Han Seunghee, Kim Jihan
Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Daejeon34141, Republic of Korea.
ACS Omega. 2023 Jan 18;8(4):4278-4284. doi: 10.1021/acsomega.2c07517. eCollection 2023 Jan 31.
Separation of ethane and ethylene is considered to be industrially important for various chemical processes, but their similarities make the process expensive. In this study, we integrated computational screening with machine learning to find optimal metal-organic frameworks (MOFs) with high ethane/ethylene selectivity. Using our algorithm, a hypothetical MOF structure with an ideal adsorption solution theory selectivity of 3.6 at 298 K and 1 bar was discovered. Furthermore, structural analysis was implemented, and the full adsorption isotherm of some of the top structures was obtained.
乙烷和乙烯的分离对于各种化学过程而言在工业上具有重要意义,但它们的相似性使得该过程成本高昂。在本研究中,我们将计算筛选与机器学习相结合,以寻找具有高乙烷/乙烯选择性的最佳金属有机框架(MOF)。使用我们的算法,发现了一种假设的MOF结构,其在298 K和1巴下的理想吸附溶液理论选择性为3.6。此外,还进行了结构分析,并获得了一些顶级结构的完整吸附等温线。