Molecular recognition is an attractive approach to designing sensitive and selective sensors for volatile organic compounds (VOCs). Although organic macrocycles and cages have been well-developed for recognising organics by their adaptive pockets in liquids, porous solids for gas detection require a deliberate design balancing adaptability and robustness. Here we report a dynamic 3D covalent organic framework (dynaCOF) constructed from an environmentally sensitive fluorophore that can undergo concerted and adaptive structural transitions upon adsorption of gas and vapours. The COF is capable of rapid and reliable detection of various VOCs, even for non-polar hydrocarbon gas under humid conditions. The adaptive guest inclusion amplifies the host-guest interactions and facilitates the differentiation of organic vapours by their polarity and sizes/shapes, and the covalently linked 3D interwoven networks ensure the robustness and coherency of the materials. The present result paves the way for multiplex fluorescence sensing of various VOCs with molecular-specific responses.
Metal-organic frameworks (MOFs) are promising nanoporous materials with diverse applications. Traditional material discovery based on intensive manual experiments has certain limitations on efficiency and effectiveness when faced with nearly infinite material space. The current situation offers an opportunity for high-throughput (HT) and machine learning (ML) approaches, including computational and experimental methods, as they have greatly improved the efficiency of MOF screening and discovery and have the capacity to deal with the enormous growth of data. In this review, we discuss the research progress in HT computation and experiments and their effect on MOF screening and discovery. We also highlight how ML-based approaches and the integration of HT methods with ML algorithms accelerate MOF design. In addition, we provide our insights on the future capability of data-driven techniques for MOF discovery, despite facing some knowledge gaps as an obstacle.
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