Cheng Fang1,2, Marco Fantin3, Xiangcheng Pan3, Kurt de Fiebre1, Michelle L Coote4, Krzysztof Matyjaszewski3, Peng Liu1,5. 1. Department of Chemistry , University of Pittsburgh , Pittsburgh , Pennsylvania 15260 , United States. 2. Computational Modeling & Simulation Program , University of Pittsburgh , Pittsburgh , Pennsylvania 15260 , United States. 3. Department of Chemistry , Carnegie Mellon University , Pittsburgh , Pennsylvania 15213 , United States. 4. ARC Centre of Excellence for Electromaterials Science, Research School of Chemistry , Australian National University , Canberra , Australian Capital Territory 2601 , Australia. 5. Department of Chemical and Petroleum Engineering , University of Pittsburgh , Pittsburgh , Pennsylvania 15261 , United States.
Abstract
Copper-catalyzed atom transfer radical polymerization (Cu-ATRP) is one of the most widely used controlled radical polymerization techniques. Notwithstanding the extensive mechanistic studies in the literature, the transition states of the activation/deactivation of the growing polymer chain, a key equilibrium in Cu-ATRP, have not been investigated computationally. Therefore, the understanding of the origin of ligand and initiator effects on the rates of activation/deactivation is still limited. Here, we present the first computational analysis of Cu-ATRP activation transition states to reveal factors that affect the rates of activation and deactivation. The Br atom transfer between the polymer chain and the Cu catalyst occurs through an unusual bent geometry that involves pronounced interactions between the polymer chain end and the ancillary ligand on the Cu catalyst. Therefore, the rates of activation/deactivation are determined by both the electronic properties of the Cu catalyst and the ligand-initiator steric repulsions. In addition, our calculations revealed the important role of ligand backbone flexibility on the activation. These theoretical analyses led to the identification of three chemically meaningful descriptors, namely HOMO energy of the catalyst ( EHOMO), percent buried volume ( Vbur%), and distortion energy of the catalyst (Δ Edist), to describe the electronic, steric, and flexibility effects on reactivity, respectively. A robust and simple predictive model for ligand effect on reactivity is thereby established by correlating these three descriptors with experimental activation rate constants using multivariate linear regression. Validation using a structurally diverse set of ligands revealed the average error is less than ±2 kcal/mol compared to the experimentally derived activation energies. The same approach was also applied to develop a predictive model for reactivity of different alkyl halide initiators using R-X bond dissociation energy (BDE) and Cu-X halogenophilicity as descriptors.
Copper-catalyzed atom transfer radical n class="Chemical">polymerization (Cu-ATRP) is one of the most widely used controlled radical polymerization techniques. Notwithstanding the extensive mechanistic studies in the literature, the transition states of the activation/deactivation of the growing polymer chain, a key equilibrium in Cu-ATRP, have not been investigated computationally. Therefore, the understanding of the origin of ligand and initiator effects on the rates of activation/deactivation is still limited. Here, we present the first computational analysis of Cu-ATRP activation transition states to reveal factors that affect the rates of activation and deactivation. The Br atom transfer between the polymer chain and the Cu catalyst occurs through an unusual bent geometry that involves pronounced interactions between the polymer chain end and the ancillary ligand on the Cu catalyst. Therefore, the rates of activation/deactivation are determined by both the electronic properties of the Cu catalyst and the ligand-initiator steric repulsions. In addition, our calculations revealed the important role of ligand backbone flexibility on the activation. These theoretical analyses led to the identification of three chemically meaningful descriptors, namely HOMO energy of the catalyst ( EHOMO), percent buried volume ( Vbur%), and distortion energy of the catalyst (Δ Edist), to describe the electronic, steric, and flexibility effects on reactivity, respectively. A robust and simple predictive model for ligand effect on reactivity is thereby established by correlating these three descriptors with experimental activation rate constants using multivariate linear regression. Validation using a structurally diverse set of ligands revealed the average error is less than ±2 kcal/mol compared to the experimentally derived activation energies. The same approach was also applied to develop a predictive model for reactivity of different alkyl halide initiators using R-X bond dissociation energy (BDE) and Cu-Xhalogenophilicity as descriptors.
Authors: Rustam Z Khaliullin; Erika A Cobar; Rohini C Lochan; Alexis T Bell; Martin Head-Gordon Journal: J Phys Chem A Date: 2007-07-27 Impact factor: 2.781