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

Area of Science:

• Materials Science
• Chemical Engineering
• Environmental Science

Background:

• Sulfur dioxide (SO2) emissions harm human health and the environment, contributing to acid rain and smog.
• Adsorption-based separation using metal-organic frameworks (MOFs) offers a promising route for cost-effective SO2 capture.
• Developing efficient MOFs is crucial for mitigating SO2 pollution.

Purpose of the Study:

• To computationally screen a large database of hypothetical MOFs for SO2 separation from methane (CH4), carbon dioxide (CO2), and nitrogen (N2).
• To identify optimal MOF materials and functional groups for SO2/CH4, SO2/CO2, and SO2/N2 separations based on selectivity, working capacity, and regenerability.
• To establish performance limits of MOFs for SO2 capture based on their functional groups.

Main Methods:

• Utilized a multi-level computational approach to screen over 300,000 hypothetical MOFs.
• Evaluated MOF performance using key metrics: adsorption selectivity, working capacity, and regenerability.
• Analyzed the relationship between MOF functional groups and their SO2 separation performance.

Main Results:

• Identified top-performing MOFs for SO2/CH4, SO2/CO2, and SO2/N2 separations with high selectivities and working capacities.
• Achieved SO2/CH4 selectivities ranging from 62.4 to 16899.7, SO2/CO2 selectivities from 13.3 to 367.2, and SO2/N2 selectivities from 137.9 to 67,338.9.
• Determined SO2 working capacities between 0.1-20.6 mol/kg and regenerabilities between 1.9-98.6% across different separation pairs.

Conclusions:

• Specific MOFs and functional groups show significant potential for effective SO2 capture.
• Computational screening provides a powerful tool for discovering novel materials for environmental remediation.
• Understanding functional group contributions can guide the design of next-generation SO2 adsorbents.