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

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.