How can operando spectroscopy combined with machine learning accelerate the rational design of stable copper-based catalysts for selective CO₂ electroreduction?

 Recent work has shown that selective dissolution–redeposition, influenced by oxophilicity and miscibility, reshapes copper bimetallic active sites and drives C₁/C₂ selectivity shifts. Integrating operando characterization with machine-learning-guided design could reveal hidden descriptors and accelerate discovery of highly selective, durable CO₂ reduction catalysts.