Combined GCMC, MD, and DFT Approach for Unlocking the Performances of COFs for Methane Purification.

Covalent organic frameworks (COFs) are promising materials for gas storage and separation; however, the potential of COFs for separation of CH4 from industrially relevant gases such as H2, N2, and C2H6 is yet to be investigated. In this work, we followed a multiscale computational approach to unlock both the adsorption- and membrane-based CH4/H2, CH4/N2, and C2H6/CH4 separation potentials of 572 COFs by combining grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations and density functional theory (DFT) calculations. Adsorbent performance evaluation metrics of COFs, adsorption selectivity, working capacity, regenerability, and adsorbent performance score were calculated for separation of equimolar CH4/H2, CH4/N2, and C2H6/CH4 mixtures at vacuum swing adsorption (VSA) and pressure swing adsorption (PSA) conditions to identify the best-performing COFs for each mixture. Results showed that COFs could achieve selectivities of 2-85, 1-7, and 2-23 for PSA-based CH4/H2, CH4/N2, and C2H6/CH4 separations, respectively, outperforming conventional adsorbents such as zeolites and activated carbons for each mixture. Structure-performance relations revealed that COFs with pore sizes <10 Å are promising adsorbents for all mixtures. We identified the gas adsorption sites in the three top-performing COFs commonly identified for each mixture by DFT calculations and computed the binding strength of gases, which were found to be on the order of C2H6 > CH4 > N2 > H2, supporting the GCMC results. Nucleus-independent chemical shift (NICS) indexes of aromaticity for adsorption sites were calculated, and the results revealed that the degree of linker aromaticity could be a measure for the selection or design of highly alkane-selective COF adsorbents over N2 and H2. Finally, COF membranes were shown to achieve high H2 permeabilities, 4.57 × 103 -1.25 × 106 Barrer, and decent membrane selectivities, as high as 4.3, outperforming polymeric and MOF-based membranes for separation of H2 from CH4.

Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation.

The acceleration in design of new metal organic frameworks (MOFs) has led scientists to focus on high-throughput computational screening (HTCS) methods to quickly assess the promises of these fascinating materials in various applications. HTCS studies provide a massive amount of structural property and performance data for MOFs, which need to be further analyzed. Recent implementation of machine learning (ML), which is another growing field in research, to HTCS of MOFs has been very fruitful not only for revealing the hidden structure-performance relationships of materials but also for understanding their performance trends in different applications, specifically for gas storage and separation. In this review, we highlight the current state of the art in ML-assisted computational screening of MOFs for gas storage and separation and address both the opportunities and challenges that are emerging in this new field by emphasizing how merging of ML and MOF simulations can be useful.

Effect of Metal-Organic Framework (MOF) Database Selection on the Assessment of Gas Storage and Separation Potentials of MOFs.

Development of computation-ready metal-organic framework databases (MOF DBs) has accelerated high-throughput computational screening (HTCS) of materials to identify the best candidates for gas storage and separation. These DBs were constructed using structural curations to make MOFs directly usable for molecular simulations, which caused the same MOF to be reported with different structural features in different DBs. We examined thousands of common materials of the two recently updated, very widely used MOF DBs to reveal how structural discrepancies affect simulated CH4 , H2 , CO2 uptakes and CH4 /H2 separation performances of MOFs. Results showed that DB selection has a significant effect on the calculated gas uptakes and ideal selectivities of materials at low pressure. A detailed analysis on the curated structures was provided to isolate the critical elements of MOFs determining the gas uptakes. Identification of the top-performing materials for gas separation was shown to strongly depend on the DB used in simulations.