Hypothetical yet effective: Computational identification of high-performing MOFs for CO2 capture

Abstract

With the advances in computational resources and algorithms, computer simulations are being increasingly used to tackle the most challenging problems of the world. Among them, CO2 capture is a topic that needs imminent attention as the presence of high levels of CO2 in the air can lead to drastic shifts in global climate. Here, a recently developed hypothetical metal-organic framework (MOF) database comprised of anion-pillared (AP) MOFs is computationally screened for the separation of CO2/CO, CO2/H2, and CO2/N2 gas mixtures at room temperature. The best performing MOFs are identified using three performance metrics, adsorption selectivity, working capacity, and regenerability, in conjunction. In these top materials, the preferential adsorption sites are illustrated, which will be useful in guiding the experimental design of new MOFs with extraordinarily high CO2 selectivities. The favorable separation performances of AP MOFs suggest that efficient gas separations can be conducted using MOFs without open metal sites.