Publication:
Multi-scale computational screening to accelerate discovery of IL/COF composites for CO2/N-2 separation

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Publication Date

2022

Language

English

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Journal Article

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Abstract

Covalent organic frameworks (COFs) have emerged as novel adsorbents and membranes for gas separation. Incorporation of ionic liquids (ILs) into COFs is important to exceed the current performance limits of COFs. However, synthesis and testing of a nearly unlimited number of IL/COF combinations are simply impractical. Herein, we used a multi-scale computational screening approach combining COnductor-like Screening MOdel for Realistic Solvents (COSMO-RS) method, Grand Canonical Monte Carlo (GCMC), molecular dynamics (MD) simulations, and density functional theory (DFT) calculations to unlock both the adsorption-and membrane based CO2/N-2 separation performances of IL/COF composites. Several adsorbent and membrane performance assessment metrics including selectivity, working capacity, regenerability, adsorbent performance score, and permeability were computed. Our results revealed that IL incorporation into COFs significantly improves CO2/N-2 adsorption selectivities (from 12 to 26) and adsorbent performance scores (from 3.7 to 12 mol/kg). By performing DFT calculations, the nature of the interactions between CO2, N-2, COFs, and their IL-incorporated composites was evaluated. The high CO2 selectivity of IL/COF composites was attributed to the cooperative intermolecular effects induced by the COF and the IL. Finally, IL/COF membranes were studied, and results showed that they achieve significantly higher CO2 permeabilities (2.4 x 10(4)-9.4 x 10(5) Barrer) than polymeric and zeolite membranes with comparable selectivities (up to 15.7). This shows a great promise of IL/COF composites to replace the conventional membrane materials for flue gas separation. Our results will be useful in accelerating the experimental efforts to design new IL/COF composites that can achieve high-performance CO2 separation.

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Separation and Purification Technology

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Elsevier

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Engineering

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