From metalloenzymes to catalysts: nature-inspired metal-based systems for CO₂ activation and conversion

The development of sustainable technologies for CO₂ utilization is a major challenge in modern chemistry. Nature provides remarkable examples of efficient CO₂ activation and transformation through metalloenzymes such as carbonic anhydrase (CA), which catalyzes the reversible hydration of CO₂ to bicarbonate (HCO₃^-) and biotin-dependent carboxylases, which activate HCO₃^- and incorporate it into organic substrates through ATP-driven carboxylation reactions. Other notable examples include the families of formate dehydrogenases (FDHs) and carbon monoxide dehydrogenases (CODHs), which catalyze the reversible reduction of CO₂ to formate (HCOO^-) and carbon monoxide (CO), respectively. These systems highlight the remarkable ability of metal-containing active sites not only to overcome the intrinsic stability of CO₂, but also to facilitate its capture, concentration, delivery to catalytic active sites and subsequent conversion under mild conditions. In this Feature Article, we review recent advances in nature-inspired metal-based systems for CO₂ utilization, focusing on how insights from metalloenzymes have guided the development of synthetic catalysts for CO₂ activation and conversion via cycloaddition reactions to produce cyclic carbonates (CCs), extending biomimetic principles to transformations not directly found in nature. Particular attention is given to sorbents and catalysts incorporating renewable ligands and earth-abundant metals, including green metal-organic frameworks (MOFs) and recent contributions from our laboratory involving ascorbate-, curcumin-, adenine- and biotin-derived systems. By examining these developments within the broader context of biomimetic and sustainable catalysis, we aim to identify current challenges and future opportunities for the design of next-generation CO₂ utilization technologies with particular emphasis on emerging roles of large language models (LLMs) and machine learning (ML) to accelerate research progress and enable predictive catalyst design.