Temperature Dependence of the Number of Defect-Structures in Poly(vinylidene fluoride).

Poly(vinylidene fluoride) (PVDF) is predominantly characterized by alternating CH2 and CF2 units in a polymer backbone, originating from the head-to-tail addition of monomers or regular propagation. Due, to a small extent, to inverse monomer addition, so-called defect structures occur which influence the macroscopic properties of PVDF significantly. The amount of defect structures in the material is determined by the polymerization conditions. Here, the temperature dependence of the fraction of defect structures in PVDF obtained from polymerizations between 45 and 90 °C is reported. We utilized 19F-NMR spectroscopy to determine the fraction of defect structures as a function of temperature. To derive kinetic data, the polymerization of VDF is considered a quasi-copolymerization described by the Terminal Model involving four different propagation reactions. Based on the experimentally determined temperature-dependent fractions of defect structures, the known overall propagation rate coefficient, and taking into account the self-healing behavior of the macroradical, the Arrhenius parameters of the individual propagation rate coefficients were determined using the Monte Carlo methods.

Ab initio kinetic Monte Carlo simulation of seeded emulsion polymerizations of styrene.

Seeded emulsion polymerizations of styrene are modeled on the basis of a detailed kinetic scheme accounting for the chain length and conversion dependence of termination rate coefficients. A holistic kinetic Monte Carlo approach was developed, which simulates the elemental reactions in the aqueous phase, the transfer of radicals into individual particles, and the radical polymerization in each particle based on a complete kinetic model. Experimentally-derived particle size distributions are used as input for the simulations. The required rate coefficients were taken from literature. Without any adjustment of this data a very good agreement between simulation results and experimental data is found. The validation of the model is performed based on monomer conversion - time data and full molar mass distributions.

Propagation Kinetics of Isoprene-Glycidyl Methacrylate Copolymerizations Investigated via PLP-SEC.

Pulsed-laser polymerization combined with polymer analysis by NMR and size-exclusion chromatography is used to study the radical copolymerization kinetics of isoprene (IP) with glycidyl methacrylate (GMA). The copolymer is characterized by a close-to-alternating microstructure, with the addition of IP leading to a significant decrease in the composition-averaged propagation rate coefficient. A rigorous fitting strategy is developed to fit a mixed penultimate model to the data, with the selectivity of the IP, but not the GMA, macroradical dependent on the penultimate unit.

AI-Based Forecasting of Polymer Properties for High-Temperature Butyl Acrylate Polymerizations.

High-temperature polymerizations involving self-initiation of the monomer are attractive because of high reaction rate, comparable lower viscosities, and no need for an additional initiator. However, the polymers obtained show a more complex microstructure, e.g., with specific branching levels or significant amounts of macromonomer. Simulations of the polymerization processes are powerful tools to gain a deeper understanding of the processes and the elemental reactions at the molecular level. However, simulations can be computationally demanding, requiring significant time and memory resources. Therefore, this study aims at applying AI-based forecasting of tailored polymer properties and using a kinetic Monte Carlo simulator for the generation of training and test data. The applied machine learning (ML) models (random forest and kernel density (KD) regression) predict monomer concentration, macromonomer content, and full molar mass distributions as a function of time, as well as the average branching level with an excellent performance (R 2 (coefficient of determination) > 0.99, MAE (mean absolute error) < 1% for kernel density regression). This study explores the number of training data needed for reliable and accurate predictions in ML models. Explainability methods reveal that the importance of input variables in ML models aligns with expert knowledge.

Reverse Engineering of Radical Polymerizations by Multi-Objective Optimization.

Reverse engineering is applied to identify optimum polymerization conditions for the synthesis of polymers with pre-defined properties. The proposed approach uses multi-objective optimization (MOO) and provides multiple candidate polymerization procedures to achieve the targeted polymer property. The objectives for optimization include the maximal similarity of molar mass distributions (MMDs) compared to the target MMDs, a minimal reaction time, and maximal monomer conversion. The method is tested for vinyl acetate radical polymerizations and can be adopted to other monomers. The data for the optimization procedure are generated by an in-house-developed kinetic Monte-Carlo (kMC) simulator for a selected recipe search space. The proposed reverse engineering algorithm comprises several steps: kMC simulations for the selected recipe search space to derive initial data, performing MOO for a targeted MMD, and the identification of the Pareto optimal space. The last step uses a weighted sum optimization function to calculate the weighted score of each candidate polymerization condition. To decrease the execution time, clustering of the search space based on MMDs is applied. The performance of the proposed approach is tested for various target MMDs. The suggested MOO-based reverse engineering provides multiple recipe candidates depending on competing objectives.

Self-Initiated Butyl Acrylate Polymerizations in Bulk and in Solution Monitored By In-Line Techniques.

High-temperature acrylate polymerizations are technically relevant, but yet not fully understood. In particular the mechanism and the kinetics of the thermal self-initiation is a topic of current research. To obtain more detailed information the conversion dependence of the polymerization rate, rbr, is determined via in-line DSC and FT-NIR spectroscopy for reactions in bulk and in solution at temperatures ranging from 80 to 160 °C. Solution polymerizations revealed that dioxane is associated with the highest rbr, while aromatic solvents result in the lowest values of rbr. Interestingly, rbr for polymerizations in solution with dioxane depends on the actual monomer concentration at a given time in the system, but is not depending on the initial monomer concentration. The overall rate of polymerization in bulk and in solution is well represented by an equation with three or four parameters, respectively, being estimated by multiple linear regression and the temperature as additional parameter.

Polymer Electrolyte Membranes Prepared by Graft Copolymerization of 2-Acrylamido-2-Methylpropane Sulfonic Acid and Acrylic Acid on PVDF and ETFE Activated by Electron Beam Treatment.

Polymer electrolyte membranes (PEM) for potential applications in fuel cells or vanadium redox flow batteries were synthesized and characterized. ETFE (poly (ethylene-alt-tetrafluoroethylene)) and PVDF (poly (vinylidene fluoride)) serving as base materials were activated by electron beam treatment with doses ranging from 50 to 200 kGy and subsequently grafted via radical copolymerization with the functional monomers 2-acrylamido-2-methylpropane sulfonic acid and acrylic acid in aqueous phase. Since protogenic groups are already contained in the monomers, a subsequent sulfonation step is omitted. The mechanical properties were studied via tensile strength measurements. The electrochemical performance of the PEMs was evaluated by electrochemical impedance spectroscopy and fuel cell tests. The proton conductivities and ion exchange capacities are competitive with Nafion 117, the standard material used today.

Kinetic Monte Carlo Simulation Based Detailed Understanding of the Transfer Processes in Semi-Batch Iodine Transfer Emulsion Polymerizations of Vinylidene Fluoride.

Semi-batch emulsion polymerizations of vinylidene fluoride (VDF) are reported. The molar mass control is achieved via iodine transfer polymerization (ITP) using IC4F8I as chain transfer agent. Polymerizations carried out at 75 °C and pressures ranging from 10 to 30 bar result in low dispersity polymers with respect to the molar mass distribution (MMD). At higher pressures a significant deviation from the ideal behavior expected for a reversible deactivation transfer polymerization occurs. As identified by kinetic Monte Carlo (kMC) simulations of the activation⁻deactivation equilibrium, during the initialization period of the chain transfer agent already significant propagation occurs due to the higher pressure, and thus, the higher monomer concentration available. Based on the kMC modeling results, semi-batch emulsion polymerizations were carried out as a two pressure process, which resulted in very good control of the MMD associated with a comparably high polymerization rate.

Preparation of Polymer Electrolyte Membranes via Radiation-Induced Graft Copolymerization on Poly(ethylene-alt-tetrafluoroethylene) (ETFE) Using the Crosslinker N,N'-Methylenebis(acrylamide).

Polymer electrolyte membranes (PEM) prepared by radiation-induced graft copolymerization are investigated. For this purpose, commercial poly(ethylene-alt-tetrafluoroethylene) (ETFE) films were activated by electron beam treatment and subsequently grafted with the monomers glycidyl methacrylate (GMA), hydroxyethyl methacrylate (HEMA) and N,N'-methylenebis(acrylamide) (MBAA) as crosslinker. The target is to achieve a high degree of grafting (DG) and high proton conductivity. To evaluate the electrochemical performance, the PEMs were tested in a fuel cell and in a vanadium redox-flow battery (VRFB). High power densities of 134 mW∙cm-2 and 474 mW∙cm-2 were observed, respectively.