Covalent organic frameworks (COFs) have emerged as a novel platform for material design and functional explorations, but it remains a challenge to synthetically functionalize targeted structures for task-specific applications. Optically pure 1,1′-bi-2-naphthol (BINOL) is one of the most important sources of chirality for organic synthesis and materials science, but it has not yet been used in construction of COFs for enantioselective processes. Here, by elaborately designing and choosing an enantiopure BINOL-based linear dialdehyde and a tris­(4-aminophenyl)­benzene derivative or tetrakis­(4-aminophenyl)­ethene as building blocks, two imine-linked chiral fluorescent COFs with a 2D layered hexagonal or tetragonal structure are prepared. The COF containing flexible tetraphenylethylene units can be readily exfoliated into ultrathin 2D nanosheets and electrospun to make free-standing nanofiber membrane. In both the solution and membrane systems, the fluorescence of COF nanosheets can be effectively quenched by chiral odor vapors via supramolecular interactions with the immobilized BINOL moieties, leading to remarkable chiral vapor sensors. Compared to the BINOL-based homogeneous and membrane systems, the COF nanosheets exhibited greatly enhanced sensitivity and enantioselectivity owing to the confinement effect and the conformational rigidity of the sensing BINOL groups in the framework. The ability to place such a useful BINOL chiral auxiliary inside open channels of COFs capable of amplifying chiral discrimination of the analytes represents a major step toward the rational synthesis of porous molecular materials for more chirality applications.

A computational study is reported to screen metal–organic frameworks for H2S and CO2 separation from natural gas under humid conditions.

Cascade biocatalysis via intracellular epoxidation and hydrolysis was developed as a green and efficient method for enantioselective dihydroxylation of aryl olefins to prepare chiral vicinal diols in high ee and high yield. Escherichia coli (SSP1) coexpressing styrene monooxygenase (SMO) and epoxide hydrolase SpEH was developed as a simple and efficient biocatalyst for S-enantioselective dihydroxylation of terminal aryl olefins 1a−15a to give (S)-vicinal diols 1c−15c in high ee (97.5−98.6% for 10 diols; 92.2−93.9% for 3 diols) and high yield (91−99% for 6 diols; 86−88% for 2 diols; 67% for 3 diols). Combining SMO and epoxide hydrolase StEH showing complementary regioselectivity to SpEH as a biocatalyst for the cascade biocatalysis gave rise to R-enantioselective dihydroxylation of aryl olefins, being the first example of this kind of reversing the overall enantioselectivity of cascade biocatalysis. E. coli (SST1) coexpressing SMO and StEH was also engineered as a green and efficient biocatalyst for R-dihydroxylation of terminal aryl olefins 1a−15a to give (R)-vicinal diols 1c−15c in high ee (94.2−98.2% for 7 diols; 84.2−89.9% for 6 diols) and high yield (90−99% for 6 diols; 85−89% for 5 diols; 65% for 1 diol). E. coli (SSP1) and E. coli (SST1) catalyzed the trans-dihydroxylation of trans-aryl olefin 16a and cis-aryl olefin 17a with excellent and complementary stereoselectivity, giving each of the four stereoisomers of 1-phenyl-1,2-propanediol 16c in high ee and de, respectively. Both strains catalyzed the trans-dihydroxylation of aryl cyclic olefins 18a and 19a to afford the same trans-cyclic diols (1R,2R)-18c and (1R,2R)-19c, respectively, in excellent ee and de. This type of cascade biocatalysis provides a tool that is complementary to Sharpless dihydroxylation, accepting cis-alkene and offering enantioselective trans-dihydroxylation.

At present, 100 000+ metal–organic frameworks (MOFs) have been synthesized, and it is challenging to identity the best candidate for a specific application. In this study, MOFs are rapidly screened via a hierarchical approach for propane/propylene (C3H8/C3H6) separation. First, the adsorption capacity and selectivity of C3H8/C3H6 mixture in “Computation-Ready, Experimental” (CoRE) MOFs are predicted via a molecular simulation (MS) method. The relationships between separation metrics and structural factors are established, and top-performing CoRE MOFs are identified. Then, machine learning (ML) models are trained and developed upon the CoRE MOFs using pore size, pore geometry, and framework chemistry as feature descriptors. By introducing binned pore size distributions and geometric descriptors, the accuracy of ML models is substantially improved. The feature importance of the descriptors is physically interpreted by the Gini impurities and Shapley Additive Explanations. Subsequently, the ML models are used to rapidly screen experimental “Cambridge Structural Database” (CSD) MOFs and hypothetical MOFs for C3H8/C3H6 separation. In the CSD MOFs, the out-of-sample predictions are found to agree well with simulation results, demonstrating the excellent transferability of the ML models from the CoRE to CSD MOFs. Moreover, nine CSD MOFs are identified to possess separation performance superior to top-performing CoRE MOFs. Finally, the similarity and diversity among experimental and hypothetical MOFs are visualized and compared by the t-Distributed Stochastic Neighbor Embedding (t-SNE) feature projections. Remarkably, the CoRE and CSD MOFs are revealed to share a close similarity in both chemical and geometric feature spaces. By synergizing MS and ML, the hierarchical approach developed in this study would advance the rapid screening of MOFs across different databases toward industrially important separation processes.

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