Xu Xiaoyi, Xin Bingru, Dai Zhongde, Liu Chong, Zhou Li, Ji Xu, Dai Yiyang
School of Chemical Engineering, Sichuan University, Chengdu 610065, China.
School of Carbon Neutrality Future Technology, Sichuan University, Chengdu 610065, China.
Nanomaterials (Basel). 2025 Mar 7;15(6):412. doi: 10.3390/nano15060412.
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.
基于变压吸附（PSA）工艺的金属有机框架材料（MOF）在从空气中分离氩气方面显示出巨大潜力。随着研究的迅速发展，MOF的数量呈指数级增长，使得通过实验鉴定具有显著气体分离潜力的材料变得不切实际。本研究基于结构-性能关系，通过两步策略引入了高通量筛选方法，利用巨正则蒙特卡罗（GCMC）模拟，在大量MOF中快速准确地识别出能够从空气中分离氩气的高性能MOF吸附剂。与传统的材料开发和筛选方法相比，该方法显著降低了实验和计算资源需求。本研究从一个可计算的实验MOF（CoRE MOF）数据库中对12020种实验性MOF进行预筛选，筛选至7328种，然后通过结构-性能相关性选择了4083种有前景的候选材料。这些MOF经过GCMC模拟评估，显示出比传统分子筛更好的吸附性能。此外，还对性能最佳的MOF的结构特征和金属原子，以及温度、压力和实际气体条件对其吸附性能的影响进行了深入讨论。这项工作为实验室合成用于高效氩气分离的下一代MOF提供了新方向，有助于高纯度氩气生产中的节能和降耗。
