Microporous Metal–Organic Framework with Cage-within-Cage Structures for Xenon/Krypton Separation

Metal-Organic Frameworks Possessing Suitable Pores for Xe/Kr Separation

Adsorption separation of the Xe/Kr mixture remains a tough issue since Xe and Kr have an inert nature and similar sizes. Here we present a chlorinated metal-organic framework (MOF) [JXNU-19(Cl)] and its nonchlorinated analogue (JXNU-19) for Xe/Kr separation. The two isostructural MOFs constructed from the heptanuclear cobalt-hydroxyl clusters bridged by organic ligands are three-dimensional structures. Detailed contrast of the Xe/Kr adsorption separation properties of the MOF shows that significantly enhanced Xe uptakes and Xe/Kr adsorption selectivity (17.1) are observed for JXNU-19 as compared to JXNU-19(Cl). The main binding sites for Xe in the MOF revealed by computational simulations are far away from the chlorine sites, suggesting that the introduction of the chlorine groups results in the unfavorable Xe adsorption for JXNU-19(Cl). The optimal pores, high surface area, and multiple strong Xe-framework interactions facilitate the effective Xe/Kr separation for JXNU-19.

Prediction of Xe/Kr Separation in Metal-Organic Frameworks by a Precursor-Based Neural Network Synergistic with a Polarizable Adsorbate Model

Adsorption and separation of Xe/Kr are significant for making high-density nuclear energy environmentally friendly and for meeting the requirements of the gas industry. Enhancing the accuracy of the adsorbate model for describing the adsorption behaviors of Xe and Kr in MOFs and the efficiency of the model for predicting the separation potential (SP) value of Xe/Kr separation in MOFs helps in searching for promising MOFs for Xe/Kr adsorption and separation within a short time and at a low cost. In this work, polarizable and transferable models for mimic Xe and Kr adsorption behaviors in MOFs were constructed. Using these models, SP values of 38 MOFs at various temperatures and pressures were calculated. An optimal neural network model called BPNN-SP was designed to predict SP value based on physical parameters of metal center (electronegativity and radius) and organic linker (three-dimensional size and polarizability) combined with temperature and pressure. The regression coefficient value of the BPNN-SP model for each data set is higher than 0.995. MAE, MBE, and RMSE of BPNN-SP are only 0.331, -0.002, and 0.505 mmol/g, respectively. Finally, BPNN-SP was validated by experiment data from six MOFs. The transferable adsorbate model combined with the BPNN-SP model would highly improve the efficiency for designing MOFs with high performance for Xe/Kr adsorption and separation.