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Advancing CO <sub>2</sub> Upgrading to Multicarbon Products: Synergies Between Photo/Electrochemical and Biological Conversion

Bo Sun, Chen Deng, Yifan Xu, Xiaoman He, Rongxin Xia, Wenlei Zhu, Huifeng Yao, Shangqian Zhu, Xihui Kang, Xin Tu, Dekui Shen, Jerry D. Murphy, Richen Lin

2025Advanced Functional Materials9 citationsDOI

Abstract

Abstract While photo‐ and electrochemical CO 2 reduction efficiently generate C 1 –C 2 molecules (e.g., formate, carbon monoxide, methane, ethylene), their limited selectivity and kinetic constraints impede direct C 2+ synthesis. Conversely, biological CO 2 fixation excels at producing multicarbon compounds (e.g., glucose, fatty acids, biopolymers) but requires energy‐intensive substrates. This Review explores the transformative potential of hybrid abiotic–biotic systems, where tailored C 1 –C 2 electron mediators synergize inorganic catalysis with biological conversion to enable scalable C 2+ production. We critically evaluate: i) recent breakthroughs in photo/electrocatalyst design, reactor engineering, and mechanistic control of C 1 –C 2 production; ii) engineered microbial and enzymatic pathways (autotrophic, mixotrophic, and synthetic) that optimize carbon flux toward C 2+ targets; and iii) integrated system architectures (in situ and spatially segregated), emphasizing mediator biocompatibility, mass‐transfer kinetics, and reactor scalability. A focused analysis highlights paired anodic processes (e.g., biomass oxidation) as energy‐efficient alternatives to the oxygen evolution reaction. Techno‐economic and life‐cycle assessments identify key bottlenecks, including mediator toxicity, system integration, and anodic byproduct valorization. By synthesizing interdisciplinary progress, this work identifies pathways to advance C 2+ production and establishes a roadmap for next‐generation CO 2 upgrading technologies.

Topics & Concepts

Materials scienceElectrochemistryNanotechnologyElectrodePhysical chemistryChemistryCO2 Reduction Techniques and CatalystsAdvanced Photocatalysis TechniquesMachine Learning in Materials Science