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As the major hydration product of cement, hydrated calcium silicate (C‐S‐H) governs the overall performance of cement‐based materials. The molar ratio of CaO to SiO2 (Ca/Si ratio) significantly affects the structure and properties of C‐S‐H. This study analyzed the effect of Ca/Si ratios (0.83–2.0) on the structural morphology evolution, bond lengths and angles, polymerization process, and nanoporosity of amorphous C‐S‐H, with the help of the ReaxFF force field. The results showed that the reacted C‐S‐H tend to form a fibrous network‐like morphology at low Ca/Si ratios, while the silicate chains are prone to accumulating at high Ca/Si ratios, forming a dense granular ovoid structure. Meanwhile, the Ca/Si ratio has no effect on the bond lengths and angles. In addition, the Ca2+ ions can interrupt the silicate chains during hydration, which leads to a decrease in the average silicate chain length with increasing Ca/Si ratio. The porosity of C‐S‐H decreases from 59.3% to 54.3% when the Ca/Si ratio increases from 0.83 to 2.0. It can be deduced from these findings that the increase in the Ca/Si ratio decreases the compressive strength of cement‐based materials but increases their durability. [ABSTRACT FROM AUTHOR]

This work controlled the polymerization reaction, melting, and mixing processing of a mixture including epoxy resin and polyethylene glycol (PEG) by a pulverizer, to effectively develop high-performance shape memory epoxy/PEG (EPP) filaments. The process parameters of polymerization-melting-mixing-drawing were determined through the extrusion force investigation. The results showed that EPP mixtures could be developed into filaments through polymerization-melting-mixing-drawing process, reducing the spinning temperature and filament diameter from 300°C and 31 mm to 235°C and 13 mm, respectively. The Fourier transform infrared spectra confirmed the complete polymerization of epoxy resin and the stable existence of PEG. EPP filaments developed through the polymerization-melting-mixing-drawing process have exhibited controllable dynamic thermal mechanical properties and Tg, excellent shape recovery effects, and adjustable stimulus temperature. This work developed shape memory EPP filaments through a polymerization-melting-mixing-drawing process, with controllable spinnability and adjustable stimulus temperature, providing a reference for high-performance filament fabrication and process optimization. [ABSTRACT FROM AUTHOR]

Polymerization reactions are characterized by complex, nonlinear behaviors that pose significant challenges for conventional modeling techniques. Accurate and reliable models are crucial for advancing material science and enabling technological innovations across various industries. Conventional first-principles models often fall short in capturing the intricate dynamics of polymeric systems, leading to limitations in predictive accuracy. In this work, we propose a novel hybrid modeling approach that combines a conventional first-principles model with the strengths of a data-driven multi-layer perceptron (MLP) model and also a linear regression (LR) model to enhance the predictability of polymerization processes. Utilizing this hybrid approach significantly reduces the mean absolute error for predicting the concentrations of main reagents by 84% and 86%, respectively, in experiments with significantly deviant outcomes. Our results indicate that the model is capable of predicting the concentrations of both the main and side products with a maximum error margin of 3.5%.

The relative molecular mass and its distribution of polyethylene terephthalate (PET) are key parameters affecting its polymerization and processing technology. Based on the characteristic that hexafluoroisopropanol (HFIP) can dissolve most polyester polymers, the relative molecular mass and its distribution of PET were measured by using HFIP as the mobile phase of gel permeation chromatography. The calibration curves were established by narrow standard samples of poly(methyl methacrylate) (PMMA). The relative molecular mass and its distribution were measured at different dissolution temperatures and time, and the relative standard deviation (RSD) was analyzed. The results show that the optimal dissolution scheme for PET is a dissolution temperature of 20 °C and a dissolution time of 8 h. Drying contrast experiment show that HFIP dissolves PET without drying treatment, and the dissolution conditions are mild, which can avoid PET degradation caused by insufficient drying and high dissolution temperature. Under the conventional PET polymerization conditions, the relative molecular mass of PET increases slowly after high vacuum polymerization for more than two hours. Different polymerization kettles and polymerization processes will lead to differences in the relative molecular mass distribution of PET, and the relative molecular mass distribution of PET obviously affects the temperature setting of melt spinning process. [ABSTRACT FROM AUTHOR]

In this work, we consider a Becker-Döring-like mathematical interaction model between Aβ-monomers and Aβ proto-oligomers playing an important role in Alzheimer's disease. In this context, the clustering process where two or more Aβ-monomers spontaneously aggregate and form a seed of proto-oligomers is highlighted. We prove the quadratic well-posedness [4] of the problem associated with the estimation of clustering rate μ from measured data at different times. [ABSTRACT FROM AUTHOR]

Prior to crosslinking and vulcanization, fluorosilicone rubber is a linear polymer. This linear polymer contains 3,3,3,-trifluoropropyl methyl siloxane links, a few methyl vinyl siloxane links, and is formed by co-polymerization of 1,3,5-trimethyl-1,3,5-tris(3,3,3-trifluoropropyl) cyclotrisiloxane (D3F) with 2,4,6,8-tetramethyl-2,4,6,8-tetravinylcyclotetrasiloxane (V4) under alkaline conditions. To improve the performance of fluorosilicone rubber, three key points should be considered during the synthesis of vinyl-containing high-molecular-weight linear fluorosilicone polymers (fluorosilicone raw rubber): first, avoid the generation of low molecular weight equilibrium by-products; second, eliminate the influence of impurities; and third, increase the copolymerization participation rate of monomer V4. From the three aspects above, this study optimized the reaction conditions for the synthesis of high-molecular-weight linear fluorosilicone polymers containing vinyl. Various factors influencing polymerization were thoroughly investigated. These factors include the initiation system, accelerator, equilibrium reaction, feeding ratio, feeding sequence, neutralization mode, impurity content, etc.

Poly(vinyl acetate) has been used as adhesive or thickener in wood-based panel industry because of its fast curing speed and excellent initial tack. In this work, VAc and PVA were used as monomer and protective colloid, respectively, to prepare low viscosity, high solid content PVAc emulsions by semi-continuous emulsion polymerization. The effect of PVA type and other polymerization process on the emulsion properties were explored. Among these factors, the type of PVA played an important role in the properties of the emulsion. The emulsions synthesized with different species of PVA were characterized by laser particle size analyzer and DSC, etc. The particle size result suggested that the particle size of the emulsion increased significantly with the PVA alcoholysis degree. DSC result showed that the Tg of the emulsion was greatly affected by the PVA polymerization degree. The PVAc emulsion prepared using PVA0599 as protective colloid exhibited a lap shear strength of 8.62 MPa was achieved when it was used as a wood adhesive. [ABSTRACT FROM AUTHOR]

Data-driven soft sensors have increasingly been applied for the quality measurement of industrial polymerization processes in recent years. However, owing to the costly assay process, the limited labeled data available still pose significant obstacles to the construction of accurate models. In this study, a novel soft sensor named the selective Wasserstein generative adversarial network, with gradient penalty-based support vector regression (SWGAN-SVR), is proposed to enhance quality prediction with limited training samples. Specifically, the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is employed to capture the distribution of the available limited labeled data and to generate virtual candidates. Subsequently, an effective data-selection strategy is developed to alleviate the problem of varied-quality samples caused by the unstable training of the WGAN-GP. The selection strategy includes two parts: the centroid metric criterion and the statistical characteristic criterion. An SVR model is constructed based on the qualified augmented training data to evaluate the prediction performance. The superiority of SWGAN-SVR is demonstrated, using a numerical example and an industrial polyethylene process. [ABSTRACT FROM AUTHOR]

Producing isotactic polypropylene (iPP) homo- and copolymers in a wide composition and property range according to customer demand requires perfect alignment between the process technology, catalyst system and polymer structure. The present review shows this for the Borstar® PP process, a hybrid process employing liquid bulk and gas phase stages, in an exemplary way. It starts with the process design and continues through two generations of Ziegler–Natta catalyst development history to the design of advanced multimodal random and multiphase copolymers. Essential elements of each of the three areas contributing to performance range are highlighted, and an outlook to future development is given. [ABSTRACT FROM AUTHOR]

Copyright of Plastics Science & Technology / Suliao Ke-Ji is the property of Plastics Science & Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Synthetic Fiber Industry is the property of Sinopec Baling Petrochemical Company and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Carbon fiber is becoming a key material for engineering applications due to its excellent comprehensive properties. The process parameter optimization is an important step in the polymerization process of carbon fiber production. At present, most of the research on process parameter optimization is usually carried out on a single production line, without considering the correlation between optimization problems. In this paper, a multiobjective mechanism model for the co-optimization of the polymerization process of carbon fiber production is established. Each of these submodels is a multiobjective process parameter optimization task, corresponding to the polymerization process of a production line. In order to solve the model effectively, we also designed an evolutionary multitasking algorithm based on transfer learning, which reuses the past experiences of one task to generate a population pool for the next iteration of another task, enabling explicit genetic transfer between different tasks and accelerating the population convergence speed. The proposed multitasking framework for operation optimization has been conducted on 10 different production conditions of the polymerization process. Experimental results show that compared with other implicit and explicit genetic algorithms, this algorithm is very competitive in generating effective solutions. This research provides important support for process parameter optimization and manufacturing of carbon fiber production, which will help engineers and technicians to make informed decisions. [ABSTRACT FROM AUTHOR]

Copyright of Advanced Textile Technology is the property of Zhejiang Sci-Tech University Magazines and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Acta Materiae Compositae Sinica is the property of Acta Materiea Compositae Sinica Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we are interested in the modeling of Alzheimer's disease from the angle of the amyloid cascade hypothesis, where the pathogenic agent is the A β protein in its oligomeric form. The formation dynamics of this pathogenic form is modeled by a Becker–Doring type model where APP (Amyloid Precursor Protein) protein, after cleavage by specific enzymes, forms monomeric A β proteins, which, by polymerization and/or nucleation, lead to the formation of oligomeric A β. We propose an optimal control problem to estimate the rate of nucleation which seems to be at the base of this amyloid cascade hypothesis and for which no (experimental) measurement exists for the moment. [ABSTRACT FROM AUTHOR]

Carbon fiber is becoming a key material for engineering applications due to its excellent comprehensive properties. The process parameter optimization is an important step in the polymerization process of carbon fiber production. At present, most of the research on process parameter optimization is usually carried out on a single production line, without considering the correlation between optimization problems. In this paper, a multiobjective mechanism model for the co-optimization of the polymerization process of carbon fiber production is established. Each of these submodels is a multiobjective process parameter optimization task, corresponding to the polymerization process of a production line. In order to solve the model effectively, we also designed an evolutionary multitasking algorithm based on transfer learning, which reuses the past experiences of one task to generate a population pool for the next iteration of another task, enabling explicit genetic transfer between different tasks and accelerating the population convergence speed. The proposed multitasking framework for operation optimization has been conducted on 10 different production conditions of the polymerization process. Experimental results show that compared with other implicit and explicit genetic algorithms, this algorithm is very competitive in generating effective solutions. This research provides important support for process parameter optimization and manufacturing of carbon fiber production, which will help engineers and technicians to make informed decisions.

For predicting the conversion rate of vinyl chloride monomer (VCM) in the polyvinyl chloride (PVC)production process, a neural network soft-sensor model based on data dimensionality reduction strategies was proposed. In order to solve the problem of complex neural network topology and long training time caused by the excessive input vector dimension, seven kinds of data dimensionality reduction methods, such as principal component analysis (PCA), locality preserving projection (LPP), kernel principal component analysis (KPCA), expectation max principal component analysis (EMPCA), local tangent space alignment (LTSA), T-distributed stochastic neighbor embedding (TSNE) and neighboring preserving embedding (NPE), are used to reduce the dimension of the high-dimensional input data used in the neural network soft-sensor model. Then the radial basis function (RBF) neural network based on the gradient learning, orthogonal least squares and clustering learning methods and the dynamic fuzzy neural network (D-FNN) were utilized to realize the prediction on the VCM conversion rate. Simulation results show that the proposed neural network soft-sensor models based on seven data dimensionality reduction strategies can effectively predict the key economic and technical indicators of PVC polymerization process and meet the real-time control requirements of PVC production process. [ABSTRACT FROM AUTHOR]

The participation of the nitroethene and its α-substituted analogs as model nitrofunctionalized monomers, and (Z)-C,N-diphenylnitrone and methyl vinyl ether as initiators in the polymerization reactions, has been analyzed in the framework of the density functional theory calculations at the M06-2X(PCM)/6-311 + G(d) level. Our computational study suggests the zwitterionic mechanism of the polymerization process. The exploration of the nature of critical structures shows that the first reaction stage exhibits evidently polar nature, whereas additions of further nitroalkene molecules to the polynitroalkyl molecular system formed should be considered as moderate polar processes. The more detailed view on the molecular transformations gives analysis based on the bonding evolution theory. This study shows that the case of polymerization reaction between nitroethene and (Z)-C,N-diphenylnitrone allows for distinguishing eleven topologically different phases, while, in the case of polymerization reaction nitroethene and methyl vinyl ether, we can distinguish nine different phases. [ABSTRACT FROM AUTHOR]

• Non-diagonal Gaussian mixture distribution is employed to variational autoencoder for complex multimode feature representation. • A quality-driven stragy is added to extract the quality-related features. • A gating mechanism is utilized to enhance the ability of nonlinear representation. • The trained decoder coupled with the sampling space can directly achieve online prediction. • The proposed method is tested in two actual polymerization processes. Variational autoencoder has been widely used to build soft sensor models as a deep unsupervised feature extractor in recent years. However, the vanilla VAE employs a single diagonal Gaussian distribution as the approximate posterior in its latent feature space, which may lead to inferior performance when solving complex non-Gaussian multimode modeling problem. To address the issue, this paper proposes a quality-driven Gaussian mixture variational probabilistic network-based (QGMVPN) soft sensor model. First, Gaussian mixture density with diagonal covariance matrices and Householder transformations are implemented to characterize the distributions of non-Gaussian multimode feature. Then, a quality-driven strategy is designed to extract quality-related features and a novel gating mechanism is employed to enhance the nonlinear representation ability. Last, the trained decoder, coupled with the sampling space, is directly applied to soft sensor modeling owing to the special extra input of the decoder. The effectiveness of QGMVPN-based model is demonstrated on two polymerization datasets. [ABSTRACT FROM AUTHOR]

Recent theoretical modeling offers a unified picture for the description of stochastic processes characterized by a crossover from anomalous to normal behavior. This is particularly welcome, as a growing number of experiments suggest the crossover to be a common feature shared by many systems: in some cases the anomalous part of the dynamics amounts to a Brownian yet non-Gaussian diffusion; more generally, both the diffusion exponent and the distribution may deviate from normal behavior in the initial part of the process. Since proposed theories work at a mesoscopic scale invoking the subordination of diffusivities, it is of primary importance to bridge these representations with a more fundamental, “microscopic” description. We argue that the dynamical behavior of macromolecules during simple polymerization processes provide suitable setups in which analytic, numerical, and particle-tracking experiments can be contrasted at such a scope. Specifically, we demonstrate that Brownian yet non-Gaussian diffusion of the center of mass of a polymer is a direct consequence of the polymerization process. Through the kurtosis, we characterize the early-stage non-Gaussian behavior within a phase diagram, and we also put forward an estimation for the crossover time to ordinary Brownian motion.

The global manufacturing sector faces a significant challenge about minimizing environmental impacts without witnessing the adverse effects on the market competitiveness. A cleaner production methodology must involve different approaches for minimizing or recycling waste streams, obtaining at the same time economical and environmental advantages for manufacturing companies (Severo et al., J Clean Prod 142:87–97, 2017). In the polypropylene production process, more than 60 grades of polypropylene are produced. To achieve this target, changes in the process variables are carried out, due to the continuous operation process. Those changes generate a mixture of resins with intermediate properties (transition), which must be minimized. Moreover, failures during the transitions process generate a large extra amount of material that represents a significant impact on the environment, when it is considered as waste. In this paper, an environmental analysis was made by applying a waste reduction algorithm (WAR) to establish the potential impact of a polypropylene production process over the environment, applying grade transitions strategies and then to demonstrate how the improvement of transition times implies a significant reduction of plastic waste. It was found that the reduction of transitions time minimizes the environmental impact based on real industry data. Last should be considered for the real polymer plant analyzed and future projects. [ABSTRACT FROM AUTHOR]

In this paper we present an analytical and numerical modeling of a general polymerization process with possible lengthening by an agglomeration mechanism. The proposed model takes into account the 2D spatial diffusion of the monomers for the mass transfer between monomers and polymers. We investigate the well-posedness of this general polymerization model and propose an adequate numerical scheme based on a generalization of the anti-dissipative method developed in Goudon (Math. Models Methods Appl. Sci. 23:1177–1215, 2013). [ABSTRACT FROM AUTHOR]

Over the past researches, the formulation of nanoparticles is considered to be a most time-consuming process. Accordingly, it is a relevant process to attempt the synthesis of the silica nanoparticle with a reasonable series of time. Therefore, shock wave as a source of energy has contributed an effective role in the field of material science. The research is motivated to reduce the processing time, and the alternative source of energy in the form of shock waves is intruded over the precursor and initiates the reaction in the precursor and also it is used to sustain and control the reactions of nanoparticles. In this research, an attempt has been made to use shock wave as a source of energy to synthesis silica nanoparticles. Henceforth, the processing time taken for the synthesis of silica is comparatively very high in the existing manufacturing schemes. Initially, the synthesis is initiated from the phase of material selection that is capable of the composition. Henceforth, tetraethyl orthosilicate, ethanol, water, ammonium hydroxide and shock tubes are the material participates in the composition. Accordingly, a disparate solution has been formed after the fusion of the relative materials. The prepared solution is treated with the consecutive shock pulses for the formation of the ammonium acetate and further treated in the centrifuge tube. Concurrently, on the basis of Fourier transform infrared spectroscopy (FTIR) the formation of silica nanoparticles is confirmed. Using field emission scanning electron microscope, the growth in the polymerization of silica nanoparticles is analyzed with a relative progress by inserting the shocks. Extensively, by acquiring the specified shock wave energy as an alternative solution the processing time attained for the synthesis will be more effectively performed when compared to the existing techniques as such that the existing practices will require 3 h to fulfill the synthesis as the proposed energy will attain only 15 min to formulate the nanoparticle. Ultimately, the FTIR analysis upon the research exhibits from the allotted region of the asymmetric vibration over basic intense band of silica is rectified as Si–O–Si over 1100–1120 cm−1 and also deliberates a bending vibration with vibration of the corresponding Si–O–Si at 1670–1677 cm−1, respectively. Conclusively, the research rectifies an immense of evolution in the processing time to synthesis silica nanoparticle from the existing practices followed in real-time scenario, respectively. [ABSTRACT FROM AUTHOR]

A millistructured reactor and spectroscopic setup for contactless kinetic measurements in oscillating droplets is presented in this work. The polymerization of acrylic acid serves as a model reaction. Design and construction of the reactor focus on the optical access for Raman measurement, the nearly isothermal behavior, and the preservation of droplets during long‐term reactions with a high increase of viscosity. Another key aspect is the possibility of full automation at a later stage. [ABSTRACT FROM AUTHOR]

Prior to crosslinking and vulcanization, fluorosilicone rubber is a linear polymer. This linear polymer contains 3,3,3,-trifluoropropyl methyl siloxane links, a few methyl vinyl siloxane links, and is formed by co-polymerization of 1,3,5-trimethyl-1,3,5-tris(3,3,3-trifluoropropyl) cyclotrisiloxane (D 3 F) with 2,4,6,8-tetramethyl-2,4,6,8-tetravinylcyclotetrasiloxane (V 4 ) under alkaline conditions. To improve the performance of fluorosilicone rubber, three key points should be considered during the synthesis of vinyl-containing high-molecular-weight linear fluorosilicone polymers (fluorosilicone raw rubber): first, avoid the generation of low molecular weight equilibrium by-products; second, eliminate the influence of impurities; and third, increase the copolymerization participation rate of monomer V 4 . From the three aspects above, this study optimized the reaction conditions for the synthesis of high-molecular-weight linear fluorosilicone polymers containing vinyl. Various factors influencing polymerization were thoroughly investigated. These factors include the initiation system, accelerator, equilibrium reaction, feeding ratio, feeding sequence, neutralization mode, impurity content, etc., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors. Published by Elsevier Ltd.)

To reduce fresh water consumption in a polyvinyl chloride (PVC) plant, the effluent from a biological treatment must be demineralized to be re-used in the resin polymerization process. Demineralization is a critical process, since the quality and the stability of the PVC resins are highly influenced by the water quality used in the process. The main target values for water parameters are the following: conductivity <10 μScm, TOC < 10 mg L, and Al < 0.1 mg L. To achieve this quality, several reverse osmosis membranes from different materials and suppliers were tested and compared in the demineralization treatment. Polyamide membranes showed higher salt rejection compared to cellulose acetate membranes yielding both types similar flux and permeability. Two-pass reverse osmosis treatment was necessary to reach conductivities lower than 10 μS cm. On the other hand, a good quality effluent for reuse was obtained by combining RO and ionic exchange resins. Results showed that good quality PVC resins in terms of color, granulometry, porosity, and bulk density were obtained when demineralized water from two-pass reverse osmosis was used as fresh water, proving the feasibility of the effluent reuse in the PVC industry. [ABSTRACT FROM AUTHOR]

Preparation of polystyrene nanocomposites containing flame retardants is difficult to achieve in one step by suspension polymerization. Styrene suspension polymerization was studied to determine the effects of the flame retardant on the polymerization process and properties of polystyrene beads. Triphenyl phosphate (TPP) was used in this work, which can dissolve completely in styrene monomers. The results showed that TPP were nanosized spherical particles, distributed homogenously and uniformly in a polystyrene (PS) matrix, and the formation mechanism of TPP nanoparticles was also investigated. In addition, the effects of TPP on the styrene polymerization process were investigated. With TPP amount increasing, the polymerization time increased significantly; molecular weight of PS nanocomposites also decreased and molecular weight distribution became wide; the particle size distribution (PSD) of the PS nanocomposites became wider than pure PS slightly as the particle size decreased. PS/TPP nanocomposites obtained good flame retardance because of nanodispersed TPP particles in its matrix. In a word, the suspension polymerization method provides a facile approach to prepare PS/TPP nanocomposites with better properties. [ABSTRACT FROM AUTHOR]

Copyright of Jianzhu Cailiao Xuebao: Journal of Building Materials is the property of Journal of Building Materials and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Metallurgical Processes & Equipment is the property of Technopark Ltd. DonSTU, Unitech and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• The relationship between TPUA reaction degree and time and the temperature was determined. • The chemical modification mechanism of asphalt by thermosetting PU was analyzed quantitatively and qualitatively. • Adding 30 wt% content of modifier significantly changed the microstructure of the TPUA system. • The addition of PU modifier increased the surface free energy of asphalt. Thermosetting polyurethane (PU) can significantly improve the high-temperature stability, mechanical strength, and low-temperature flexibility of asphalt. To broaden its application, a systematic understanding of how the PU interacts with the asphalt is needed. Currently, limited effort has been focused on the polymerization and modification mechanism of thermosetting polyurethane modified asphalt (TPUA). The interaction mechanism of thermosetting PU with asphalt was revealed from three aspects: molecular structure, microstructure, and surface free energy (SFE). First, the polymerization process of thermosetting PU in asphalt was analyzed based on curing reaction kinetics, that is, reaction order and activation energy. Then, the physical and chemical interactions between modifier and asphalt were investigated quantitatively and qualitatively by infrared spectroscopy and nuclear magnetic resonance spectrometer (1H NMR). Besides, the two-phase structure of PU and asphalt was observed by fluorescence microscope (FM) and scanning electron microscope (SEM). Finally, the effect of thermosetting PU on the SFE of asphalt was revealed by the sessile drop method. The results showed that asphalt did not affect the secondary reaction characteristics of the PU modifier. The relationship between the curing time and the reaction degree and temperature was identified, which well predicted the curing time required to finish the reaction under different temperatures. Moreover, the isocyanate component in PU reacted with the active hydrogen in asphalt to form carbamate (–NHCOO–), and the exact degree of chemical modification was asphalt-source dependent. With the increase of modifier content, the continuous phase in TPUA gradually changed from asphalt to PU, and the phase transition point was near 30 wt%. When the critical content was exceeded, asphalt was dispersed in the PU skeleton as microspheres. In addition, adding PU increased asphalt polarity, and the SFE of TPUA increased gradually as the modifier content increased. [ABSTRACT FROM AUTHOR]

The main objective of producing ceramic-metal composites is to improve the fracture toughness of the brittle ceramic matrix. Classical methods of powder metallurgy (such as isostatic or uniaxial pressing) are not capable of fabricating complex-shape elements. For this reason, colloidal processes have been applied recently in manufacturing of ceramic matrix composites. In the present work, AlO-3 vol.% Ni composites were prepared by aqueous gelcasting process and sintering in reducing atmosphere. The commercially available monomers 2-hydroxyethyl acrylate and N,N′-methylenebisacrylamide as cross-linking agents were used. The activator was N,N,N′,N′,-tetramethylenediamine (TEMED) and ammonium persulfate was added as an initiator of the polymerization process. Composite slurries were obtained from alumina powders (grain size 0.21 μm) and 3% (in respect to the total solid volume) of two nickel powders of different grain size (~10 and ~0.4 μm). The paper describes the rheological properties of the obtained composite slurries. The properties of green and sintered elements have also been presented. It was found that the presence of nickel particles had a significant influence on the slurry polymerization process. The green and sintered relative density was in every case higher than 57 and 97%, respectively. [ABSTRACT FROM AUTHOR]

Highlights: [•] Fast solution strategies of MIDO problems for integrated scheduling and control. [•] Two formulations of the integrated MIDO problem with different model structures. [•] Generalized Benders decomposition methods taking advantage of problem structures. [•] Global optimization method for mixed-integer fractional programming master problems. [Copyright &y& Elsevier]

Abstract: Nitroxide-mediated polymerization (NMP) is a controlled/living radical polymerization (CLRP) technique that enables the design of well-defined, functional and complex macromolecular architectures. This comprehensive review covers all aspects, features and achievements of NMP, from its discovery to 2012. All topics related to NMP are thoroughly discussed and detailed in-depth: synthetic approaches to nitroxides and alkoxyamines, kinetic aspects and polymerization features, range of controllable monomers, polymer characterization, polymerization processes (ionic liquids, dispersed media, etc.), macromolecular coupling approaches, functionalization strategies, macromolecular architectures, bio-related and hybrid materials, industrial applications as well as environmental constraints. [Copyright &y& Elsevier]

Abstract: In this paper we investigated by different complementary methods the processes occurring in the polymer layers bulk and at the polymer/substrate interface during the polymerization of liquid films of polydimethylsiloxane lying on the surface of a solid aluminum respectively titanium substrate under atmospheric pressure air corona discharges. The infrared spectroscopy, Glow Discharge Optical Emission Spectrometry and Plasma Profiling Time of Flight Mass Spectrometry investigations show the presence of Al2O3 and aluminosilicates type compounds at the polymer-substrate/interface and their linkage to the polymers chains by Sih name="sbnd" />Al bonds. By coupling the spectral results with those obtained by Scanning Electron Microscopy investigations we evidenced the formation of silicon oxide structures preponderantly on the free surface of the polydimethylsiloxane layers but also in the polymer bulk deposited on aluminum respectively titanium substrates. [Copyright &y& Elsevier]

The process of bending empty carbon nanotubes (10,10) and the tube filled by fullerenes C60 was investigated. The polymerization of fullerenes and the chemical interaction between the fullerenes and the tube wall is observed in the tube filled with fullerenes in the bending of 270 degrees. It was found that the minimum angle of the bending, which is required for the polymerization, is equal to 16 degrees. The inside of the nanotube is transformed into a wave-like. We used two methods: the empirical and the quantum–mechanical methods. The simulation until the polymerization of fullerenes is carried out by the empirical method and the polymerization process are modeled by the tight binding method. [ABSTRACT FROM AUTHOR]

This work presents an algorithm for the development of adaptive soft sensors. The method is based on the local learning framework, where locally valid models are built and maintained. In this framework, it is possible to model nonlinear relationship between the input and output data by the means of a combination of linear models. The method provides the possibility to perform adaptation at two levels: (i) recursive adaptation of the local models and (ii) the adaptation of the combination weights. The dataset used for evaluation of the algorithm describes a polymerization reactor where the target value is a simulated catalyst activity in the reactor. This dataset is also used to evaluate the performance of the proposed algorithm. The results show that the traditional recursive partial least squares algorithm struggles to deliver accurate predictions. In contrast to this, by exploiting the two-level adaptation scheme, the proposed algorithm delivers more accurate results. © 2010 American Institute of Chemical Engineers AIChE J, 57, 2011 [ABSTRACT FROM AUTHOR]

Abstract: We consider a polymerization (fragmentation) model with size-dependent parameters involved in prion proliferation. Using power laws for the different rates of this model, we recover the shape of the polymerization rate using experimental data. The technique used is inspired from , where the fragmentation dependence on prion strains is investigated. Our improvement is to use power laws for the rates, whereas used a constant polymerization coefficient and linear fragmentation. [Copyright &y& Elsevier]

We consider a linear integro-differential equation which arises to describe both aggregation-fragmentation processes and cell division. We prove the existence of a solution (λ, $\mathcal U$, ϕ) to the related eigenproblem. Such eigenelements are useful to study the long-time asymptotic behavior of solutions as well as the steady states when the equation is coupled with an ODE. Our study concerns a non-constant transport term that can vanish at x = 0, since it seems to be relevant to describe some biological processes like proteins aggregation. Non-lower-bounded transport terms bring difficulties to find a priori estimates. All the work of this paper is to solve this problem using weighted-norms. [ABSTRACT FROM AUTHOR]