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Ligand-Substrate Dispersion Facilitates the Copper-Catalyzed Hydroamination of Unactivated Olefins.


ABSTRACT: The current understanding of ligand effects in transition metal catalysis is mostly based on the analysis of catalyst-substrate through-bond and through-space interactions, with the latter commonly considered to be repulsive in nature. The dispersion interaction between the ligand and the substrate, a ubiquitous type of attractive noncovalent interaction, is seldom accounted for in the context of transition-metal-catalyzed transformations. Herein we report a computational model to quantitatively analyze the effects of different types of catalyst-substrate interactions on reactivity. Using this model, we show that in the copper(I) hydride (CuH)-catalyzed hydroamination of unactivated olefins, the substantially enhanced reactivity of copper catalysts based on bulky bidentate phosphine ligands originates from the attractive ligand-substrate dispersion interaction. These computational findings are validated by kinetic studies across a range of hydroamination reactions using structurally diverse phosphine ligands, revealing the critical role of bulky P-aryl groups in facilitating this process.

SUBMITTER: Lu G 

PROVIDER: S-EPMC5798229 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Ligand-Substrate Dispersion Facilitates the Copper-Catalyzed Hydroamination of Unactivated Olefins.

Lu Gang G   Liu Richard Y RY   Yang Yang Y   Fang Cheng C   Lambrecht Daniel S DS   Buchwald Stephen L SL   Liu Peng P  

Journal of the American Chemical Society 20171109 46


The current understanding of ligand effects in transition metal catalysis is mostly based on the analysis of catalyst-substrate through-bond and through-space interactions, with the latter commonly considered to be repulsive in nature. The dispersion interaction between the ligand and the substrate, a ubiquitous type of attractive noncovalent interaction, is seldom accounted for in the context of transition-metal-catalyzed transformations. Herein we report a computational model to quantitatively  ...[more]

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