Abstract
Metal-organic frameworks (MOFs) based on the pressure swing adsorption (PSA) process show great promise in separating argon from air. As research burgeons, the number of MOFs has grown exponentially, rendering the experimental identification of materials with significant gas separation potential impractical. This study introduced a high-throughput screening through a two-step strategy based on structure-property relationships, which leveraged Grand Canonical Monte Carlo (GCMC) simulations, to swiftly and precisely identify high-performance MOF adsorbents capable of separating argon from air among a vast array of MOFs. Compared to traditional approaches for material development and screening, this method significantly reduced both experimental and computational resource requirements. This research pre-screened 12,020 experimental MOFs from a computationally ready experimental MOF (CoRE MOF) database down to 7328 and then selected 4083 promising candidates through structure-performance correlation. These MOFs underwent GCMC simulation assessments, showing superior adsorption performance to traditional molecular sieves. In addition, an in-depth discussion was conducted on the structural characteristics and metal atoms among the best-performing MOFs, as well as the effects of temperature, pressure, and real gas conditions on their adsorption properties. This work provides a new direction for synthesizing next-generation MOFs for efficient argon separation in labs, contributing to energy conservation and consumption reduction in the production of high-purity argon gas.