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Computational Design of 2D Covalent-Organic Framework Membranes for Organic Solvent Nanofiltration

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posted on 2018-11-27, 00:00 authored by Wan Wei, Jie Liu, Jianwen Jiang
Covalent-organic frameworks (COFs) are ordered crystalline materials covalently formed by building blocks of light elements. There has been increasing interest in the development of COF membranes for chemical separation. In this study, seven two-dimensional (2D) COF membranes are computationally designed with different functional groups and aperture sizes. By 245 sets of molecular simulations, the seven ultrathin COF membranes are investigated for organic solvent nanofiltration (OSN) of seven solvents (acetonitrile, acetone, methanol, ethanol, isopropyl alcohol, methyl ethyl ketone, and n-hexane) and four solutes (2,5-furandiamine, paracetamol, α-methylstyrene dimer, and Nile red). The solvent fluxes through the COF membranes are revealed to be governed by the aperture size and membrane functionality, as well as solvent properties. In general, the larger the aperture size, the higher is the flux. For membranes with comparable aperture size, the hydrophobic one exhibits higher fluxes than the hydrophilic counterpart for all the solvents except n-hexane. To elucidate this trend, solvent structures near the membranes are analyzed and the potentials of mean force for solvent permeation are evaluated. The solvent permeances through hydrophobic and hydrophilic membranes are correlated respectively with two different combinations of solvent properties. The solute rejection is found to depend on a complex interplay among solute size and polarity, solvent viscosity, solute–solvent interaction, aperture size and membrane functionality. In the presence of solutes, solvent permeances are reduced by approximately 10%. From the bottom-up, this comprehensive computational study provides quantitative insights into solvent permeation and solute rejection in the COF membranes, unravels the key governing factors, and would facilitate the development of new membranes for high-performance OSN.

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