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Multivariable Sieving and Hierarchical Recognition for Organic Toxics in Nonhomogeneous Channel of MOFs

Abstract : Developing optimal molecular sieves, which are able to perform multivariable aqueous sieving of diverse harmful organics, is of practical significance in chemistry and environmental protection. Current porous sieves, however, can only recognize organics on the basis of two variables (charge and size), which is far from enough because of the multi-complexity of the toxics. Herein, we report a series of isostructural metal-organic frameworks, MIL-140s [ZrO(O 2C–R–CO 2)], with triangular hydrophobic channels for the separation of dyes and practical toxics, such as high carcinogenic pesticides and persistent organic pollutants. Theoretical calculations demonstrate the key role of nonhomogeneous electron distribution within channels for recognition of various guest molecules. Multivariable sieving for both dyes and practical toxics has been validated by both separation experiments and DFT calculation. The sieving mechanisms are revealed through presenting an outside-in theoretical model, hierarchical recognition, by the regulation of surface charge, pore size, and potential energy surface of MIL-140s.
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Qi Zhang, Mohammad Wahiduzzaman, Sujing Wang, Stefan Henfling, Narjes Ayoub, et al.. Multivariable Sieving and Hierarchical Recognition for Organic Toxics in Nonhomogeneous Channel of MOFs. Chem, Cell Press, 2019, 5 (5), pp.1337-1350. ⟨10.1016/j.chempr.2019.03.024⟩. ⟨hal-02152466⟩

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