Multi-Level Computational Screening of in Silico Designed MOFs for Efficient SO2 Capture.

Abstract

SO2 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 SO2 emissions can be developed. Herein, a large database of hypothetical MOFs composed of >300,000 materials is screened for SO2/CH4, SO2/CO2, and SO2/N2 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 SO2/CH4 selectivities of 62.4–16899.7, SO2 working capacities of 0.3–20.1 mol/kg, and SO2 regenerabilities of 5.8–98.5%. Regarding SO2/CO2 separation, they possess SO2/CO2 selectivities of 13.3–367.2, SO2 working capacities of 0.1–17.7 mol/kg, and SO2 regenerabilities of 1.9–98.2%. For the SO2/N2 separation, their SO2/N2 selectivities, SO2 working capacities, and SO2 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.