Large-Scale Computational Screening of Metal-Organic Framework Membranes for Ethane/Ethylene Separation

doi:10.6023/A22040186

Abstract

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

Cite this article

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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Fig. & Tab. 8

Figure 1. Screening workflow used in this study

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

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

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

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

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

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

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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