A QM Approach to the Calculation of Reactivity Ratios in Free‐Radical Copolymerization

Dossi, Marco; Moscatelli, Davide

doi:10.1002/mren.201100065

Cited by 19 publications

(9 citation statements)

References 103 publications

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“…Since then, many copolymer systems of common acrylates and methacrylates, styrene, and functional acrylates as well as of other common monomers were studied applying both the terminal model (TM) [108] and the penultimate unit effect (PUE) [109,110] model [101,107,[111][112][113][114][115], which are detailed in Figure 2. A list of relevant computational results for the copolymer systems studied by the application of QM methods is presented in Table 3.…”

Section: Copolymerizationmentioning

confidence: 99%

On the Use of Quantum Chemistry for the Determination of Propagation, Copolymerization, and Secondary Reaction Kinetics in Free Radical Polymerization

2015

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Throughout the last 25 years, computational chemistry based on quantum mechanics has been applied to the investigation of reaction kinetics in free radical polymerization (FRP) with growing interest. Nowadays, quantum chemistry (QC) can be considered a powerful and cost-effective tool for the kinetic characterization of many individual reactions in FRP, especially those that cannot yet be fully analyzed through experiments. The recent focus on copolymers and systems where secondary reactions play a major role has emphasized this feature due to the increased complexity of these kinetic schemes. QC calculations are well-suited to support and guide the experimental investigation of FRP kinetics as well as to deepen the understanding of polymerization mechanisms. This paper is intended to provide an overview of the most relevant QC results obtained so far from the investigation of FRP. A comparison between computational results and experimental data is given, whenever possible, to emphasize the performances of the two approaches in the prediction of kinetic data. This work provides a comprehensive database of reaction rate parameters of FRP to assist in the development of advanced models of polymerization and experimental studies on the topic.

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Section: Copolymerizationmentioning

confidence: 99%

On the Use of Quantum Chemistry for the Determination of Propagation, Copolymerization, and Secondary Reaction Kinetics in Free Radical Polymerization

2015

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“…Like Q-e model, the POR is an empirical scheme and its major limitation is that it requires six parameters as against the four (Q 1 , Q 2 , e 1 , e 2 ) required by the Q-e scheme for reactivity ratio prediction. In their study, Dossi and Moscatelli [18] proposed and validated a density functional theory (DFT)-based approach to determine reactivity ratios. Requirement of several molecular dynamics parameters and complexity of the method are the two main difficulties with this approach.…”

Section: Other Models For Prediction Of Reactivity Ratiosmentioning

confidence: 99%

“…Since these strategies do not need experimental data, they are expected to predict reactivity ratios for those comonomer pairs, which have not yet been copolymerized. A short overview of the various models proposed for the prediction of reactivity ratios and their advantages and limitations is provided by Dossi and Moscatelli [18]. In the following a brief review of the reactivity ratio prediction models based on molecular parameters is presented.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Reactivity Ratios in Free Radical Copolymerization from Monomer Resonance–Polarity (Q–e) Parameters: Genetic Programming-Based Models

Shrinivas¹,

Kulkarni

Shaikh

et al. 2015

International Journal of Chemical Reactor Engineering

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The principal deficiency of the widely utilized Alfrey-Price (AP) scheme for computing reactivity ratios in the widely used free radical copolymerization is that it ignores important factors, such as the steric effects. This often leads to inaccurate reactivity ratio predictions by AP model. Accordingly, in this study, exclusively data-driven, Q-e parameter-based new models have been developed for the reactivity ratio prediction in free radical copolymerization. In the model development, a novel artificial intelligence formalism known as "genetic programming (GP)" that performs symbolic regression has been employed. The GPbased models possess a different functional form than AP model. Further, parameters of GP-based models were finetuned using Levenberg-Marquardt (LM) nonlinear regression method. A comparison of AP, GP and GP-LM as well as artificial neural network (ANN)-based models indicates that GP and GP-LM models exhibit superior reactivity ratio prediction accuracy and generalization performance (with correlation coefficient magnitudes close to or greater than 0.9) when compared with AP and ANN models. The GPbased reactivity ratio prediction models developed here due to their higher accuracy and generalization capability have the potential of replacing the widely used AP models.

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“…Hence, for these reactions the reactivity can depend on the last two monomer units next to the radical center. The penultimate propagation rate coefficients are based on literature data . For MMA and styrene as comonomer pair, the so‐called implicit PMU propagation model is used, as it has shown to provide an adequate description of both copolymer composition and polymerization rate data over a broad range of monomer feeding compositions .…”

Section: Kinetic Modelmentioning

confidence: 99%

An evaluation of the impact of SG1 disproportionation and the addition of styrene in NMP of methyl methacrylate

et al. 2018

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A kinetic modeling study is presented for batch nitroxide mediated polymerization (NMP) of methyl methacrylate (MMA; nitroxide: N‐tert‐butyl‐N‐[1‐diethylphosphono‐(2,2‐dimethylpropyl)] (SG1)). Arrhenius parameters for SG1 disproportionation (A = 1.4 107 L mol−1 s−1; Ea = 23 kJ mol−1) are reported, based on homopolymerization data accounting for unavoidable temperature variations with increasing time, that is, nonisothermicity. For low targeted chain lengths (TCLs ≤ 300), this nonisothermicity is also relevant for NMP of MMA with a small amount of styrene. Parameter tuning to copolymerization data confirms a penultimate monomer unit effect for activation (sa2 = ka12/ka22=6.7; 363 K; 1: MMA; 2: styrene). To obtain, for a broad TCL range (up to 800), a dispersity well below 1.3 an initial styrene mass fraction of ca. 10% is required. An interpretation of the comonomer incorporation is performed by calculating the fractions of activation‐growth‐deactivation cycles with a given amount of monomer units and the copolymer composition distribution. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2545–2559, 2018

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A QM Approach to the Calculation of Reactivity Ratios in Free‐Radical Copolymerization

Cited by 19 publications

References 103 publications

On the Use of Quantum Chemistry for the Determination of Propagation, Copolymerization, and Secondary Reaction Kinetics in Free Radical Polymerization

On the Use of Quantum Chemistry for the Determination of Propagation, Copolymerization, and Secondary Reaction Kinetics in Free Radical Polymerization

Prediction of Reactivity Ratios in Free Radical Copolymerization from Monomer Resonance–Polarity (Q–e) Parameters: Genetic Programming-Based Models

An evaluation of the impact of SG1 disproportionation and the addition of styrene in NMP of methyl methacrylate

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