Prediction of the full molecular weight distribution in RAFT polymerization using probability generating functions

Fortunatti, Cecilia; Sarmoria, Claudia; Brandolin, Adriana; Asteasuain, Mariano

doi:10.1016/j.compchemeng.2014.02.017

Cited by 15 publications

(15 citation statements)

References 14 publications

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“…For the higher CTA content, polymerization rate and molecular weights are lower. This is a typical behavior of RAFT systems and has been reported elsewhere . In addition, the proportion of one‐arm dormant chains with respect to the intermediate adduct is higher.…”

Section: Resultssupporting

confidence: 91%

“…As previously reported, for these kinetic parameters the MWD predicted by the SF theory presents a bimodal shape . The small k f of the SF theory causes the population of the intermediate adduct species to be significant, leading to the high molecular weight peak of the bimodal MWD.…”

Section: Resultsmentioning

confidence: 94%

“…In previous works, a mathematical model of a RAFT polymerization system that follows the SF kinetics was developed . This mathematical model was capable of predicting not only the average properties but also the full MWD of the polymer by means of the probability generating function (pgf) transform.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modeling of RAFT Polymerization using Probability Generating Functions. Detailed Prediction of Full Molecular Weight Distributions and Sensitivity Analysis

Fortunatti

Sarmoria

Brandolin

et al. 2014

Macro Reaction Engineering

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A mathematical model of RAFT polymerization processes is presented capable of predicting the full molecular weight distribution (MWD) through the use of probability generating functions (pgf). The bivariate distribution of the intermediate RAFT species is calculated. The model is able to work with the three kinetic mechanisms currently under discussion for explaining the observed behavior of this type of polymerization. For comparison purposes, the population balances are also solved by direct integration of the resulting equations. The results show that the pgf technique allows obtaining accurate solutions with very small computational times for systems of any average molecular weight. Spurious oscillations observed in the high molecular weight tail of the MWD can be easily disregarded. A sensitivity analysis over several of the kinetic constants is also performed, showing the effects of changing their values over several orders of magnitude. This analysis aims to showcase the enormous potential of the pgf technique for modeling and optimization of complex polymerization kinetics.

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Section: Resultssupporting

confidence: 91%

Section: Resultsmentioning

confidence: 94%

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Modeling of RAFT Polymerization using Probability Generating Functions. Detailed Prediction of Full Molecular Weight Distributions and Sensitivity Analysis

Fortunatti

Sarmoria

Brandolin

et al. 2014

Macro Reaction Engineering

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“…Also more complex polymer architectures have been modeled in a later stage. [29,47,61,68,[84][85][86] For example, Junkers and Barner-Kowollik [29] used the PREDICI software package to find optimal reaction conditions for RAFT polymerization of acrylates, which is characterized by branch formation. Moreover, Chaffey-Millar et al [85] constructed the CLD of star polymers made via the so-called R-group approach, considering PREDICI output for the explicit simulation of the arm lengths.…”

Section: Kinetic Modeling Techniquesmentioning

confidence: 99%

“…Alternatively, the RAFT polymerization CLD can be obtained deterministically via solving probability generating functions by performing discrete Laplace transformations. [47,86] To reduce the computational cost in deterministic modeling techniques, the potential use of the pseudo steady state assumption (PSSA) has been intensively studied. [75,77,[87][88][89][90] By PSSA the number of ODEs can be reduced and a set of differential algebraic equations (DAEs) that is less stiff needs to be solved so that the computational cost is inherently reduced.…”

Section: Kinetic Modeling Techniquesmentioning

confidence: 99%

An Update on the Pivotal Role of Kinetic Modeling for the Mechanistic Understanding and Design of Bulk and Solution RAFT Polymerization

Rybel

Steenberge

Reyniers

et al. 2016

Macro Theory & Simulations

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The importance of the development of kinetic modeling tools to mechanistically understand and design bulk and solution reversible addition fragmentation chain transfer (RAFT) polymerization is highlighted. Both deterministic and stochastic kinetic modeling methods are covered, considering a detailed reaction scheme and accounting for the impact of diffusional limitations on the reaction rates. A novel strategy is introduced to fundamentally calculate the diffusional contributions for the apparent RAFT addition and fragmentation rate coefficients. Next to literature examples, case studies are included to demonstrate that detailed theoretical studies are indispensable to completely map the effect of the polymerization conditions and RAFT agent reactivity on the control over microstructural properties and the overall polymerization time. Guidelines for future kinetic modeling activities are formulated to enhance joined theoretical and experimental research.

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Mathematical Modelling of RAFT Polymerization

López‐Domínguez

Zapata‐González²,

Saldívar‐Guerra

et al. 2021

RAFT Polymerization

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Prediction of the full molecular weight distribution in RAFT polymerization using probability generating functions

Cited by 15 publications

References 14 publications

Modeling of RAFT Polymerization using Probability Generating Functions. Detailed Prediction of Full Molecular Weight Distributions and Sensitivity Analysis

Modeling of RAFT Polymerization using Probability Generating Functions. Detailed Prediction of Full Molecular Weight Distributions and Sensitivity Analysis

An Update on the Pivotal Role of Kinetic Modeling for the Mechanistic Understanding and Design of Bulk and Solution RAFT Polymerization

Mathematical Modelling of RAFT Polymerization

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