Queryable Gaseous Adsorption Properties of Pure Components and Mixtures in Metal-Organic Frameworks

Area of Science:

• Materials Science
• Computational Chemistry
• Chemical Engineering

Background:

• Grand Canonical Monte Carlo (GCMC) is standard for screening metal-organic frameworks (MOFs) for adsorption.
• GCMC simulations are limited to specific temperature and pressure conditions, hindering industrial application.
• A need exists for flexible adsorption prediction across diverse operational parameters.

Purpose of the Study:

• To develop a versatile adsorption database for MOFs using a novel macrostate-probability-distribution (MPD) approach.
• To enable prediction of gas adsorption under any pressure and temperature conditions.
• To facilitate efficient screening of MOFs for gas separation applications.

Main Methods:

• Developed an MPD-based adsorption database for ~2900 rigid MOF structures.
• Utilized flat histogram Monte Carlo to compute MPD, enabling analytical reweighting.
• Integrated Ideal Adsorbed Solution Theory (IAST) for multicomponent mixture isotherm predictions.

Main Results:

• The MPD-based database provides instant access to adsorption uptakes at user-defined conditions.
• Demonstrated rapid screening capabilities and identification of structure-performance relationships.
• Successfully predicted mixture adsorption behavior for key gases (CO2, N2, CH4, CO).

Conclusions:

• The MPD-based database offers a flexible and interactive tool for MOF discovery.
• This approach overcomes limitations of traditional GCMC for variable industrial conditions.
• Facilitates accelerated identification of optimal MOFs for gas adsorption and separation.