Ab Initio Kinetic Modelling in Radical Polymerization: A Paradigm Shift in Reaction Kinetic Analysis
Michelle L. Coote A C and Christopher Barner-Kowollik B CA ARC Centre of Excellence in Free Radical Chemistry and Biotechnology, Research School of Chemistry, Australian National University, Canberra ACT 0200, Australia.
B Centre for Advanced Macromolecular Design, School of Chemical Sciences and Engineering, University of New South Wales, Sydney, NSW 2052, Australia.
C Corresponding authors. Email: mcoote@rsc.anu.edu.au; c.barner-kowollik@unsw.edu.au
Australian Journal of Chemistry 59(10) 712-718 https://doi.org/10.1071/CH06194
Submitted: 6 June 2006 Accepted: 17 August 2006 Published: 30 October 2006
Abstract
We describe a new rationale to kinetic modelling in which adjustable parameters are avoided through the use of quantum chemistry. This new approach reverses the standard modelling approach in which, having first assumed a kinetic model, it is then fitted to the experimentally determined values of the macroscopic properties (rates, compositions, molecular weight distributions, and so forth) so as to estimate the rate coefficients of the individual reactions. Instead, one still assumes a reaction scheme, but then calculates the rates of the individual reactions using high-level ab initio calculations, and in this way a kinetic model is built that can be used to predict the macroscopic properties of the process from first principles. These can then be compared directly with experiment (for benchmarking purposes) and subsequently be employed to predict the outcome of new chemical processes. In here we illustrate the ab initio modelling technique, using a recent study of initialization in RAFT polymerization as a case study. We also discuss its advantages and possible problems, and highlight some of its potential applications in the radical polymer field.
Acknowledgments
The authors acknowledge the fruitful and stimulating discussions within the IUPAC sponsored task group on the mechanism and kinetics of the RAFT process. M.L.C. also gratefully acknowledges funding from the Australian Research Council through the Centre of Excellence in Free Radical Chemistry and Biotechnology. C.B.-K. acknowledges funding from the ARC in the form of Discovery grants.
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A One can never of course prove a model — merely disprove it. However, when the use of adjustable parameters is avoided, a model is subjected to much more critical testing, which increases the probability that an inadequate model will fail and provides much stronger support for successful models.
