463497 High-Throughput Screening of Metal-Organic Frameworks for CO2 Capture at High Humidity

Tuesday, November 15, 2016
Grand Ballroom B (Hilton San Francisco Union Square)
Song Li, Department of New Energy and Science and Engineering, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan, China, Randall Q. Snurr, Chemical and Biological Engineering, Northwestern University, Evanston, IL and Yongchul G. Chung, Chemical & Biological Engineering, Pusan National University, Busan, Korea, The Republic of

One of the main challenges for the development of technology to capture CO2 from power plant flue gas is to find or create materials with high selectivity for CO2 at high humidity. Metal-organic frameworks (MOFs) have gained significant attention due to their excellent performance in gas adsorption and separation. However, water adsorption in MOFs is generally detrimental to CO2 separation, and the removal of water content from flue gas prior to CO2 capture would add to the overall cost. In this study, the CoRE (Computation-Ready, Experimental) MOF database of existing MOF structures has been computationally screened to find MOF candidates that are highly selective toward CO2 over H2O using a hierarchical computational screening approach. MOF candidates were selected based on the ratio of Henry’s constants of CO2 over H2O computed via Widom insertion method, corresponding to the selectivity of CO2 over H2O, and the adsorption isotherms of selected MOFs with high selectivities were assessed using more accurate partial charges from density function theory (DFT) calculations by grand canonical Monte Carlo (GCMC) simulation. Finally, the top MOF candidates with high CO2/H2O selectivity were identified. Decomposition of interaction energy between MOF and adsorbate show that the electrostatic interaction contributes to the majority of the adsorption energy of H2O in these selected MOFs. This work shows a path for the discovery of adsorbent materials for CO2 capture at high humidity by large-scale computational screening.

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