Molecular landscape of olefin block copolymers (OBCs) was patterned by hybridizing capabilities of Kinetic Monte Carlo (KMC) and Artificial Neural Network (ANN) stochastic modeling approaches to explore complexities with chain shuttling copolymerization of ethylene with αolefins. Theoretical data on chain microstructure were obtained by an in-house KMC simulator. The interdependence between microstructure and operating conditions was uncovered by ANN modeling. The average number of linkage points per OBC chain is monitored as a direct criterion reflecting the multi-block nature of OBCs. We also quantified hard and soft block length and ethylene sequence length of both blocks in terms of catalyst composition, ethylene to 1-octene ratio, and chain shuttling agent level, giving useful insights to be applied to developing tailored OBCs. The proposed hybrid stochastic modeling approach successfully predicts the conditions for producing OBCs with predesigned structure; i.e., block length, block number, and ethylene sequence length in hard and soft segments of OBC. As a unique feature of this work, we suggest operation condition for developing and identifying new families of OBCs with microstructures that were previously unexplored.
Copolymer properties and processability depend on copolymer microstructure, i.e., copolymer composition and monomer unit arrangements along the copolymer chains. To predict the ultimate properties of copolymers, one needs complete information on the length and position of sequences of each monomer type in every chain. A versatile kinetic Monte Carlo code is developed and applied for the simulation of typical free radical copolymerizations. The code allows explicit monitoring of every growing chain during the course and at the end of polymerization, can account for comonomer systems of any arbitrary reactivity ratios (r1 and r2) over the full range of monomer composition. Meanwhile, it eliminates the need for solving arrays of differential equations arising from deterministic modeling approaches. Since the code virtually synthesizes billions of copolymer molecules and keeps in storage information on each and every copolymer chain in the system, it allows for detailed statistical analysis. The simulator visualizes the bivariate sequence length–chain length distribution for typical copolymerization systems and examples with: r1 < 1 and r2 < 1; r1 > 1 and r2 < 1; (r1 × r2) = 1; and r1 = r2 = 1, and is also applied successfully to an experimental scenario described in the literature.
Experimental and mathematical modeling analyses were used for controlling melt free-radical grafting of vinylic monomers on polyolefins and, thereby, reducing the disturbance of undesired cross-linking of polyolefins. Response surface, desirability function, and artificial intelligence methodologies were blended to modeling/optimization of grafting reaction in terms of vinylic monomer content, peroxide initiator concentration, and melt-processing time. An in-house code was developed based on artificial neural network that learns and mimics processing torque and grafting of glycidyl methacrylate (GMA) typical vinylic monomer on high-density polyethylene (HDPE). Application of response surface and desirability function enabled concurrent optimization of processing torque and GMA grafting on HDPE, through which we quantified for the first time competition between parallel reactions taking place during melt processing: (i) desirable grafting of GMA on HDPE; (ii) undesirable cross-linking of HDPE. The proposed robust mathematical modeling approach can precisely learn the behavior of grafting reaction of vinylic monomers on polyolefins and be placed into practice in finding exact operating condition needed for efficient grafting of reactive monomers on polyolefins.
Layered silicate-incorporated polyamide 6 (PA6) nanocomposites were studied to undertake correlations between morphological, rheological, permeability, and mechanical characteristics. The microstructure of nanocomposite specimens was seen through X-ray diffraction, atomic force microscopy, and transmission electron microscopy measurements. In addition, impact, tensile, and permeability data were analyzed combining the theoretical and experimental analyses to draw a convincing conclusion on the state of exfoliation and intercalation of layered silicates throughout the PA6 matrix. The results provided support for the fact that introduction of silicate nanofiller brings about some advantages like higher impact and modulus, at the same time some drawbacks arising from the improper dispersion of nanoplatelets. It was also revealed that microscopic studies do not necessarily agree with each other regarding filler dispersion state. The correlations between microstructure and experimental data from mechanical and permeability tests were checked by some well-established theoretical models. The trends in the test results were somewhat dependent on the state of filler dispersion. The role of the crystalline areas in achieving higher permeability resistance was also discussed, in a short review, to be comprehensively discussed in a future work. J. VINYL ADDIT. TECHNOL., 23:35-44, 2017.
Crystallization kinetics of polymer/clay systems was the subject of numerous investigations, but still there are some ambiguities in understanding thermal behavior of such systems under isothermal and nonisothermal circumstances. In this work, isothermal rheokinetic and nonisothermal calorimetric analyses are combined to demonstrate crystallization kinetics of polyamide6/nanoclay (PA6/NC) nanocomposites. As the main outcome of this work, we detected different regimes of crystallization and compared them by both isothermal dynamic rheometry and nonisothermal differential scanning calorimetry (DSC), which has not been simultaneously addressed yet. A novel analysis, somehow different from the common ones, is used to convert the storage modulus data to crystallinity values leading to more reasonable Avrami parameters in isothermal crystallization. It was found based on isothermal rheokinetic studies that increase of NC content and shear rate are responsible for erratic behavior of Avrami exponent and crystallization rates. Optimistically, however, isothermal crystallization by rheometer was confirmed by DSC. Nonisothermal calorimetric evaluations suggested an accelerated crystallization of PA6 upon increasing NC content and cooling rate. The crystallization behavior was quantified applying Ozawa (r2 between 0.070 and 0.975), and combinatorial Avrami–Ozawa (r2 between 0.984 and 0.998) models, where the latter appeared more appropriate for demonstration of nonisothermal crystallization of PA6/NC nanocomposites. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018, 135, 46364.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.