Electrocatalytic
reduction of oxidized nitrogen compounds (NOx) promises to help rebalance the nitrogen
cycle. It is widely accepted that nitrate reduction to NH4+/NH3involves NO as an intermediate, and NO
hydrogenation is the potential-limiting step of NO reduction. Whether
*NO hydrogenates to *NHO or *NOH is still a matter of debate, which
makes it difficult to optimize catalysts for NOxelectroreduction. Here, “catalytic matrices” are used to swiftly extract features of active transition
metal catalysts for NO electroreduction. The matrices show that active
catalysts statistically stabilize *NHO over *NOH and have undercoordinated
sites. Besides, square-symmetry active sites with Cu and other elements
may prove active for NO electroreduction. Finally, multivariate regressions
are able to reproduce the main features found by the matrices, which
opens the door for more sophisticated machine-learning studies. In
sum, catalytic matrices may ease the analysis of complex electrocatalytic
reactions on multifaceted materials.