Detailed Modeling, Simulation, and Parameter Estimation of Nitroxide Mediated Living Free Radical Polymerization of Styrene 1*

Bonilla, Jose C.; Saldívar, Enrique; Flores‐Tlacuahuac, Antonio; Vivaldo‐Lima, Eduardo; Pfaendner, Rudolf; Tiscareño-Lechuga, Fernando

doi:10.1081/pre-120016296

Cited by 59 publications

(90 citation statements)

References 26 publications

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“…First, extrapolation of the conversion versus time profile for the initial stages shows that there must be an induction period for the reactions using TBEC as the initiator. The induction period has already been described by Bonilla et al 22 and Butté et al 23 This occurs because the rate of initiator decomposition is slow compared to that when BPO is the added initiator. After this period, there still will be a significant concentration of TBEC present and so extra radical production will continue, because the half life of TBEC is much higher (t 1/2 = 40.6 min at T = 125…”

Section: Effect Of the Type Of Initiatormentioning

confidence: 91%

Effect of initiator type and concentration on polymerization rate and molecular weight in the bimolecular nitroxide‐mediated radical polymerization of styrene

Nogueira¹,

Gonçalves²,

Lona³

et al. 2010

Adv Polym Technol

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ABSTRACT:To increase the polymerization rate in the bimolecular nitroxide-mediated radical polymerization (NMRP) of styrene, without using expensive non-commercial reagents, an experimental study using 2,2,6,6-tetramethyl-1-piperidinoxyl as a controller and tert-butylperoxy 2-ethylhexyl carbonate (TBEC) as the initiator was carried out. The basis for comparison was the bimolecular NMRP of styrene with dibenzoyl peroxide as initiator. It was found that faster polymerization rates and still relatively low polydispersities were possible using TBEC. C

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Section: Effect Of the Type Of Initiatormentioning

confidence: 91%

Effect of initiator type and concentration on polymerization rate and molecular weight in the bimolecular nitroxide‐mediated radical polymerization of styrene

Nogueira¹,

Gonçalves²,

Lona³

et al. 2010

Adv Polym Technol

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“…We are not offering a literature (or background) review on NMRP, as the emphasis here is to demonstrate the benefits of specific aspects of the Bayesian design of experiments and not the kinetics of NMRP. For the interested reader, kinetic and modeling information closely related to the case study presented herein could be found in references [5][6][7]. The case study that follows implements the Bayesian design steps of Table 3, with added emphasis on the interpretation of diagnostic criteria and information measures.…”

Section: Resultsmentioning

confidence: 99%

Diagnostic Checks and Measures of Information in the Bayesian Design of Experiments with Complex Polymerizations

Nabifar

McManus

Vivaldo‐Lima

et al. 2011

Macromolecular Symposia

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Summary: A case study is presented from the bimolecular nitroxide-mediated radical polymerization of styrene. A Bayesian approach is implemented in order to design two optimal sequences of 2-trials each, and compared with a standard fractional factorial design. The improved effectiveness of the Bayesian design is demonstrated through a discussion of diagnostic criteria (on the quality of prior knowledge and the significance of estimated effects) and enhanced information content measures.

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“…For the interested reader, kinetic and modeling information closely related to the case studies presented herein could be found in [11][12][13][14][15]. The following case studies implement the Bayesian design steps of Tab.…”

Section: Resultsmentioning

confidence: 99%

“…To generate the initial values for the parameter means, a 2 3 standard factorial design is used and the corresponding molecular weight responses are obtained from a general mechanistic model developed for NMRP of styrene [11,12,14]. This model is based on the reaction mechanism and consists of mole balances for the main recipe ingredients and detailed population balances for the molecular weight part, which eventually leads to the development of 22 ordinary differential equations.…”

Section: Case Study 1: N-trials Experiments Vs Sequences Of Fewer Trialsmentioning

confidence: 99%

A Sequential Iterative Scheme for Design of Experiments in Complex Polymerizations

et al. 2010

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The Bayesian design approach is an experimental design technique which has many advantages over standard experimental designs. It incorporates prior knowledge about the process into the design to suggest a set of future experiments in an optimal, sequential, and iterative fashion. Since for many complex polymerizations prior information is available, either in the form of experimental data or mathematical models, the use of Bayesian design methodology could be beneficial. Exploiting this technique in complex polymerizations could hopefully lead to optimal performance in fewer trials, thus saving time and money. Advantages of the Bayesian design approach are illustrated via case studies drawn from the nitroxide-mediated radical polymerization as an example. However, since this technique is perfectly general, it can be potentially applied to other polymerization variants. IntroductionFor many complex polymerizations some sort of experimental information might be available (even in preliminary form) either from industrial or exploratory laboratory work. In addition, mathematical models (of different level of detail, empirical, or semi-mechanistic) usually do exist, albeit with unreliable and/or highly correlated parameters and sometimes even unverified mechanistic bases. Hence, ideas from the modelbased design of experiments applied as early as possible (in the design/brainstorming stage) can be very beneficial for the clarification of polymerization kinetics.Standard experimental design methods, e.g., (fractional) factorial designs, have been employed extensively and are useful in optimizing a wide variety of systems. However, these designs usually suffer from several limitations and cannot handle certain situations, as listed in Tab. 1. In addition, these approaches do not take direct advantage of the considerable prior knowledge that is available about the reaction system to design experiments. As prior information is already available within existing data, it is logical that it should be used in order to contribute to the optimality of the designed experiments, and hence to improved models and performance of the process in question.Although some of the issues with standard designs cited in Tab. 1 have solutions, these are usually known only to experts in the design of experiments. Hence, it often happens that the practicing scientist or engineer is not aware of the solutions, cannot easily handle the issues faced, and gives up on the use of statistical designs altogether. Using more efficient experimental designs which can accommodate these restrictions could lead to optimal performance in fewer trials, thus saving time and money.Such efficient designs can be found in the family of Bayesian approaches. The Bayesian design is a powerful and in the poly- [9]. The common characteristic of all these Bayesian framework applications (including the ones mentioned above as well as others from the scientific literature not cited herein for the sake of brevity), to a split of about 7 or 8 to 2, is that they are concern...

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Detailed Modeling, Simulation, and Parameter Estimation of Nitroxide Mediated Living Free Radical Polymerization of Styrene 1*

Cited by 59 publications

References 26 publications

Effect of initiator type and concentration on polymerization rate and molecular weight in the bimolecular nitroxide‐mediated radical polymerization of styrene

Effect of initiator type and concentration on polymerization rate and molecular weight in the bimolecular nitroxide‐mediated radical polymerization of styrene

Diagnostic Checks and Measures of Information in the Bayesian Design of Experiments with Complex Polymerizations

A Sequential Iterative Scheme for Design of Experiments in Complex Polymerizations

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