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One-dimensional intergrowths in two-dimensional zeolite nanosheets and their effect on ultra-selective transport

Nature Materials volume 19, pages 443–449 (2020)Cite this article

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Abstract

Zeolite MFI is a widely used catalyst and adsorbent that also holds promise as a thin-film membrane. The discovery of nanometre-thick two-dimensional (2D) MFI nanosheets has facilitated methods for thin-film zeolite fabrication that open new horizons for membrane science and engineering. However, the crystal structure of 2D-MFI nanosheets and their relationship to separation performance remain elusive. Using transmission electron microscopy, we find that one- to few-unit-cell-wide intergrowths of zeolite MEL exist within 2D-MFI. We identify the planar distribution of these 1D or near-1D-MEL domains, and show that a fraction of nanosheets have high (~25% by volume) MEL content while the majority of nanosheets are MEL-free. Atomistic simulations show that commensurate knitting of 1D-MEL within 2D-MFI creates more rigid and highly selective pores compared to pristine MFI nanosheets, and permeation experiments show a separation factor of 60 using an industrially relevant (undiluted 1 bar xylene mixture) feed. Confined growth in graphite is shown to increase the MEL content in MFI nanosheets. Our observation of these intergrowths suggests strategies for the development of ultra-selective zeolite membranes.

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Fig. 1: Structure of the nanosheet.
Fig. 2: Identification of MFI and MEL heterostructures in nanosheets.
Fig. 3: Electron diffraction analysis for quantification of MEL in MFI nanosheets.
Fig. 4: Effect of MEL domains on separation and mechanical properties of the MFI nanosheet.

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Data availability

The main data supporting the findings of this study are available within this Article and its Supplementary Information. Further data are available from the corresponding authors upon request.

Code availability

The template matching code used to identify MFI and MEL is available from K.A.M. and P.K. upon reasonable request.

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Acknowledgements

This work was primarily supported by the National Science Foundation (CBET-1705687). XRD was performed at Argonne National Laboratory supported by the US Department of Energy, Office of Science, Office of Basic Energy Sciences, under contract no. DE-AC02-06CH11357. Parts of this work were carried out in the Characterization Facility, University of Minnesota, which receives partial support from the NSF through the MRSEC and NNIN programs (DMR-1420013). The FPMD simulations used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under contract no. DE-AC02-06CH11357. Additional computer resources were provided by the Minnesota Supercomputing Institute. P.K. and E.O.F. acknowledge support from a Doctoral Dissertation Fellowship received from the Graduate School at the University of Minnesota. H.X. and T.D. acknowledge support from NSF 1332228. Q.X. acknowledges support from the National Natural Science Foundation of China (21471131).

Author information

Author notes

  1. These authors contributed equally: Dae Woo Kim, Neel Rangnekar, Hao Xu, Evgenii O. Fetisov.

Authors and Affiliations

  1. Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN, USA

    Prashant Kumar, Dae Woo Kim, Neel Rangnekar, Supriya Ghosh, Han Zhang, Meera Shete, J. Ilja Siepmann, Michael Tsapatsis & K. Andre Mkhoyan

  2. Department of Chemical and Biomolecular Engineering, YONSEI University, Seodaemun-gu, Seoul, Republic of Korea

    Dae Woo Kim

  3. Separations and Process Chemistry, Corporate Strategic Research, ExxonMobil Research and Engineering, Annandale, NJ, USA

    Neel Rangnekar & Benjamin McCool

  4. Department of Mechanical Engineering, University of Minnesota, Minneapolis, MN, USA

    Hao Xu & Traian Dumitrica

  5. Department of Chemistry and Chemical Theory Center, University of Minnesota, Minneapolis, MN, USA

    Evgenii O. Fetisov & J. Ilja Siepmann

  6. Key Laboratory of the Ministry of Education for Advanced Catalysis Materials, Institute of Advanced Fluorine-Containing Materials, Zhejiang Normal University, Jinhua, Zhejiang, China

    Qiang Xiao

  7. Department of Chemical and Biomolecular Engineering & Institute for NanoBioTechnology Johns Hopkins University, Baltimore, MD, USA

    Michael Tsapatsis

  8. Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, USA

    Michael Tsapatsis

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Contributions

P.K., along with K.A.M. and M.T., conceived the project. P.K. performed TEM data collection and analysis and developed the template matching algorithm with input from M.T. and K.A.M. H.Z., Q.X. and N.R. synthesized the 2D nanosheets. D.W.K. prepared the MEL-containing directly synthesized nanosheets (in the presence of graphite) with input from M.T., and S.G. performed TEM characterization. N.R. and M.S. performed XRD and AFM measurements. N.R. synthesized membranes and, with B.M., performed xylene membrane permeation measurements. H.X. and T.D. performed DFT simulations and, along with K.A.M. and P.K., interpreted the results on the mechanical properties of the nanosheets. E.O.F. and J.I.S. performed and analysed the FPMD simulations for the separation properties of the nanosheets. P.K., M.T. and K.A.M. prepared the manuscript with written contributions from all co-authors. K.A.M. and M.T. co-directed all aspects of the project.

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Correspondence to Prashant Kumar, Michael Tsapatsis or K. Andre Mkhoyan.

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Extended data

Extended Data Fig. 1 MEL content quantification through XRD.

a, Indexed in-plane XRD data after background removal is labeled with corresponding peak positions. b, Bulk powder XRD patterns for MFI with 0–10% MEL are simulated using DiffaX computer code. The (102) peak decreases in intensity with increasing MEL content (bottom panel) while the (101) peak remains unchanged. c, Calibration curve from simulated data is plotted for % MEL vs I102/I101. Using the fitted curve, the MEL content of the entire sample is calculated: the experimentally determined I102/I101 ratio is 0.07, which corresponds to 4.3% MEL content.

Extended Data Fig. 2 ADF-STEM imaging of MFI nanosheets.

a, (left panel) Low magnification ADF-STEM image of an MFI nanosheet deposited on a carbon support. (middle and right panel) As-acquired raw ADF-STEM images of the sections shown in main text (Fig. 2a,b). b, ADF-STEM image (top) and the corresponding FFT (bottom) at increasing electron doses (left to right). Image acquired at the optimal dose is highlighted in blue. At less than 1.6x109 e−, pores are barely visible (missing FFT spots as compared to FFT on right), while at higher doses the nanosheet is amorphized as seen by the disappeared spots in FFT (when compared to FFT on left). Cumulative electrons (e-), electron dose (e-/Å2) and the total time of exposure (t) for image acquisition are listed at the top right corner of the as-acquired images. Maximum detectable periodicity is indicated by the arrows in FFT. c, Filtering steps (Left to right) for as-acquired raw image are shown. The same filtering steps are implemented as step 1 in template matching algorithm described in Extended data Fig. 4. Top row shows the ADF-STEM images. Middle row shows a smaller section from the image on top. Bottom column is the fast-fourier transform (FFT) of the image.

Extended Data Fig. 3 ADF-STEM defocus series of a 1.5 u.c. thick (along b-axis) MFI and MEL unit cell.

The simulation parameters were: Cs = 0 mm, Vo = 200 kV, Δf = -20 to 200 Å, 35 and 317 mrad for ADF detector inter- and outer-angles, 256 x 256 pix2 image size. The frozen phonons were not included in these calculations. Δf = 100 Å simulated image (enclosed by dotted lines) matched the experimental defocus conditions and is used as starting template (shown at the bottom after Gaussian blurring) for the template matching algorithm described in Extended data Fig. 4. Each simulated image is scaled in intensity individually from 0 to 255.

Extended Data Fig. 4 Algorithm for finding MFI and MEL distribution across ADF-STEM.

Demonstration of the cross-correlation based template matching code (written in MATLAB) to find multislice simulated MFI and MEL template in TEM images. a, Flowchart describing the four steps (bound by grey boxes) for executing the algorithm. b, Representative outputs of the code from the locations marked as 1* - 7* in the flowchart shown in a.

Extended Data Fig. 5 Results of template matching algorithm on different ADF-STEM images.

As-acquired raw images from the microscope are shown on the left. Dotted lines represent the section which is aligned with a- and c-axes of the crystal as vertical and horizontal lines respectively. Filtered images processed using radial Wiener, Band-pass, and moving 5x5 average filters of the section shown in the first column are shown in the second column. Results of the template matching algorithm are shown in the third and fourth column with shades of red and yellow circles representing two different orientations of MEL and MFI, respectively (same color scheme as in the Extended Data Fig. 4b and main text Fig. 2a). Third column images are obtained by cross-correlation of simulated unit-cell TEM images of MFI and MEL, with the filtered images in column 2, while the fourth column is obtained by cross-correlating the (cross-correlated and averaged) templates created from the matched units of each color in the third column with the corresponding images in column 1. Fourth column shows more complete filling since the scanned templates are created from the experimental image, thereby incorporating the noise levels and astigmatism present during collection of experimental data.

Extended Data Fig. 6 Effect of different stages of filtering an ADF-STEM image on template matching.

a, ADF-STEM at different stages of filtering. b, Effect of each stage of filtering on the ADF-STEM images. Difference image between Stage 2 and Stage 1 shows a low-frequency intensity variation arising due to the carbon residues on the nanosheet. Histogram of the image at Stage 3 shows a decrease in FWHM by 14% as compared to histogram of the image at Stage 2. c, Results of the template matching algorithm (algorithm is shown in Extended Data Fig. 4) at each stage of filtering show that the aforementioned reduction in FWHM correspond to a decrease in the noise in image.

Extended Data Fig. 7 Template matching results for BF-TEM images.

Filtered BF-TEM images (first column), template matching results using simulated MFI and MEL unit cell images (second column) and results using templates created from the matched units in run 1 (third column) for a, nominally pristine MFI nanosheet b, MFI-MEL nanosheet. Simulated and cross-correlated experimental units extracted from c, panels a for MFI nanosheet and d, panels b for MFI-MEL nanosheet. First row indicates the simulated BF-TEM templates used for run 1. Second and third rows indicate the cross-correlated and averaged units identified after run 1 and 2, respectively.

Extended Data Fig. 8 Effect of domain size on the reciprocal space of MFI nanosheet.

a, 3D model of a MFI nanosheet with lengths along a-, b-, and c-direction as Na, Nb, and Nc unit cells (u.c.). b, Changes in shape of the structure factor iso-surface at a reciprocal lattice point are shown, as the length of the MFI supercell is changed along a-direction from 1-4 u.c. The width of the reciprocal lattice point along a*- and b*- direction is inversely proportional to length along a- and b-direction (Na, Nb). c, MFI supercells (Na = 1-4 u.c., Nb = 1.5 u.c., Nc = 11 u.c.) along with different projections of the corresponding structure factor iso-surfaces in the 3D reciprocal space are shown. It is seen that as the length of the MFI supercell increases in a-direction, the elongation of the spot in a* - direction decreases. This corroborates with the streaking of spots along a* direction in acquired electron diffraction patterns.

Extended Data Fig. 9 Simulation of ED patterns for various nanosheet heterostructures.

a, ED patterns simulated using multislice code for nanosheet heterostructures shown in bottom right of each panel. Total length of the heterostructure along a-, b-, and c-direction is 11 u.c., 1.5 u.c. and 20 u.c. b, Diffraction pattern section indicated by dotted line in panel a is shown in a magnified view (top row) for models 1-10. Iso-surfaces for structure factor, |F|2 are plotted for the same models (bottom row). c, Line scans from |F|2 taken across (101) and (102) spot along a*-direction show changes in intensity of (102) spot while (101) spot remains unchanged as the MEL content and domain size varies. d, Intensity ratio vs % MEL content in the heterostructures (1 to 10) shows linear variation (line fit has R2=0.98). Intensities of individual spots are calculated by adding up the pixel values (or area under the 2D gaussian fit) for each spot after background subtraction. e, MFI domain width along a-direction (d) vs FWHM of (102) peak over FWHM of (101) peak (R) for heterostructures (1 to 10) shows exponential variation (fit has R2=0.97). FWHM is calculated along a*- and c*-direction by fitting a 2D-Gaussian function.

Extended Data Fig. 10 Analysis of experimental SAED patterns.

a, Representative SAED patterns showing variation in diffraction spots across different nanosheets. (101) spot is highlighted in red and (102) spot is highlighted in white. b, (101) and (102) spots from SAED patterns in panel a are magnified and shown. Streaking and splitting of (102) spots is seen while the corresponding (101) spot shows minimal variations in shape and intensity. c, Line-scans across diffraction spots shown in panel b are plotted. Linescans 2 and 3 show splitting of spots with distance along a* between the peaks being 0.0027 Å-1 and 0.0036 Å-1. This splitting indicates the presence of a repeating heterostructure in the nanosheet which has a periodicity of 37 nm and 28 nm respectively. d, Averaged (101) and (102) spots are shown from SAED patterns of 50 MFI nanosheets and 50 MFI-MEL nanosheets. e, Intensities of diffraction spots shown in panel d are listed in the table, with MFI nanosheets having 0% MEL and MFI-MEL nanosheets having 25% MEL. Percentage of MEL is calculated using the calibration chart (I102/I101 vs %MEL) shown in Extended data Fig. 9d.

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Membrane fabrication and testing discussion, Supplementary Figs. 1–7, Tables 1–3 and references 1–5.

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Kumar, P., Kim, D.W., Rangnekar, N. et al. One-dimensional intergrowths in two-dimensional zeolite nanosheets and their effect on ultra-selective transport. Nat. Mater. 19, 443–449 (2020). https://doi.org/10.1038/s41563-019-0581-3

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