Model Discrimination between RAFT Polymerization Models Using Sequential Bayesian Methodology. Issue 5 (28th May 2018)

• Record Type: Journal Article
• Authors: Masoumi, Samira; Duever, Thomas A.; Penlidis, Alexander; Azimi, Reza; López‐Domínguez, Porfirio; Vivaldo‐Lima, Eduardo
• Is Part Of: Macromolecular theory and simulations. Volume 27:Issue 5(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-05-28
• Subjects: model discrimination -- reversible addition‐fragmentation chain transfer (RAFT) -- reversible deactivation radical polymerization (RDRP) -- sequential Bayesian methodology; Macromolecules -- Periodicals; Polymers -- Periodicals; Polymerization -- Periodicals; Macromolécules -- Périodiques; 547.705
• DOI: 10.1002/mats.201800016
• Languages: English
• ISSNs: 1022-1344

Abstract

Abstract: The use of sequential Bayesian methodology for model discrimination purposes in reversible addition‐fragmentation transfer (RAFT) polymerization is analyzed and discussed from a mathematical model discrimination point of view. The RAFT models are detailed nonlinear mechanistic models from the literature, where the debate is still ongoing about their validity. A sensitivity analysis is performed first on the simulated models in order to identify the most informative process (measured) outputs from the candidate models with respect to model discrimination. Next, sequential Bayesian Monte Carlo model discrimination (SBMCMD) methodology is applied to discriminate between the two rival models. The effectiveness of the SBMCMD procedure in discriminating between the two proposed models (both describing basic RAFT polymerization kinetic trends successfully) is explored further. Most informative experiments are designed and suggested based on the design of experiments step of the SBMCMD methodology. The methodology is capable of selecting the "real" model. Abstract : A sequential Bayesian methodology is used for discrimination between two rival models in RAFT polymerization. Number average molar mass turns out to be the best output process for model discrimination purposes. Although in this specific case, the type and reliability of experimental data available do not allow a clear determination of the "best" model, the technique provides valuable insights.