Predicting electrocatalytic urea synthesis using a two-dimensional descriptor

Electrochemical synthesis routes powered by renewable electricity can provide sustainable chemical commodities by replacing conventional fossil-based processes. Increasing research focuses on value-added chemicals like the indispensable fertilizer urea, which also constitutes a study case for electrochemical CN-coupling. To guide the identification of highly selective catalysts, we aim to provide new insight by analysing existing experimental data on the selectivity of transition metal catalysts towards electrochemically synthesized urea. Firstly, we project high dimensional experimental data using principal component analysis (PCA) to lower dimensions, and thereby confirm that urea selectivity is correlated with the selectivity towards CO and NH3. Furthermore, we identified the most suitable two-dimensional descriptors for selectivity prediction out of various adsorption energies calculated using density functional theory (DFT). We suggest that the adsorption energies of *H and *O on transition metal slabs predict the selectivity towards urea in the co-reduction of CO2 and nitrite (NO2-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\rm{NO}}}_{2}<^>{-}$$\end{document}).