Chinese Journal of Catalysis ›› 2022, Vol. 43 ›› Issue (8): 1991-2000.DOI: 10.1016/S1872-2067(21)64036-6

• Special column on surface & interface chemistry connecting thermo-,photo- and electro-catalysis • Previous Articles     Next Articles

Selectivity control in alkyne semihydrogenation: Recent experimental and theoretical progress

Xiao-Tian Li, Lin Chen, Cheng Shang, Zhi-Pan Liu()   

  1. Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China
  • Received:2021-11-30 Accepted:2022-01-24 Online:2022-08-18 Published:2022-06-20
  • Contact: Zhi-Pan Liu
  • Supported by:
    National Key Research and Development Program of China(2018YFA0208600);National Natural Science Foundation of China(22033003);National Natural Science Foundation of China(21533001);National Natural Science Foundation of China(91745201)

Abstract:

Researchers have been attempting to characterize heterogeneous catalysts in situ in addition to correlating their structures with their activity and selectivity in spite of many challenges. Here, we review recent experimental and theoretical advances regarding alkyne selective hydrogenation by Pd-based catalysts, which are an important petrochemical reaction. The catalytic selectivity for the reaction of alkynes to alkenes is influenced by the composition and structure of the catalysts. Recent progress achieved through experimental studies and atomic simulations has provided useful insights into the origins of the selectivity. The important role of the subsurface species (H and C) was revealed by monitoring the catalyst surface and the related catalytic performance. The atomic structures of the Pd catalytic centers and their relationship with selectivity were established through atomic simulations. The combined knowledge gained from experimental and theoretical studies provides a fundamental understanding of catalytic mechanisms and reveals a path toward improved catalyst design.

Key words: Alkyne semihydrogenation, Catalytic selectivity, Surface science, Machine learning, Neural network potential