459662 A Full Exploitation of the Pulsed Laser Polymerization Technique to Assess All Important Rate Coefficients in Acrylate Radical Polymerization

Sunday, November 13, 2016: 5:00 PM
Continental 1 (Hilton San Francisco Union Square)
Yoshi W. Marien1, Paul H.M. Van Steenberge1, Katrin B. Kockler2,3, Christopher Barner-Kowollik2,3, Marie-Françoise Reyniers1, Dagmar R. D'hooge1,4 and Guy B. Marin1, (1)Laboratory for Chemical Technology (LCT), Ghent University, Ghent, Belgium, (2)Preparative Macromolecular Chemistry, Institut für Technische Chemie und Polymerchemie, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany, (3)Institut für Biologische Grenzflächen, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany, (4)Department of Textiles, Ghent University, Ghent, Belgium

Free-radical polymerization (FRP) processes are extensively applied for the production of macromolecular materials, since they allow the synthesis of a large variety of vinyl polymers under mild reaction conditions and with a high tolerance toward impurities.[1] Despite the high industrial importance of FRP, the quantitative understanding of radical polymerization kinetics is still incomplete. Accurate knowledge of Arrhenius parameters and rate coefficients for key reactions such as chain initiation, propagation, and termination is a prerequisite for FRP design, particularly when conducted in large-scale reactors, in which temperature control is a key issue.

Figure 1. Determination of kp via the characteristic inflection points of the PLP-SEC trace.

One of the most established experimental techniques to determine intrinsic rate coefficients in radical polymerization is pulsed laser polymerization (PLP; Figure 1). This technique, as originally introduced by Olaj and coworkers,[2] has been extensively used to obtain the propagation rate coefficient kp. In PLP, photoinitiator radical fragments are consecutively formed via laser pulses with a frequency ν (or dark time Δt = ν-1), so that under well-defined conditions (e.g. limited monomer conversion) in the corresponding size exclusion chromatography (SEC) trace inflection points (Lj; j = 1, 2, …) can be identified that are directly linked to kp via (Figure 1):

                                                              Lj = kp [M]0 (jΔt)                                               (1)

Currently, the PLP technique has been used to accurately measure a wide range of kp values.[3-7] For standard monomers such as styrene and methyl methacrylate, reliable values are already available. A special case is polymerizations with several radical types, for which the obtained kp (Equation (1)) must be seen as an apparent averaged one (kp,app). For example, in acrylate radical polymerization with applications e.g. in the coating and paint industry both secondary end-chain radicals (ECRs) and tertiary mid-chain radicals (MCRs) can be present (Figure 2), leading to the need for the determination of kp,ecr and kp,mcr. The tendency of ECRs to switch to MCRs is expressed by the backbiting rate coefficient kbb (Figure 2). Currently, a limited number of experimental methods exists for the determination of kbb. One of the most promising methods was introduced by Nikitin and coworkers,[8] based on the observation that at high ν, Equation (1) relates to kp,ecr , while at low ν, Equation (1) is influenced by both kp,ecr and kp,mcr. From the onset of the sharp decrease in kp,app with decreasing ν, Nikitin and coworkers[8] assessed kbb. Very recently Wenn and Junkers[9] suggested a more simplified procedure, based on the position of the sigmoidal fit to kp,app(ν).

Figure 2. Backbiting reaction leading to a transformation of the radical nature from secondary to tertiary in acrylate radical polymerization; dominant path via a cyclic six-membered transition state.[10]

In the present work, a novel and highly accurate method to determine kbb is presented, using a detailed kinetic Monte Carlo (kMC) model and considering 2,2-dimethoxy-2-phenylacetophenone (DMPA) as photoinitiator and n-butyl acrylate as monomer. For different solvent volume fractions (0-0.75), with this model regression analysis is applied to inflection point data in the low frequency domain (~100 s-1), which can be easily scanned with less expensive PLP equipment. The novelty of the method lies in the variation of the solvent volume fraction, which allows to independently change the average MCR lifetime and to obtain a high sensitivity toward kbb.

The developed kMC model is also capable of the accurate simulation of the complex SEC trace for acrylate PLP, hence, allowing a kinetic analysis transcending the correct simulation of only inflection points for kp and kbb determination. In particular, hidden information on chain initiation and short-long termination can be elegantly extracted. It is for instance confirmed that inhibition with one of the DMPA radical fragments as formed by photoinitiation is taking place by detailed analysis of the peak intensities. In addition, it is demonstrated that the extracted PLP data for short-long termination (kt,app,ij; i,j: chain length) can be used to benchmark FRP diffusion models in the less studied regime of diluted conditions or low monomer conversions. Such information is crucial to design not only FRP processes but also novel radical polymerization processes, aiming at improved microstructural control.

[1] D. R. D’hooge, P. H. M. Van Steenberge, M.-F. Reyniers, G. B. Marin, Prog. Polym. Sci. 2016, in press (DOI: 10.1016/j.progpolymsci.2016.04.002).

[2] O. F. Olaj, I. Bitai, F. Hinkelmann, Macromol. Chem. Phys. 1987, 188, 1689.

[3] K. B. Kockler, A. P. Haehnel, T. Junkers, C. Barner-Kowollik, Macromol. Rapid Commun. 2016, 37, 123.

[4] S. Beuermann,  M. Buback, Prog. Polym. Sci. 2002, 27, 191.

[5] A. P. Haehnel, M. Schneider-Baumann, L. Arens, A. M. Misske, F. Fleischhaker, C. Barner-Kowollik, Macromolecules 2014, 47, 3483.

[6] T. Junkers, S. P. S. Koo, C. Barner-Kowollik, Polym. Chem. 2010, 1, 438.

[7] C. Barner-Kowollik, F. Gunzler, T. Junkers, Macromolecules 2008, 41, 8971.

[8] A. N. Nikitin, R. A. Hutchinson, M. Buback, P. Hesse, Macromolecules 2007, 40, 8631.

[9] B. Wenn,  T. Junkers, Macromol. Rapid Commun. 2016, 37, 781.

[10] D. Konkolewicz, S. Sosnowski, D. R. D'hooge, R. Szymanski, M. F. Reyniers, G. B. Marin, K. Matyjaszewski, Macromolecules 2011, 44, 8361.


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