Combining multi-scale simulations and super-resolution imaging methods can visualize individual zeolite catalyst

Here we show a deep data approach via synergy of multiscale reaction-diffusion simulations and super-resolution structured illumination microscopy to image the dynamically spatiotemporal evolution of molecules and acid sites in SAPO-34 zeolite crystal in industrial methanol-to-olefins process.

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My group focuses on the development of industrial methanol-to-olefins (MTO) process, which now constitutes the most significant process for light olefins (ethylene and propylene) synthesis with non-oil feedstocks including coal, natural gas, biomass and CO21, 2. SAPO-34 zeolite catalyst has been found most suitable for MTO due to the high light olefins selectivity achieved. However, the reaction mechanism underlying MTO in SAPO-34 is very complicated because there are substantial gradients of molecules at different positions inside even an individual catalyst for different time3, 4, 5, owning to the interplay of molecular diffusion, complex porous structure, and physicochemical properties of active sites in SAPO-34 zeolites. Thus, it is very important to understand the reaction and diffusion process, and in particular to obtain the information of spatiotemporal evolution of molecules and active sites during MTO reaction in SAPO-34 zeolite catalyst with a few microns which is of practical significance.

Widespread imaging techniques developed so far can only provide limited information (e.g. active sites, pore structure, molecular transport and adsorption, chemical transformations, and heat effect) at scale of single catalyst crystal either with large probe molecules or in model catalyst with a size far beyond the interests of industrial catalysis.

Recently, we developed a deep data approach6 that can link the simulations at different scale and integrate multiscale simulations7 and spatiotemporal-resolved spectroscopy, among other measurements, to image the spatiotemporal evolution of gas molecules, carbonaceous species and active sites in SAPO-34 zeolite during MTO process. In this approach, we essentially used a multiscale reaction-diffusion model8, 9 that has been established by our group and can provide a link of catalytic process from molecular dynamics, individual zeolite crystal, catalyst ensemble to specific reactor. Multiscale simulations are used to elaborate the information of molecular evolution in individual catalyst and changes of product selectivity in catalyst bed. As shown in Figure 1, the multiscale simulations are meantime integrated with super-resolution structured illumination microscopy at individual catalyst crystal scale. The spatiotemporal evolution of gas molecules, carbonaceous species and acid sites in individual SAPO-34 zeolite crystal of a few microns are therefore visualized. Peculiarly, we found crystalline size of SAPO-34 zeolite has significant impact on the spatiotemporal evolution of carbonaceous species during MTO reaction. Multiscale reaction-diffusion simulations substantiate these observations, and further provide rational and detailed reaction-diffusion process in SAPO-34 zeolite crystal during MTO reaction.

Substantially, we think our approach can take the advantages of the well-established theoretical model and specifically developed experimental techniques, and make it especially suitable for extensive applications where obtaining a complete picture of the reaction process is not possible with only the simulations or experiments alone.

Figure 1. Deep data approach integrating multi-scale reaction-diffusion simulations and experiments can make ‘molecular’ movie’ of methanol-to-olefins reaction over SAPO-34 zeolites3.

To find out more details, please read our article “Imaging spatiotemporal evolution of molecules and active sites in zeolite catalyst during methanol-to olefins reaction” in Nature Communications.

This post was co-author with Mingbin Gao, a PhD student in my laboratory.

Reference

  1. Ye, M., Li, H., Zhao, Y., Zhang, T. & Liu, Z. MTO Processes Development: The Key of Mesoscale Studies. Adv. Chem. Eng. 47, 279-335 (2015).
  2. Tian, P., Wei, Y., Ye, M. & Liu, Z. Methanol to Olefins (MTO): From Fundamentals to Commercialization. ACS Catal. 5, 1922-1938 (2015).
  3. Meirer, F. & Weckhuysen, B. M. Spatial and temporal exploration of heterogeneous catalysts with synchrotron radiation. Nat. Rev. Mater. 3, 324-340 (2018).
  4. Buurmans, I. L. C. et al. Catalytic activity in individual cracking catalyst particles imaged throughout different life stages by selective staining. Nat. Chem. 3, 862–867 (2011).
  5. Kärger, J. et al. Microimaging of transient guest profiles to monitor mass transfer in nanoporous materials. Nat. Mater. 13, 333-343 (2014).
  6. Kalinin, S. V., Sumpter, B. G. & Archibald, R. K. Big–deep–smart data in imaging for guiding materials design. Nat. Mater. 14, 973–980 (2015).
  7. Keil, F. J. Molecular Modelling for Reactor Design. Annu. Rev. Chem. Biomol. Eng. 9, 201-227 (2018).
  8. Li, H., Ye, M. & Liu, Z. A multi-region model for reaction–diffusion process within a porous catalyst pellet. Chem. Eng. Sci. 147, 1-12 (2016).
  9. Gao, M. et al. A modeling study on reaction and diffusion in MTO process over SAPO-34 zeolites. Chem. Eng. J. 377, 119668 (2019).

Mao Ye

Professor, Dalian Institute of Chemical Physics

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