Multi-Level Computational Screening of in Silico Designed MOFs for Efficient SO 2 Capture.

SO 2 presence in the atmosphere can cause significant harm to the human and environment through acid rain and/or smog formation. Combining the operational advantages of adsorption-based separation and diverse nature of metal-organic frameworks (MOFs), cost-effective separation processes for SO 2 emissions can be developed. Herein, a large database of hypothetical MOFs composed of >300,000 materials is screened for SO 2 /CH 4 , SO 2 /CO 2 , and SO 2 /N 2 separations using a multi-level computational approach. Based on a combination of separation performance metrics (adsorption selectivity, working capacity, and regenerability), the best materials and the most common functional groups in those most promising materials are identified for each separation. The top bare MOFs and their functionalized variants are determined to attain SO 2 /CH 4 selectivities of 62.4-16899.7, SO 2 working capacities of 0.3-20.1 mol/kg, and SO 2 regenerabilities of 5.8-98.5%. Regarding SO 2 /CO 2 separation, they possess SO 2 /CO 2 selectivities of 13.3-367.2, SO 2 working capacities of 0.1-17.7 mol/kg, and SO 2 regenerabilities of 1.9-98.2%. For the SO 2 /N 2 separation, their SO 2 /N 2 selectivities, SO 2 working capacities, and SO 2 regenerabilities span the ranges of 137.9-67,338.9, 0.4-20.6 mol/kg, and 7.0-98.6%, respectively. Besides, using breakdowns of gas separation performances of MOFs into functional groups, separation performance limits of MOFs based on functional groups are identified where bare MOFs (MOFs with multiple functional groups) tend to show the smallest (largest) spreads.