Toward the inverse design of MOF membranes for efficient D2/H2 separation by combination of physics-based and data-driven modeling
Toward the inverse design of MOF membranes for efficient D2/H2 separation by combination of physics-based and data-driven modeling
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DOI：
10.1016/j.memsci.2019.117675
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发表时间：
2020-03
影响因子：
9.5
通讯作者：
Musen Zhou;A. Vassallo;Jianzhong Wu
中科院分区：
文献类型：
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作者：
Musen Zhou;A. Vassallo;Jianzhong Wu
氢同位素对科学研究、能源生产和医疗都很有用。然而，它们的工业生产是昂贵的，因为用于分离氢同位素体的常规方法大多基于能量密集的宏观程序，分离效率极低。金属有机骨架（MOFs）通过利用其明确的化学和结构特征提供了一种有前途的D2/H2分离途径。在这项工作中，我们报告了12,723个实验合成的MOF膜D2/H2分离的高通量筛选，通过预测气体吸附和传输性能支撑的分离效率。引入膜性能评分以鉴定具有最佳选择性和容量的排名靠前的M0 F。从基于物理的建模中生成的大量数据使得机器学习方法能够应用于预测新型纳米多孔材料的理想特征，以更有效地分离氢同位素。
Hydrogen isotopes are useful for scientific research, energy generation and medical treatment. However, their industrial production is expensive because conventional processes for separation of hydrogen isotopologues are mostly based on energy-intensive macroscopic procedures with extremely low separation efficiency. Metal-organic frameworks (MOFs) provide a promising route to D2/H2separation by leveraging their well-defined chemical and structural features. In this work, we report high-throughput screening of 12,723 experimentally synthesizable MOF membranes for D2/H2separation by predicting gas adsorption and transport properties underpinning the separation efficiency. A membrane performance score is introduced to identify top ranked MOFs with the best selectivity and capacity. The extensive data generated from the physics-based modeling enables application of machine learning methods to predict desirable features of novel nanoporous materials for more efficient separation of hydrogen isotopes.