the four binding energies collectively explain and group diverse electrochemical reduction products.

Conclusions:

• Non-coupled binding energies of intermediates serve as effective 'genes' for predicting CO2 electroreduction product distribution.
• A combination of H*, COOH*, CO*, and CH3O* adsorption energies provides a comprehensive framework for understanding catalytic selectivity.
• This descriptor-based approach offers a powerful tool for designing catalysts for selective electrochemical reduction of CO2 and related compounds.