Polymer Reaction Engineering Research Articles

Improved Approach for ab Initio Calculations of Rate Coefficients for Secondary Reactions in Acrylate Free-Radical Polymerization.

Secondary reactions in radical polymerization pose a challenge when creating kinetic models for predicting polymer structures. Despite the high impact of these reactions in the polymer structure, their effects are difficult to isolate and measure to produce kinetic data. To this end, we used solvation-corrected M06-2X/6-311+G(d,p) ab initio calculations to predict a complete and consistent data set of intrinsic rate coefficients of the secondary reactions in acrylate radical polymerization, including backbiting, β-scission, radical migration, macromonomer propagation, mid-chain radical propagation, chain transfer to monomer and chain transfer to polymer. Two new approaches towards computationally predicting rate coefficients for secondary reactions are proposed: (i) explicit accounting for all possible enantiomers for reactions involving optically active centers; (ii) imposing reduced flexibility if the reaction center is in the middle of the polymer chain. The accuracy and reliability of the ab initio predictions were benchmarked against experimental data via kinetic Monte Carlo simulations under three sufficiently different experimental conditions: a high-frequency modulated polymerization process in the transient regime, a low-frequency modulated process in the sliding regime at both low and high temperatures and a degradation process in the absence of free monomers. The complete and consistent ab initio data set compiled in this work predicts a good agreement when benchmarked via kMC simulations against experimental data, which is a technique never used before for computational chemistry. The simulation results show that these two newly proposed approaches are promising for bridging the gap between experimental and computational chemistry methods in polymer reaction engineering.

What Can Industrial Catalytic Olefin Polymerization Plants Tell Us About Reaction Kinetics? From Production Rate and Residence Time to Catalyst Reaction Performance.

AbstractThe information available in daily plant operation data is not fully exploited by polymer reaction engineers: what do the catalytic olefin polymerization plants tell? In this article, a method is proposed to increase catalyst and process know‐how, based on experimentally acquired production rate results, coming from a continuous tandem reactor polymerization process. The polymer reaction engineering methodology is also discussed in detail for connecting the catalyst reaction performance to the expected activity profile and yield for batch operation, together with the residence time distribution effect for continuous operation. The potential of the proposed methodology is highlighted with a theoretical example and the effectiveness of the method is demonstrated with an applied example, accurately estimating deactivation parameter values for two catalysts based on plant information and, validated based on small‐scale polymerization experiments.

A Signal‐To‐Noise‐Ratio‐Based Automated Algorithm to accelerate Kinetic Monte Carlo Convergence in Basic Polymerizations

AbstractKinetic Monte Carlo (kMC) modelling is ubiquitous to simulate the time evolution of (bio)chemical processes, specifically if populations are involved. A recurring task is the selection of the smallest control volume that leads to convergence, which means that the model outputs are accurate and sufficiently free from stochastic noise and do not significantly change upon further increasing this volume. Selecting a too high (safe) control volume leads to an excessive simulation time, while many small incremental control volume increases are inefficient. This work therefore presents an automated tool to determine the smallest control volume leading to convergence. The tool is illustrated for (intrinsic) free radical and nitroxide mediated polymerization (FRP/NMP), in which the chain length distribution (CLD) is a crucial output. The algorithm starts with a very low volume to then check if the desired (monomer) conversion can be reached, the number average chain length is accurate, and finally the signal‐to‐noise (SNR) ratio at the CLD level is below a threshold. The execution time of the algorithm is less than twice the time of running the converged simulation directly, hence, saving tremendous time in setting up a kMC simulation and facilitating benchmark studies even beyond polymer reaction engineering applications.

Polymer reaction engineering meets explainable machine learning

Due to the complex polymerization technique and statistical composition of the polymer, tailoring its characteristics is a challenging task. Modeling of the polymerizations can contribute to deeper insights into the process. This study applies state-of-the-art machine learning (ML) methods for modeling and reverse engineering of polymerization processes. ML methods (random forest, XGBoost and CatBoost) are trained on data sets generated by an in house developed kinetic Monte Carlo simulator. The applied ML models predict monomer concentration, average molar masses and full molar mass distributions with excellent accuracy (R2> 0.96). Reverse engineering results delivering the polymerization recipe for a targeted molar mass distribution are less accurate, but still only minor deviations from the targeted molar mass distribution are seen. The influences of the input variables in ML models obtained by explainability methods correspond to the expert expectations.

A retrospective look at developments in size exclusion chromatography from the early Hamielec research era to the present day

AbstractAt the start of Archie Hamielec's research career, size exclusion chromatography (SEC) (including gel permeation chromatography [GPC]) was in its infancy as an analytical and preparative laboratory technique in polymer science and engineering. As described below, Archie Hamielec saw its potential early in his academic career and was among the group of pioneers who took it from a developing technique to what it is now. His group's work was instrumental in improving the understanding of the mechanism of separation in SEC and setting up best procedures for sample handling. In addition, much work was done to assess detectors to improve data collection and to obtain consistent/reliable measurements. Importantly, he established it as a key method for obtaining molecular size information to support research in polymer reaction engineering. Nowadays we take it for granted that we can use SEC to obtain molecular weight data rapidly and reliably with user‐friendly computer‐aided analyses. This ease of routine practicality stems from the base that the early researchers built. Although now it is a well‐developed (almost ‘black box’) method there are still areas of novel interest into analysis of complex polymer samples. This has led to modern developments forging ahead in expanding the applicability of SEC as an analytical technique with the exploitation of multi‐detector systems and novel detectors.

Computer-aided design of initiator utilization strategies for vinyl chloride suspension polymerization with constant reaction rate

From the viewpoint of polymer reaction engineering (PRE), polymerization with constant reaction rate is beneficial for reducing manual operations and promoting production efficiency. Using vinyl chloride (VC) suspension polymerization as a model system, manipulating the radical concentration by radical concentration-dependent optimization (RCO)-based initiators mixing (IM) strategy or continuous initiator dosing (CiD) strategy enables the polymerization with constant reaction rate. Case studies have been done to illustrate how the RCO-based IM strategy works for binary initiator combination screening and how to conduct VC suspension polymerizations with different reaction rates using RCO-based IM strategy. Importantly, decomposition kinetic parameters estimated for virtual initiators can be used for guiding the design of the capable initiators. Moreover, in the single initiator systems, the applicability of the RCO-based CiD strategy has been demonstrated by conducting VC suspension polymerization with different reaction rates and different temperatures. Optimal dosing rate profiles of the initiator can be provided for the cases demonstrated. It is expected that both RCO-based IM and CiD strategies can be extended to other free radical polymerization systems.

Simulation of irreversible and reversible degradation kinetics of linear polymers using sectional moment method

In the field of polymer reaction engineering, accurately simulating polymer degradation process is beneficial to understand the reaction kinetics, which guides the design, operation, and control of the degradation reactor. Herein, a sectional strategy based on the method of moments with a newly defined critical chain length was proposed to simulate the degradation process of linear polymers with low computational cost. By accounting for the concentration of species with low molar mass (e.g., dimer), the evolution of average properties and concentration polymer species during linear polymer degradation were simulated with high accuracy through this model. In addition, the evolution of molar mass distribution was approximated by using the as-developed model. Sensitivity analysis was performed for simultaneous random scission and irreversible/reversible chain-end scission processes. Results show that monomer yields, average and distributed molecular properties can be tuned by the rate coefficients of random scission (kr), chain-end scission (ke), and its reverse reaction (k-e). Finally, kinetic insights into the polylactic acid alcoholysis system were gained by the as-developed MoM-based model, which demonstrates the accuracy and applicability of the model. This work provides a new method for simulating linear polymer degradation, and thus revealing the kinetic evolution during degradation.

Machine learning approach to polymer reaction engineering: Determining monomers reactivity ratios

In copolymerization, the monomers' reactivity ratios play an important role in shaping the final copolymer properties. Thus, the knowledge of reactivity ratio is essential to the polymer reaction engineering. However, the experimental methods of determining reactivity ratios are laborious and require many repetitions and testing at different compositions. Therefore, computational methods for determining the reactivity ratios based on the chemical structures of the reactants have attracted significant attention. Here, we demonstrate how machine learning enables the prediction of comonomers reactivity ratios based on the molecular structure of monomers. We combined multi-task learning, multi-inputs, and one of the state-of-the-art machine learning model – Graph Attention Network – to build a model capable of predicting reactivity ratios based on the monomers’ chemical structures. We found that the interpretable characteristics of Multi-Input-Multi-Output Graph Attention Network, along with its capability to learn chemical features facilitates the accurate prediction of the reactivity ratios. Such a predictive tool can be used in combination with macroscopic kinetic models to design new copolymers.

A unified polymer reaction engineering methodology for catalytic olefin polymerization: From reaction conditions and catalyst reaction performance to molecular and rheological properties for forward, reverse engineering and deconvolution applications

AbstractThis work presents a unified polymer reaction engineering methodology for the catalytic olefin polymerization process. The proposed modelling approach offers a modelling pathway from the polymerization recipe to production rate and polymer microstructure, and finally to rheological properties. Furthermore, this work introduces for the first time the constraint of the actual reaction performance of the polymerization catalyst in the inverse rheology and microstructural deconvolution problem, limiting the solution only to the most realistic potential molecular weight distributions (MWDs) that a specific catalyst can produce. This approach can be applied for both single‐ and multi‐site catalysts, providing not a potential MWD but the unique one that the selected catalyst can offer under given polymerization conditions. Depending on the available catalyst reaction performance insight, the constraint can vary and include from the number of active sites in use to the exact kinetic parameters of each site type. The potential of the proposed methodology is highlighted within a series of indicative examples, including forward, reverse engineering and deconvolution applications.

Progress of polymer reaction engineering: From process engineering to product engineering

Polymer reaction engineering studies the design, operation, and optimization of reactors for industrial scale polymerization, based on the theory of polymerization kinetics and transfer processes ( e.g. , flow, heat and mass transfer). Although the foundation and development of this discipline are less than 80 years, the global production of polymers has exceeded 400 million tons per annum. It demonstrates that polymer reaction engineering is of vital importance to the polymer industry. Along with the maturity of production processes and market saturation for bulk polymers, emerging industries such as information technology, modern transportation, biomedicine, and new energy have continued to develop. As a result, the research objective for polymer reaction engineering has gradually shifted from maximizing the efficiency of the polymerization process to the precise regulation of high-end product-oriented macromolecules and their aggregation structures, i . e ., from polymer process engineering to polymer product engineering. In this review, the frontiers of polymer reaction engineering are introduced, including the precise regulation of polymer chain structure, the control of primary aggregation structure, and the rational design of polymer products. We narrow down the topic to the polymerization reaction engineering of vinyl monomers. Moreover, the future prospects are provided for the field of polymer reaction engineering.

Single Particle Growth, Fragmentation and Morphology Modelling: A DEM Approach

AbstractIn this work, the development of a 3D discrete element method (DEM) modeling concept and application that can describe and explain the catalyst particle growth and fragmentation mechanism and follow the morphology developments is presented. The proposed unified modeling methodology is able to predict the expected particle morphology in terms of exact shape, size, and porosity, taking into account the major contributing effects, including reaction kinetics, thermodynamics as well as mechanical considerations. The developed DEM model is validated based on indicative particle morphology patterns as captured experimentally via the SEM technique, managing to qualitatively predict the observed particle morphology. These results indicate the potential of DEM approach in polymer reaction engineering (PRE), expanding the simulation capabilities and range of application.

From identifying polymeric resins to corrosion casting applications

AbstractCorrosion casting is an important manufacturing technique to replicate anatomical structures. Its execution is often based on the injection of resins that can be polymerized starting at room temperature in the presence of an accelerator, which suits most of the practical requirements. In the present contribution, we highlight the main challenges of the corrosion casting technique, addressing a multidisciplinary research angle connecting the fields of life science, veterinary science, materials science, rheology, and polymer reaction engineering. We pay attention to (commercial) material availability, casting medium selection criteria, specimen preparation, maceration, injection possibilities, cleaning procedures, and detail of replication. We start the discussion from general principles and recommendations to then include major examples for each corrosion casting characteristic addressed.

In Silico Screening To Achieve Fast Lab-Scale Nitroxide-Mediated Polymerization of n-Butyl Acrylate with Maximal Control over Macromolecular Properties

Nitroxide-mediated polymerization (NMP) allows one to synthesize well-defined polymers with narrow molar mass distribution (MMD) and latent functionality. For acrylates, however, side reactions, such as backbiting and β-scission and increases in viscosity, can complicate the molecular control, affecting the MMD dispersity, the double bound content, and the degree of livingness. On top of this, acrylate polymerizations are highly exothermic and thus dedicated temperature control is recommended, even under general lab-scale NMP conditions. In the present work, we account for both side reactions, diffusional limitations and nonisothermicity, so that a detailed model-based design for NMP of n-butyl acrylate can be performed in view of a better evaluation of its commercial potential. The relevance of temperature control and semibatch feeding strategies is particularly explored, benefiting from (i) a successful benchmark to NMP lab-scale batch literature data and (ii) a previous successful benchmark under various lab-scale free radical polymerization (FRP) conditions. It is showcased that specific set points of NMP characteristics, e.g., polymerization time, number average molar mass, and unsaturation level, can be achieved for a high monomer conversion by realizing less conventional thus nonlinear number average molar mass profiles, which is highly relevant for the polymer synthesis and reaction engineering community. The current work, which puts forward a dedicated research strategy from FRP to NMP to minimize error on kinetic parameter determination steps, opens the pathway to maximize the potential of reversible deactivation radical polymerization techniques for their more elegant implementation and appreciation in the society.

Macromol. React. Eng. 6/2020

Front Cover: Polymer reaction engineering offers the theoretical background for the modeling pathway from polymerization process conditions to polymer microstructure and end-use properties. This pathway consists of concrete steps: (i) connection of reaction conditions to polymer microstructure, (ii) connection of microstructure to first level properties, and, (iii) estimation of second level properties as a function of microstructure and/or first level properties. This is reported by Vasileios Touloupidis, Gerold Rittenschober, and Christian Paulik in article 2000028.

Sustainable polymer reaction engineering: Are we there yet?

AbstractPolymers are a class of materials that provide unparalleled benefits to humanity, fulfilling countless needs in an exceedingly broad range of applications. Because of their ubiquity and current ways of producing them, polymers also pose significant challenges to the environment. Inspired by a previous publication on sustainable polymer reaction engineering, an update is provided on the latest trends in the use of renewable starting materials for polymers, efforts in process intensification and water‐based polymerization, as well as approaches to dealing with the end of the polymers' application life. We seek to answer the question about how far along we are regarding the achievement of completely sustainable polymer processes.

An Integrated PRE Methodology for Capturing the Reaction Performance of Single‐ and Multi‐site Type Catalysts Using Bench‐Scale Polymerization Experiments

AbstractThe scope of polymer reaction engineering (PRE) is to develop a modeling pathway from polymerization process conditions to polymer microstructure and end‐use properties. The catalyst is the heart of the low‐pressure polymerization and its kinetic parameters constitute the cornerstone of this pathway, without which none of the modeling steps can be established. In this work, an integrated PRE methodology for capturing the reaction performance of single‐ and multi‐site type catalysts is presented. According to the methodology proposed, the catalyst kinetic parameters are estimated based on a series of targeted bench‐scale polymerization experiments and characterization combined with polymer reaction engineering modeling.

STOCHASTIC MODELING OF LIVING/CONTROLLED COPOLYMERIZATIONS IN TUBULAR REACTORS WITH LATERAL FEED

It is known the controlled microstructure polymers production increase in continuous reactors due to all industrial advantages that this type of operation presents. In this paper, the synthesis via Nitroxide-Mediated Polymerization (NMP) of copolymers based on styrene and methyl methacrylate accomplished in a tubular reactor with lateral feed is modeled by Kinect Monte Carlo (kMC). This reactional configuration aims the copolymers production with originals microstructure and applications. There were obtained all the microstructural properties distributions of the synthesized material at interest and the conversion values, Polydispersity Index (PDI) and chains average molar mass are compared to experimental data. The conversion presented minimum and maximum deviation in the module of 0.61 and -21.85%, respectively. For the PDI and the molar mass, there were obtained minimum and maximum errors of 0.72%, 1.50%, -23.10% and 1.77%, respectively. It was verified that the formed product differed from the expected one in experiments. There were found copolymers of polystyrene-b-poly(methyl styrene-rand-methacrylate), not being found any region that presented composition gradient through the chains, differently than it had been foreseen in the experimental synthesis, results that show the relevance of a stochastic simulation in the process to make decisions in the context of polymer reaction engineering.

Water Soluble Polymer Flocculants: Synthesis, Characterization, and Performance Assessment

AbstractWater soluble polymer flocculants are important constituents of solid–liquid separation units for the treatment of a variety of process‐affected effluents. The systematic development of a flocculant relies on a good understanding of flocculation process, polymer synthesis, polymer characterization, and, not the least, flocculation performance assessment as desired for a particular treatment process, all of which are essential to establish meaningful relationships between flocculant microstructure and flocculation efficiency. The aim of this article is to communicate the bigger picture of this research area to the readers. The recent advances in the application of bio/natural, synthetic, and stimuli‐responsive flocculants are reviewed. Then, the basic polymer reaction engineering tools to control the microstructure of flocculants are provided and the techniques for the quantification of flocculant microstructure are concisely discussed. This is followed by a review of the methods used for the characterization of particle‐polymer force measurement, and flocculation/dewatering assessment with attention to the characterization of aggregate structures.

Evaluating and predicting molecular mechanisms of adhesive degradation during field and accelerated aging of photovoltaic modules

AbstractExtending photovoltaic (PV) module lifetimes beyond 30 years is a goal of significant priority. A challenge that must first be addressed, however, is the development of a predictive reliability model that captures the synergy of terrestrial stressors on module degradation, particularly at encapsulant interfaces. Using a metrology designed specifically for PV modules, a comprehensive study of the widely used ethylene vinyl acetate encapsulant was performed in which encapsulant adhesion was evaluated as a function of environmental stressors (UV exposure, temperature, and humidity) for modules aged both under accelerated lab and internationally located field conditions for months to nearly 3 decades. Mechanical and chemical characterization methods are combined with fundamental polymer reaction engineering to unravel the degradation processes active at the molecular scale that lead to encapsulant delamination. An analytical and modular model framework is put forward enabling the prediction of long‐term PV module durability, starting from fundamental principles at the molecular level and explicitly accounting for bond rupture events in the bulk encapsulant and at the encapsulant interfaces. Successful parameter tuning to adhesion data indicates a dominant occurrence of deacetylation, β‐scission, and hydrolytic depolymerization, respectively. The model contributes to the longstanding challenge of predicting module lifetimes in any geographic location while minimizing time‐consuming and costly aging studies.

Polymer reaction engineering tools to design multifunctional polymer flocculants

A series of multifunctional terpolymers, poly(N-isopropyl acrylamide/2-(methacryloyloxy) ethyl trimethyl ammonium chloride/N-tert-butylacrylamide) [P(NIPAM-MATMAC-BAAM)], were designed to flocculate and dewater oil sands mature fine tailings (MFT). The hydrophobic BAAM comonomer helped in expelling water from the sediments, while the cationic MATMAC comonomer promoted the charge neutralization of negatively charged particles suspended in MFT. The chemical composition distributions of these terpolymers were designed based on the knowledge of the reactivity ratios of all comonomers, instead of by trial and error, as usually done for most polymer flocculants. The binary reactivity ratios of the comonomers were estimated by synthesizing the binary copolymers with various mole fractions of each comonomer in the feed and experimentally measuring the corresponding fraction of comonomer in the copolymers. Polymer reaction engineering tools were used to minimize compositional drift and guarantee the synthesis of terpolymers with narrow chemical composition distributions suitable for MFT dewatering. Focused beam reflectance measurement (FBRM) experiments showed that terpolymers promoted the formation of large MFT flocs (120 μm). The initial settling rate decreased with the increase in flocculant hydrophobicity, likely because the hydrophobic terpolymer segments did not take part in the bridging of the MFT particles. In contrast, the sediment dewaterability increased with the increase in terpolymer hydrophobicity. This study provides guidelines to design a polymer flocculant from first principles and demonstrates the potential of using hydrophobically modified cationic polymers to flocculate MFT effectively.