Tracking the Evolution of Single-Atom Catalysts for the CO2 Electrocatalytic Reduction Using Operando X-ray Absorption Spectroscopy and Machine Learning

Transition metal-nitrogen-dopedcarbons (TMNCs) are a promisingclass of catalysts for the CO2 electrochemical reductionreaction. In particular, high CO2-to-CO conversion activitiesand selectivities were demonstrated for Ni-based TMNCs. Nonetheless,open questions remain about the nature, stability, and evolution ofthe Ni active sites during the reaction. In this work, we addressthis issue by combining operando X-ray absorption spectroscopy withadvanced data analysis. In particular, we show that the combinationof unsupervised and supervised machine learning approaches is ableto decipher the X-ray absorption near edge structure (XANES) of theTMNCs, disentangling the contributions of different metal sites coexistingin the working TMNC catalyst. Moreover, quantitative structural informationabout the local environment of active species, including their interactionwith adsorbates, has been obtained, shedding light on the complexdynamic mechanism of the CO2 electroreduction.