This review demonstrates energy demands reduction in electrolysis systems, highlighting machine learning's role in reactor designs and co-production potential.
Paired electrolysis represents a paradigm shift in overcoming the energy intensity of conventional CO2 electrolysis. By supplanting the oxygen evolution reaction with value-added anodic oxidations, this technology simultaneously slashes energy demands and diversifies output. This review critically synthesizes design principles for pairing these anodic reactions with two key cathode pathways, namely CO2 electroreduction and electrocarboxylation, supported by an analysis of pioneering systems from the past three years. We further explore advanced reactor designs for co-production, and the pivotal role of operando characterization and machine learning in unraveling complex mechanisms. We conclude with a perspective on the key challenges and pathways to industrial adoption, aiming to bridge fundamental advances with practical implementation.
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Zhai et al. (2025) studied this question.
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