Chiral metal/covalent-organic frameworks for enantiomer recognition: mechanistic insights, multi-scenario applications and device integration

Abstract The demand for enantiopure substances in the pharmaceutical, agricultural, and food industries is continuously increasing, while traditional chiral separation/recognition technologies still face limitations such as high costs and limited selectivity. Chiral metal-organic frameworks (CMOFs) and chiral covalent-organic frameworks (CCOFs) have emerged as new platforms integrating molecular recognition, enantioselective separation, and multimodal signal transduction, showing broad application prospects. This article systematically reviews the construction strategies of CMOFs and CCOFs, including direct synthesis, chiral induction, post-synthetic modification/exchange, spontaneous resolution, and defect engineering. It further elucidates the enantiomer recognition mechanisms from four dimensions: pore confinement-stereochemical matching, multiple non-covalent interactions, coordination-orbital energy synergy, and framework dynamics-induced fit. The discussion further encompasses various signal transduction mechanisms (optical, electrochemical, and mass-sensitive) that empower high-selectivity recognition within the complex matrices. The key roles of DFT, molecular dynamics, Hirshfeld surface analysis, and machine learning in mechanism revelation and performance prediction are highlighted. It also presents application examples of CMOFs and CCOFs in various scenarios, such as chiral amino acids, alcohols, amines, small-molecule drugs and macromolecules, bioactive molecules and metabolites, chiral pesticides and chiral flavors. These examples demonstrate their high-selectivity recognition capabilities in complex samples. Finally, device integrations like mixed-matrix membranes, chip-level SURMOF/QCM arrays, and AI-assisted portable detectors are assessed. Challenges regarding material stability, scalable synthesis, and cost control are identified, with an outlook on green manufacturing and high-throughput computing synergistically advancing these materials from lab to field detection and industrial separation.