Group recognition as a new design methodology for selective hydrogenation
Year: 2025
Type: article
Abstract: Selective hydrogenation is a core reaction in heterogeneous catalysis and is employed in over one-quarter of industrial chemical processes involving fine chemicals, pharmaceuticals, and energy conversion. However, achieving selectivity in multifunctional molecules remains a significant challenge because of the nondiscriminative nature of conventional heterogeneous metal catalysts toward competing reducible groups. Specifically, the fundamental issue stems from the kinetic and thermodynamic overlap of reducible groups with similar redox potentials, which makes suppressing side reactions by optimizing the reaction conditions difficult. This study proposes a “group recognition hydrogenation” strategy that achieves breakthrough selectivity by precisely controlling surface adsorption and activation processes. “Group recognition hydrogenation” is defined as a strategy that selectively distinguishes functional groups by regulating surface adsorption and activation in a spatially or electronically discriminative manner. This strategy encompasses functional group recognition adsorption and activation. The former enables preferential adsorption by modulating the adsorption strength, spatial constraints, and microenvironments, whereas the latter achieves selective activation by controlling the electronic structure and orbital interactions. This framework enables exclusive recognition of target functional groups while maintaining catalytic activity, providing a quantifiable approach for catalyst design. Future research directions in this field may include electronic–geometric structure decoupling and enantioselective control. This framework offers a novel perspective on selectivity control in heterogeneous catalysis with broad application prospects; therefore, researchers are encouraged to propose more effective selectivity control methods inspired by these concepts.
Source: Green Carbon
Authors: Minghang Li, Zhaoyuan Fu, Shanjun Mao, Yong Wang
Language: English
Subfield: Organic Chemistry
Field: Chemistry
Domain: Physical Sciences
Open Access status: diamond
Awards: G3693201551, G4741965969, G8316974453
OpenAlex ID: W4416425938
