Highly Active Nickel Catalysts for C–H Functionalization Identified through Analysis of Off-Cycle Intermediates

Abstract

An inhibitory role of 1,5-cyclooctadiene (COD) in nickel-catalyzed C-H functionalization processes was identified and studied. The bound COD participates in C-H activation by capturing the hydride, leading to a stable off-cycle π-allyl complex that greatly diminished overall catalytic efficiency. Computational studies elucidated the origin of the effect and enabled identification of a 1,5-hexadiene-derived pre-catalyst that avoids the off-cycle intermediate and provides catalytic efficiencies that are superior to those of catalysts derived from Ni(COD)2.

Ni-Catalyzed Site-Selective Dicarboxylation of 1,3-Dienes with CO2

“…15 Interestingly, the reactivity of Ni-1 with 1a was not comparable to that observed with Ni(COD) 2 /L7, with not even traces of 2a being observed in the former. 28 While one might attribute this finding to the non-innocent role of COD, 29 the reluctance of coordinatively saturated Ni-1 to dissociate L7 prior to 1,3-diene binding seems more likely. Unfortunately, the preparation of p-allyl complex I (Scheme 1) in pure analytical form bearing L7 as ligand proved to be particularly elusive.…”

Automatic Proposal of Multistep Reaction Mechanisms using a Graph-Driven Search

“…A key application of contemporary computational chemistry lies in analyzing the elementary steps of complex chemical reaction mechanisms associated with processes such as catalysis, [1][2][3][4][5] polymerization 6 and combustion. [7][8][9] Given initial reactant and product molecular configurations (i.e.…”

Traversing Dense Networks of Elementary Chemical Reactions to Predict Minimum‐Energy Reaction Mechanisms

“…First, many heuristic (or rules-based) reaction-discovery tools have been suggested over the last couple of decades, most commonly based on the idea of representing molecules as graphs (or adjacency matrices) and chemical reactions as matrix operations acting on those graphs. Examples of these approaches include the Reaction Mechanism Generator (RMG) developed by Green and co-workers, [8,16,17] the Zstruct approach by Zimmerman, [18][19][20] recent work by Reiher and co-workers, [11,21,22] the basin-hopping Monte Carlo strategy adopted by Kim and co-workers, [23,24] and our own recent work on graph-driven sampling schemes to investigate both open-ended and double-ended reaction networks. [6,9,14] This non-exhaustive list of examples, which spans approaches ranging from Hamiltonian-based sampling schemes to brute-force reaction-network generation, demonstrates the flexibility of graph-based descriptors in constructing algorithms for investigating reaction networks.…”