4.8 Article

Rational design of intermetallic compound catalysts for propane dehydrogenation from a descriptor-based microkinetic analysis

Journal

JOURNAL OF CATALYSIS
Volume 404, Issue -, Pages 32-45

Publisher

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jcat.2021.09.008

Keywords

Propane dehydrogenation; Density functional theory; Descriptor-based microkinetic modeling; Volcano curve; Intermetallic compound catalysts screening

Funding

  1. Natural Science Foundation of China [91645122, 22073027]
  2. Natural Science Foundation of Shanghai [20ZR1415800]
  3. Fundamental Research Funds for the Central Universities [222201718003]

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A descriptor-based microkinetic analysis was used to screen for inexpensive and environmentally friendly propane dehydrogenation catalysts, leading to the synthesis and evaluation of non-precious Fe3Ga1 catalyst with improved propylene selectivity and catalytic stability compared to Pt catalyst.
By using a descriptor-based microkinetic analysis combined with results from density functional theory calculations over eight transition-metal (including Ni, Cu, Rh, Pd, Ag, Ir, Pt, and Au) terrace and stepped surfaces, intermetallic compounds have been screened to search for inexpensive and environmentally friendly propane dehydrogenation (PDH) catalysts with enhanced catalytic performance. Based on the scaling relations, the formation energies of ethyl on terrace and methylidyne on stepped surfaces are identified as two descriptors to represent the energetics of other reaction intermediates and transition sates. The derived PDH activity and propylene selectivity volcano curves not only provide a rational interpretation of the previously experimentally reported catalyst candidates, but also predict several unexpected binary combinations by screening 1482 A(3)B(1) and 741 A(1)B(1) intermetallic compounds. The non-precious Fe3Ga1 is then synthesized and evaluated. Its improved propylene selectivity and catalytic stability with respect to those of the Pt catalyst validates the theoretical predictions experimentally. (C) 2021 Elsevier Inc. All rights reserved.

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