<scp> CO ₂ </scp> ‐Derived Architected Carbons for Sustainable Energy Conversion and Storage

ABSTRACT The rising atmospheric CO 2 concentration has intensified interest in technologies that couple carbon utilization with the production of high‐value functional materials. Direct conversion of captured CO 2 into carbon architectures offers a distinct route in which emissions are transformed into electrochemically active solids rather than molecular fuels or commodity chemicals. Unlike conventional carbons, CO 2 ‐derived carbons are generated through thermochemical, electrochemical, plasma‐assisted, and hybrid conversion pathways that reconstruct carbon frameworks from fully oxidized molecular feedstocks. These synthesis routes enable hierarchical porosity, tunable graphitic order, high defect densities, and heteroatom‐coordinated active sites, providing opportunities to engineer electronic structure and interfacial reactivity across multiple length scales. This review examines how synthesis conditions govern structural evolution and how these features dictate performance in electrocatalytic and electrochemical energy‐storage systems. Emphasis is placed on defect‐mediated active sites, heteroatom coordination, interfacial charge redistribution, and metal–carbon interactions that control oxygen reduction, oxygen evolution, and hydrogen evolution reactions. The roles of CO 2 ‐derived carbons in lithium‐ion, sodium‐ion, lithium–sulfur, and related battery chemistries, as well as electrochemical capacitors, are evaluated through their influence on ion transport, charge‐transfer kinetics, and storage mechanisms. Recent advances reveal that CO 2 conversion can encode functionality directly during synthesis, eliminating many post‐synthetic modification steps. However, significant barriers remain, including scalable manufacturing, deterministic defect control, long‐term stability, and rigorous environmental and economic assessment. Future progress will depend on integrating operando characterization, theory‐guided design, machine learning, and life‐cycle analysis to establish predictive design rules and accelerate deployment in sustainable energy technologies. image