Catalyst design and tuning for oxidative dehydrogenation of propane – A review

• Published in: Applied catalysis. A, General Vol. 609; p. 117914
• Main Authors: Gambo, Yahya, Adamu, Sagir, Abdulrasheed, Abdulrahman A., Lucky, Rahima A., Ba-Shammakh, Mohammed S., Hossain, Mohammad. M.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 05.01.2021; Elsevier Science SA
• Subjects: Active site; Catalysis; Catalyst design; Catalysts; Commercialization; Dehydrogenation; DFT; Machine learning; Oxidative dehydrogenation of propane; Oxygen vacancy; Precursors; Programming languages; Propane; Selectivity; Tuning; DFT; Catalyst design; Oxidative dehydrogenation of propane; Active site; Machine learning; Oxygen vacancy
• ISSN: 0926-860X; 1873-3875
• DOI: 10.1016/j.apcata.2020.117914

[Display omitted] •Strategies of tuning catalysts for oxidative dehydrogenation of propane (ODHP).•Nature of active sites, probing active sites, site isolation, and site accessibility.•Role of key catalytic properties - oxygen vacancy, reducibility, metal-support interaction, acidity and basicity.•Rational design of novel catalysts and establishment of property-activity relationshipsvia accelerated catalyst discovery. In heterogeneous catalysis, unravelling the distinct structural and compositional nature of active sites provides a good platform in modulating important catalytic properties toward improved activity and selectivity. Obviously, despite the direct propane dehydrogenation (PDH) being a commercial process, oxidative dehydrogenation of propane (ODHP) remains a highly prospective technology for propene production due to its leveraged thermodynamic advantage and less susceptibility to catalyst deactivation. The present work has systematically and comprehensively reviewed recent advances on key aspects related to ODHP catalysts such as nature of the active sites, active sites isolation, accessibility of active sites, active sites in layered-double hydroxide (LDH) derived mixed oxide catalysts, intercalated LDH precursors, co-precipitated LDH precursors, oxides-support interaction, oxygen vacancy, surface oxide reducibility, and acid–base property. Furthermore, discussion on accelerated discovery of novel catalysts via high-throughput experimentations (HTE) and high-throughput quantum computations (HTC) has been provided. Integrated frameworks via machine learning and Python language are hugely augmenting in this pursuit. It is hoped that this perspective provides insights on the design and tuning of heterogeneous catalysts, and in particular the development of efficient catalysts that could prompt industrialization and commercialization of ODHP process.