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Mechanistic Studies into the Oxidative Addition of Co(I) Complexes: Combining Electroanalytical Techniques with Parameterization
Author(s) -
Christopher Sandford,
Lydia R. Fries,
Tyler Ball,
Shelley D. Minteer,
Matthew S. Sigman
Publication year - 2019
Publication title -
journal of the american chemical society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 7.115
H-Index - 612
eISSN - 1520-5126
pISSN - 0002-7863
DOI - 10.1021/jacs.9b10771
Subject(s) - chemistry , electrophile , ligand (biochemistry) , denticity , substituent , oxidative addition , computational chemistry , catalysis , combinatorial chemistry , substrate (aquarium) , reactivity (psychology) , reductive elimination , radical , cyclic voltammetry , photochemistry , stereochemistry , organic chemistry , electrochemistry , crystal structure , electrode , medicine , biochemistry , receptor , oceanography , alternative medicine , pathology , geology
The oxidative addition of organic electrophiles into electrochemically generated Co(I) complexes has been widely utilized as a strategy to produce carbon-centered radicals when cobalt is ligated by a polydentate ligand. Changing to a bidentate ligand provides the opportunity to access discrete Co(III)-C bonded complexes for alternative reactivity, but knowledge of how ligand and/or substrate structures affect catalytic steps is pivotal to reaction design and catalyst optimization. In this vein, experimental studies that can determine the exact nature of elementary organometallic steps remain limited, especially for single-electron oxidative addition pathways. Herein, we utilize cyclic voltammetry combined with simulations to obtain kinetic and thermodynamic properties of the two-step, halogen-atom abstraction mechanism, validated by analyzing kinetic isotope and substituent effects. Complex Hammett relationships could be disentangled to allow understanding of individual effects on activation energy barriers and equilibrium constants, and DFT-derived parameters used to build predictive statistical models for rates of new ligand/substrate combinations.

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