High-Throughput Computational Screening of the Metal Organic Framework Database for CH₄/H₂ Separations

Area of Science:

• Materials Science
• Chemical Engineering
• Computational Chemistry

Background:

• Metal-Organic Frameworks (MOFs) are highly porous materials with tunable properties.
• Their large surface areas and pore volumes make them promising for gas separation applications.
• Experimental screening of numerous MOFs for specific gas separations is impractical due to their rapidly growing number.

Purpose of the Study:

• To computationally screen the largest available collection of MOFs for methane/hydrogen (CH4/H2) separation performance.
• To identify MOFs with superior CH4/H2 selectivity and capacity compared to existing adsorbents.
• To establish structure-property relationships and develop a predictive model for MOF-based CH4/H2 separation.

Main Methods:

• Utilized molecular simulations to screen 4350 MOFs from the Cambridge Structural Database.
• Evaluated MOFs using metrics such as selectivity, working capacity, and regenerability.
• Analyzed correlations between MOF structural properties (pore size, surface area) and adsorption characteristics (heat of adsorption, metal type) with separation performance.

Main Results:

• Identified a significant number of MOFs exhibiting superior CH4/H2 selectivity over traditional materials like zeolites and activated carbons.
• Established clear relationships between MOF structural features and their CH4/H2 separation capabilities.
• Developed a predictive mathematical model for estimating MOF CH4/H2 selectivity.

Conclusions:

• Computational screening is essential for identifying promising MOFs for gas separation.
• MOFs offer extraordinary potential for efficient CH4/H2 separation.
• The developed model can guide the rational design of novel MOFs with enhanced separation performance.