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

 Operando spectroscopy enables scientists to observe, in real time, the structural and chemical evolution of copper catalysts during CO₂ reduction, revealing how the surface dissolves, redeposits, and rearranges under reaction conditions. These observations uncover which atomic structures and electronic states favor the production of C₁ or C₂ products, and how properties such as oxophilicity or alloy compatibility shape the active sites. When these continuous spectroscopic measurements are combined with machine-learning analysis, the resulting large datasets can be probed for hidden correlations—like characteristic coordination patterns or dynamic phase transitions—that govern both catalyst stability and product selectivity. This integrated strategy allows researchers to rapidly predict optimal alloy formulations and operating parameters, speeding the rational development of copper-based catalysts that remain robust while achieving high selectivity in CO₂ electroreduction. 

The traditional method involves the use of catalysts and repeated trials. Now, there are two new approaches: 1. In-situ spectroscopy to observe how copper atoms rearrange and how the surface structure changes, in order to figure out which state is the most suitable. 2. Quickly identifying from a pile of experimental data which element combinations are more stable and which surface structure is more prone to generating ethanol rather than methane. This can significantly shorten the time from laboratory research to industrial application