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Eliminating lattice defects in metal–organic framework molecular-sieving membranes

Nature Materials volume 22, pages 769–776 (2023)Cite this article

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Abstract

Metal–organic framework (MOF) membranes are energy-efficient candidates for molecular separations, but it remains a considerable challenge to eliminate defects at the atomic scale. The enlargement of pores due to defects reduces the molecular-sieving performance in separations and hampers the wider application of MOF membranes, especially for liquid separations, owing to insufficient stability. Here we report the elimination of lattice defects in MOF membranes based on a high-probability theoretical coordination strategy that creates sufficient chemical potential to overcome the steric hindrance that occurs when completely connecting ligands to metal clusters. Lattice defect elimination is observed by real-space high-resolution transmission electron microscopy and studied with a mathematical model and density functional theory calculations. This leads to a family of high-connectivity MOF membranes that possess ångström-sized lattice apertures that realize high and stable separation performance for gases, water desalination and an organic solvent azeotrope. Our strategy could enable a platform for the regulation of nanoconfined molecular transport in MOF pores.

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Fig. 1: Formation of MOF membranes with perfect lattices.
Fig. 2: Elimination of lattice defects in MOF membranes.
Fig. 3: Formation mechanism of lattice defects in MOF membranes.
Fig. 4: Molecular separation performance of MOF membranes.

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All the data needed to evaluate the conclusions in the Article are present in the main text or the Supplementary Information. Source data are provided with this paper.

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Acknowledgements

We thank X. Ren (School of Chemistry and Molecular Engineering, Nanjing Tech University) for discussions. W.Q.J. acknowledges funding from the National Natural Science Foundation of China (grant numbers 22038006 and 21921006) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). G.P.L. acknowledges funding from the National Natural Science Foundation of China (grant number 22278210) and the Natural Science Foundation of Jiangsu Province (grant number BK20220002). G.Z.L. acknowledges funding from the Project funded by the China Postdoctoral Science Foundation (grant numbers 2022TQ0147 and 2022M721584). We thank the High-Performance Computing Center of Nanjing Tech University for supporting the computational resources.

Author information

Authors and Affiliations

  1. State Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University, Nanjing, China

    Guozhen Liu, Yanan Guo, Guining Chen, Gongping Liu, Wanqin Jin & Nanping Xu

  2. Advanced Membranes and Porous Materials (AMPM) Center, Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia

    Cailing Chen & Yu Han

  3. Department of Mathematics, Nanjing University, Nanjing, China

    Yong Lu

  4. Suzhou Laboratory, Suzhou, China

    Nanping Xu

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  1. Guozhen Liu
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Contributions

W.J. and Gongping Liu conceived the idea. W.J., Gongping Liu and Guozhen Liu designed the experiments, analysed the data and wrote the manuscript. Guozhen Liu synthesized and characterized the membranes. Y.G. conducted the density functional theory simulations. C.C. and Y.H. collected and analysed the low-dose HRTEM data. Y.L. conducted and analysed the probability calculations. G.C. collected and analysed the Powder XRD data. Guozhen Liu, Y.G., C.C., Y.L., Gongping Liu, Y.H., W.J. and N.X. discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to Gongping Liu or Wanqin Jin.

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Extended data

Extended Data Fig. 1 HRTEM of perfect lattice in Zr-MOF(BDC) membrane.

Image processing of the labelled region in Supplementary Fig. 18. (a) Raw image (b) the corresponding Fourier transform; (c) CTF-corrected image; (d) ABSF-filtered CTF-corrected image.

Extended Data Fig. 2 HRTEM of defective lattice in Zr-MOF(BDC) membrane.

Image processing of the labelled region in Supplementary Fig. 19. (a) Raw image (b) the corresponding Fourier transform; (c) CTF-corrected image; (d) ABSF-filtered CTF-corrected image.

Extended Data Fig. 3 NMR analysis of lattice defects in Zr-MOF membranes.

1H NMR spectra of Zr-MOF(fumarate) (a) and Zr-MOF(BDC) (b) membranes synthesized using ligand/SBU stoichiometric ratio.

Source data

Extended Data Fig. 4 DFT calculations.

The diagram for models of Zr-MOF(fumarate) with one Zr6O4(OH)412+ metal cluster and five ligands in a specific direction (regarded as Zr6O4(OH)412+/fumarate) (a) from the optimized crystal structure of Zr-MOF(fumarate) (b) and ZIF-8 with one Zn2+ and four mIm ligands, regarded as Zn2+/mIm (c) from the optimized crystal structure of ZIF-8 (d).

Extended Data Fig. 5 Methanol/DMC separation.

Long-term stability of methanol/DMC separation of Zr-MOF(fumarate) membrane, the feed concentration of methanol is 10 wt% and the operating temperature is 50 oC.

Source data

Extended Data Fig. 6 Universal of various substrates for Zr-MOF membrane synthesis.

Digital photos of various substrates (S) and SEM surface and cross-sectional images of Zr-MOF(fumarate) membrane (M) synthesized on different substrates including PAN, PVDF, Yttria Stabilized Zirconia (YSZ) hollow fiber and ceramic tube.

Supplementary information

Supplementary Information

Supplementary Figs. 1–59, Tables 1–13, Methods, Text and references.

Supplementary Data 1

The atomic coordinates of the models for the energy scan calculations in Fig. 3d.

Supplementary Data 2

The atomic coordinates of the models for the electrostatic potential (ESP) calculations in Fig. 3e.

Supplementary Data 3

The atomic coordinates of the models for the average local ionization energy (ALIE) calculations in Supplementary Fig. 32.

Source data

Source Data Fig. 2

Source data for PXRD data plotted in Fig. 2g, CO2 adsorption plotted in Fig. 2h, N2 adsorption plotted in Fig. 2i and 1H NMR data plotted in Fig. 2j.

Source Data Fig. 3

Source data for connectivity and H2/CO2 selectivity plotted in Fig. 3a,b, expectation connectivity in Fig. 3c and potential energy barriers plotted in Fig. 3d.

Source Data Fig. 4

Source data for H2/CO2 separation plotted in Fig. 4a, and comparison to literature in Fig. 4b, permeation rate of ions in Fig. 4c, water flux and NaCl selectivity in Fig. 4d and comparison to literature in Fig. 4e, and water/DMC separation factor and flux in Fig. 4f.

Source Data Extended Data Fig. 3

Source data for NMR data plotted in Extended Data Fig. 3.

Source Data Extended Data Fig. 5

Source data for long-term water/DMC separation performance plotted in Extended Data Fig. 5.

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Liu, G., Guo, Y., Chen, C. et al. Eliminating lattice defects in metal–organic framework molecular-sieving membranes. Nat. Mater. 22, 769–776 (2023). https://doi.org/10.1038/s41563-023-01541-0

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