Macromolecular Reaction Engineering最新文献

Polyureas spread their portfolio of applications in the last years due to their unique mechanical and chemical properties. However, the scale-up required to sustain this growing interest brings about safety and sustainability concerns. First, the possibility of avoiding the use of isocyanates is compelling. To fill this gap, an innovative isocyanate-free route has been proposed based on the step-growth polymerization of a diazirine with an aliphatic diamine. Still, the selection of an environmentally friendly solvent and the proper understanding of the kinetic mechanism of this polymerization remain as open points to be urgently cleared to favor the adoption of this appealing route. For this reason, the present work pretends to establish a safe solvent for the step-growth polymerization of N,N’-(hexane-1,6-diyl)bis(aziridine-1-carboxamide) based on the evaluation of its Hansen solubility parameters. Then, a systematic kinetic analysis is performed at different stoichiometric ratios of hexamethylenediamine and diaziridine (r) to develop a kinetic model for their co-polymerization, by deriving the rate constant associated with the reaction and its dependence from temperature. With the aid of this model, the polymer microstructure can be reliably predicted and tuned by acting on the process conditions and r, thus expanding the interest in this new class of materials.

Molecular weight distribution (MWD) is crucial for the product performance of polymers. In order to explore how process conditions affect molecules with different chain lengths, this study conducts a large number of polystyrene process simulations based on polymerization kinetics and validates them through the pilot plant data to generate a reliable dataset. Machine learning methods are employed to predict average molecular weights and conversion rates. Compared to extreme gradient boosting (XGBoost) and support vector regression (SVR), the fully connected neural network (FCNN) shows the best performance. Furthermore, an improved FCNN model with feature extractor and residual structure is developed to predict MWD accurately. The polymer molecules are divided into 10 bins based on chain length, and the influence of process conditions is revealed through SHapley Additive exPlanations (SHAP). Notably, reducing the feed mass fraction of ethylbenzene and increasing the charging coefficient of the second pre-polymerization reactor will lead to an increase of low molecular weight polymers. Raising the temperature of the second pre-polymerization reactor will promote a decrease in the proportion of small molecule polymers and ultra-large molecule polymers, thereby narrowing MWD. In addition, process conditions for polystyrene with specific target MWD can be effectively predicted by machine learning.

Controlling the chemical composition distribution (CCD) of copolymers through optimal monomer addition profiles (OMAP) is of great importance for their properties. However, the requirement to know various kinetic parameters of the polymerization often complicates obtaining such addition profiles on a time basis. A simpler approach is to forecast OMAP based on monomer conversions, which only requires the reactivity ratios for solution or bulk polymerizations. For emulsion copolymerization, it's also necessary to include the solubilities of the monomers in both water and polymer. Starting with an OMAP on a conversion basis, one can establish an OMAP on time basis by performing two or three experiments measuring the conversion-time relationships as part of the iterative process. In this paper, an improved procedure is described that requires minimal knowledge of kinetics parameters and therefore is very suitable for monomers where most kinetic parameters are not known, like biobased monomers. The process starts with an initial guess of the kinetics and, within 2–3 iterations, results in a time-based OMAP. Examples are included for solution copolymerizations.

Front Cover: Micro glass beads make the reaction spaces to synthesize polymer nano particles without surfactant. Particle size is controllable by the size of the micro glass beads packed in the reactor. More details can be found in article 2400009 by Tetsuya Yamamoto and co-workers.

Reversible addition-fragmentation chain transfer (RAFT) emulsion polymerization with a well-designed amphiphilic macroRAFT agent as a surfactant has been well-developed as a powerful tool to synthesize high molecular weight multiblock copolymers. However, the polymerization kinetics research has been mostly limited to styrene polymerization. It has been reported that the dependence of the particle number on initiator concentration is described by N_p∝[I]^−0.4 in amphiphilic macroRAFT-mediated emulsion polymerization of styrene, which surprisingly deviates from the classical Smith–Eward equation. In the current study, the dependence of polymerization kinetics on the initiator concentration in the RAFT emulsion polymerization of 2-ethylhexyl acrylate (EHA) is investigated. It is revealed that the dependence of the particle number on initiator concentration (N_p∝[I]^−0.29) is similar to that of styrene but the exponent is less. Additionally, compared to styrene polymerization, the inhibition period in EHA polymerization is significantly extended due to the much lower water-solubility of EHA.