机器学习辅助研究用于膜式氢/氢分离的 MOF 的结构-性能相关性

IF 9.1 Q1 ENGINEERING, CHEMICAL Green Chemical Engineering Pub Date : 2024-02-01 DOI:10.1016/j.gce.2024.01.005
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引用次数: 0

摘要

利用膜技术分离 He/H2 在从天然气中提取 He 领域获得了极大的关注。该工艺面临的最大挑战之一是两种气体分子的动力学直径非常接近,导致膜的选择性较低。在本研究中,我们通过分子模拟和机器学习方法研究了用于 He/H2 分离的金属有机框架(MOF)膜的结构性能关系。通过分子模拟,我们从计算准备实验(CoRE)MOF 数据库中识别出了具有高分离性能的潜在 MOF 膜,并进一步阐明了以扩散为主导的机理。此外,还建立了基于随机森林(RF)的机器学习模型,以确定影响MOF膜He/H2分离性能的关键因素。结果表明,孔极限直径(PLD)和空隙率(φ)分别是决定膜选择性和氦渗透性的最重要物理特征。此外,为验证分子模拟结果,还进行了密度泛函理论(DFT)计算,结果表明孔表面的电负性原子可增强 He/H2 的扩散分离,这对提高膜的 He/H2 选择性至关重要。这项研究为在分子水平上设计和开发用于分离 He/H2 的新型 MOF 膜提供了有益的启示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Machine learning aided investigation on the structure-performance correlation of MOF for membrane-based He/H2 separation

The separation of He/H2 using membrane technology has gained significant interest in the field of He extraction from natural gas. One of the greatest challenges associated with this process is the extremely close kinetic diameters of the two gas molecules, resulting in low membrane selectivity. In this study, we investigated the structure-performance relationship of metal-organic framework (MOF) membranes for He/H2 separation through molecular simulations and machine learning approaches. By conducting molecular simulations, we identified the potential MOF membranes with high separation performance from the Computation-Ready Experimental (CoRE) MOF database, and the diffusion-dominated mechanism was further elucidated. Moreover, random forest (RF)-based machine learning models were established to identify the crucial factors influencing the He/H2 separation performance of MOF membranes. The pore limiting diameter (PLD) and void fraction (φ), are revealed as the most important physical features for determining the membrane selectivity and He permeability, respectively. Additionally, density functional theory (DFT) calculations were carried out to validate the molecular simulation results and suggested that the electronegative atoms on the pore surfaces can enhance the diffusion-based separation of He/H2, which is critical for improving the membrane selectivities of He/H2. This study offers useful insights for designing and developing novel MOF membranes for the separation of He/H2 at the molecular level.

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来源期刊
Green Chemical Engineering
Green Chemical Engineering Process Chemistry and Technology, Catalysis, Filtration and Separation
CiteScore
11.60
自引率
0.00%
发文量
58
审稿时长
51 days
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