Structure–performance descriptors and the role of Lewis acidity in the methanol-to-propylene process
De Wispelaere, Kristof
Hensen, Emiel J. M.
Martínez-Espín, Juan S.
Weckhuysen, Bert M.
Van Speybroeck, Veronique
KAUST DepartmentChemical Engineering Program
Imaging and Characterization Core Lab
KAUST Catalysis Center (KCC)
Physical Science and Engineering (PSE) Division
Online Publication Date2018-06-25
Print Publication Date2018-08
Permanent link to this recordhttp://hdl.handle.net/10754/628296
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AbstractThe combination of well-defined acid sites, shape-selective properties and outstanding stability places zeolites among the most practically relevant heterogeneous catalysts. The development of structure–performance descriptors for processes that they catalyse has been a matter of intense debate, both in industry and academia, and the direct conversion of methanol to olefins is a prototypical system in which various catalytic functions contribute to the overall performance. Propylene selectivity and resistance to coking are the two most important parameters in developing new methanol-to-olefin catalysts. Here, we present a systematic investigation on the effect of acidity on the performance of the zeolite ‘ZSM-5’ for the production of propylene. Our results demonstrate that the isolation of Brønsted acid sites is key to the selective formation of propylene. Also, the introduction of Lewis acid sites prevents the formation of coke, hence drastically increasing catalyst lifetime.
CitationYarulina I, De Wispelaere K, Bailleul S, Goetze J, Radersma M, et al. (2018) Structure–performance descriptors and the role of Lewis acidity in the methanol-to-propylene process. Nature Chemistry 10: 804–812. Available: http://dx.doi.org/10.1038/s41557-018-0081-0.
SponsorsThis research received funding from the Netherlands Organization for Scientific Research (NWO) in the framework of the TASC Technology Area ‘Syngas, a Switch to Flexible New Feedstock for the Chemical Industry (TA-Syngas). S.B., K.D.W. and V.V.S. acknowledge the Fund for Scientific Research: Flanders (FWO), the Belgian American Educational Foundation, the Research Board of Ghent University (BOF), BELSPO in the frame of IAP/7/05 and funding from the European Union’s Horizon 2020 research and innovation programme (consolidator ERC grant agreement no. 647755—DYNPOR (2015–2020)). The computational resources and services used were provided by Ghent University (Stevin Supercomputer Infrastructure) and the VSC (Flemish Supercomputer Center), funded by the Research Foundation: Flanders (FWO).