2D materials with rational design: Advancing electro- and photocatalytic CO2 reduction

CO 2 conversion is central to closing the anthropogenic carbon cycle. However, practical CO 2 RR remains limited by slow molecular activation, competing hydrogen evolution, and dynamic catalyst evolution under operating conditions. Two-dimensional (2D) materials are well suited to address these limitations because their exposed surfaces, short transport pathways, and programmable interfaces allow active sites, charge distribution, local coordination environments, and reaction microenvironments to be tuned with unusual precision. This Review discusses recent advances in 2D-material-based CO 2 RR catalysts across representative families, including graphene, graphitic carbon nitrides (g-C 3 N 4 ), hexagonal boron nitride ( h -BN), 2D transition metal oxides (2D TMOs), 2D transition metal chalcogenides (2D TMDs), layered double hydroxides (LDHs), 2D metal-organic frameworks (2D MOFs), 2D transition metal carbides/nitrides/carbonitrides (MXenes), 2D transition-metal borides (MBenes), phosphorene and borophene. Beyond treating these materials only as separate catalyst classes, the discussion emphasizes cross-cutting design principles from a coordination-chemistry perspective, including defect and doping engineering, surface termination control, interlayer confinement, heterojunction construction, metal-support coordination in isolated single- and dual-atom sites, and composite or tandem architectures. Particular attention is paid to how coordination environments, such as metal-ligand nodes in 2D MOFs, metal‑oxygen motifs in LDHs and TMOs, surface terminations in MXenes, and M–N x sites on 2D supports, regulate CO 2 activation, intermediate binding, charge redistribution, and product selectivity. Photoelectrocatalytic integration, in situ / operando characterization, and Artificial-Intelligence (AI) assisted discovery are further highlighted as emerging directions for shifting 2D CO 2 RR catalyst design from static material optimization toward working-state coordination-environment regulation and descriptor-guided prediction.