Predicting electrocatalytic urea synthesis using a two-dimensional descriptor.

Area of Science:

• Electrochemistry
• Materials Science
• Catalysis

Background:

• Renewable electricity enables sustainable chemical synthesis, replacing fossil fuel-based methods.
• Electrochemical synthesis of urea, a vital fertilizer, is a key area for developing efficient CN-coupling reactions.
• Identifying selective catalysts is crucial for optimizing electrochemical urea production.

Purpose of the Study:

• To analyze experimental data on transition metal catalyst selectivity for electrochemical urea synthesis.
• To provide new insights for identifying highly selective electrocatalysts.
• To correlate urea selectivity with selectivity for other products like CO and ammonia.

Main Methods:

• Utilizing principal component analysis (PCA) to reduce the dimensionality of experimental data.
• Applying density functional theory (DFT) calculations to determine adsorption energies.
• Identifying predictive descriptors for catalyst selectivity.

Main Results:

• Urea selectivity is correlated with selectivity for carbon monoxide (CO) and ammonia (NH3).
• Two-dimensional descriptors based on DFT-calculated adsorption energies were identified as most suitable for predicting selectivity.
• Adsorption energies of *H and *O on transition metal surfaces effectively predict urea selectivity in CO2 and nitrite co-reduction.

Conclusions:

• The study provides a data-driven approach to understanding and predicting catalyst performance in electrochemical urea synthesis.
• Identified descriptors offer guidance for the rational design of advanced electrocatalysts for sustainable fertilizer production.
• This work advances the field of electrochemical CN-coupling for value-added chemical commodities.