面向乙烷/乙烯分离的金属有机框架膜的大规模计算筛选

doi:10.6023/A22040186

摘要/Abstract

相比于传统热驱动的低温蒸馏工艺, 基于金属有机框架(Metal-organic frameworks, MOFs)的膜分离是一种在技术和成本上可行的乙烷/乙烯分离替代方案. 为了加速MOF膜在这一气体分离领域中的应用, 本工作提出了两步筛选策略对12,020个真实MOF膜材料进行了大规模计算筛选, 其中MISQIQ04表现出最高的乙烷/乙烯膜选择系数(4.16)和较高的乙烷渗透率(4.35×10⁵ Barrer). 通过结构-性能关系分析, 可以发现窄孔径和低孔隙率的MOFs是选择性分离乙烷的最佳膜材料, 并且乙烷的选择性吸附对乙烷/乙烯膜分离过程起着主导作用. 与传统计算筛选方法相比, 本工作所提出的筛选策略降低了约87.1%的计算时间成本. 为了进一步缩短模拟时间, 本工作还开发了机器学习分类模型以实现对高性能MOF膜的快速预筛选并探讨了该模型的可移植性. 结果表明, 增加数据集的多样性有助于提高所开发模型的可移植性和泛化能力.

关键词: 乙烷/乙烯分离, 金属有机框架膜, 分子模拟, 结构-性能关系, 机器学习

Compared to the traditional heat-driven cryogenic distillation process, the membrane separation based on metal- organic frameworks (MOFs) is a technically and economically viable alternative for ethane/ethylene (C₂H₆/C₂H₄) separation. To accelerate the application of MOF membranes in this gas separation field, this study performed a large-scale computational screening of 12,020 real MOFs for the identification of optimal C₂H₆-selective MOF membrane materials. According to geometric and chemical analyses, 2,192 MOFs without open metal sites and having pore limiting diameter no less than 0.38 nm were first screened out. Grand canonical Monte Carlo and molecular dynamics simulations were subsequently carried out to mimic the adsorption and diffusion behaviors of ethane and ethylene in these MOFs respectively, based on which their C₂H₆/C₂H₄ membrane selectivities and C₂H₆ permeabilities were estimated. The results showed that MISQIQ04 exhibited the highest C₂H₆/C₂H₄ membrane selectivity (4.16) and moderate C₂H₆ permeability (4.35×10⁵ Barrer). Additionally, structure- performance relationships between the C₂H₆/C₂H₄ membrane selectivity and structural properties of MOFs were investigated, covering the largest cavity diameter (LCD), pore limiting diameter (PLD), density (ρ), gravimetric surface area (GSA), void fraction (VF), and pore volume (PV). The results indicated that MOFs with the structural characteristics of 0.4 nm≤LCD≤1 nm, 0.38 nm≤PLD≤0.75 nm, 0.8 g/cm³≤ρ≤2.5 g/cm³, GSA≤1,700 m²/g, 0.3≤VF≤0.73, and PV≤0.85 cm³/g are optimal membrane materials for C₂H₆/C₂H₄ separation. Finally, a machine learning (ML) classifier was developed to achieve rapid prescreening of high-performing MOF membranes from a large MOF database, whose transferability was discussed on a hypothetical MOF database. Further t-Distributed Stochastic Neighbor Embedding analysis revealed that the ML model developed merely relying on a single MOF dataset generally exhibited poor transferability. Selecting the most representative and diverse MOFs from the entire MOF space for model development can help to improve the transferability and generalization ability of the developed model.

Key words: ethane/ethylene separation, metal-organic framework membrane, molecular simulation, structure-performance relationship, machine learning

引用此文

程敏, 王诗慧, 罗磊, 周利, 毕可鑫, 戴一阳, 吉旭. 面向乙烷/乙烯分离的金属有机框架膜的大规模计算筛选[J]. 化学学报, 2022, 80(9): 1277-1288.

Min Cheng, Shihui Wang, Lei Luo, Li Zhou, Kexin Bi, Yiyang Dai, Xu Ji. Large-Scale Computational Screening of Metal-Organic Framework Membranes for Ethane/Ethylene Separation[J]. Acta Chimica Sinica, 2022, 80(9): 1277-1288.

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图/表 8

图1. 筛选工作流程

Figure 1. Screening workflow used in this study

图2. 无限稀释条件下乙烷和乙烯在MOFs中的吸附和扩散性质的比较. (a) 乙烷和乙烯的亨利系数的比较. (b) 乙烷和乙烯的自扩散系数的比较. (c) 乙烷的亨利系数和自扩散系数的比较. (d) 乙烯的亨利系数和自扩散系数的比较

Figure 2. Comparisons of adsorption and diffusion properties of ethane and ethylene in MOFs at infinite dilution condition. (a) Comparison of Henry coefficients of ethane and ethylene. (b) Comparison of self-diffusion coefficients of ethane and ethylene. (c) Comparison of the Henry coefficient and self-diffusion coefficient of ethane. (d) Comparison of the Henry coefficient and self-diffusion coefficient of ethylene

图3. 无限稀释条件下MOF膜的分离性能. (a) 乙烷/乙烯膜选择系数与乙烷/乙烯吸附选择系数的比较. 图中每个MOF由乙烷/乙烯扩散选择系数进行颜色编码. (b) 乙烷/乙烯膜选择系数与乙烷渗透率的比较

Figure 3. Separation performances of MOF membranes at infinite dilution condition. (a) Comparison of the C2H6/C2H4 membrane selectivity and C2H6/C2H4 adsorption selectivity. Each MOF in this subfigure is color-coded by the corresponding C2H6/C2H4 diffusion selectivity. (b) Comparison of the C2H6/C2H4 membrane selectivity and C2H6 permeability

图4. 无限稀释条件和二元混合物条件下三种选择系数的比较

Figure 4. Comparison of three selectivities of MOFs produced at infinite dilution and binary mixture conditions

MOF	LCD/nm	PLD/nm	VF	$N_{C_{2}H_{6}}^{B}$/(mol•kg^-1)	$D_{C_{2}H_{6}}^{B}$/(m²•s^-1)	$S_{ads,C_{2}H_{6}/C_{2}H_{4}}^{B}$	$S_{mem,C_{2}H_{6}/C_{2}H_{4}}^{B}$	$P_{C_{2}H_{6}}^{B}$/Barrer
MISQIQ04	0.424	0.408	0.37	0.67	2.77×10^-9	3.79	4.16	4.35×10⁵
LURRUM	0.847	0.738	0.60	1.30	3.09×10^-10	2.55	3.67	1.11×10⁵
UVINAP	0.433	0.386	0.44	0.15	2.61×10^-8	1.32	3.39	6.49×10⁵
SUSZIQ	0.629	0.533	0.43	0.42	2.96×10^-10	2.43	3.32	3.06×10⁴
ZERQOE	0.450	0.403	0.34	0.26	5.43×10^-9	1.85	2.86	2.24×10⁵
QUXRIM	0.475	0.431	0.34	0.32	2.54×10^-9	2.11	2.63	1.30×10⁵
GANBAZ01	0.576	0.452	0.65	1.14	8.33×10^-12	1.41	2.32	2.40×10³
PARMIG	0.462	0.428	0.47	0.79	8.38×10^-9	2.06	2.24	1.23×10⁶

表1. 高性能MOF膜的几何特征和分离性能

Table 1. Geometric characteristics and separation performances of several top-performing MOF membranes

图5. MOF膜的结构性质与分离性能之间的关系

Figure 5. Relationships between structural properties of MOF membranes and corresponding separation performances

图6. 本工作所开发分类模型的有效性评价. (a) 不同MOF数据集中高性能和低性能膜材料的占比. (b) 不同MOF数据集中MOF膜选择系数的分布

Figure 6. Efficiency evaluation of the classification model developed in this study. (a) Proportions of high- and low-performance MOF membrane materials in different MOF datasets. (b) Density distributions of the membrane selectivity of MOFs in different MOF datasets

图7. 基于6个几何特征的t-SNE映射. 图中蓝色、绿色和灰色区域分别表示CoRE MOF、hMOF以及二者共同构成的几何特征空间, 红色和橙色区域分别表示由RF确定的471个hMOFs中$S_{mem,C_{2}H_{6}/C_{2}H_{4}}^{0}$≥3和$S_{mem,C_{2}H_{6}/C_{2}H_{4}}^{0}$<3的hMOFs

Figure 7. t-SNE maps based on six geometric features of MOFs. Blue, green, and gray backgrounds represent the geometric feature spaces of CoRE MOF database, hMOF database, and the database combined by both respectively, while red and orange backgrounds represent the hMOFs with $S_{mem,C_{2}H_{6}/C_{2}H_{4}}^{0}$≥3 and those with $S_{mem,C_{2}H_{6}/C_{2}H_{4}}^{0}$<3 respectively

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