Modeling and Experimental Evaluation of Single Particle Growth in Syndiotactic Polymerization of Styrene

A comprehensive mathematical model and experimental study of single particle growth for styrene polymerization over a silica-supported metallocene catalyst were investigated. The model was developed based on the modification of the well-known multigrain model (MGM) by introducing mesoparticle scale limitations. Thereafter, the model was employed to predict the effects of bulk phase temperature and catalyst properties (initial catalyst active site concentration and initial catalyst particle size) on the polymerization rate, degree of polymerization (DP), and the polydispersity index (PDI) of syndiotactic polystyrene (SPS). The simulation results showed a significant radial distribution of styrene concentration across polymer particle growth at different polymerization conditions. It was found that increasing the initial catalyst concentration and bulk phase temperature resulted in polymerization rate enhancement. In context, the polymerization rate decreased as the initial catalyst particle size increased from 20 to 50 μm. The results revealed that a uniform increase in DP of the polymer was obtained by increasing the initial catalyst concentration and the reaction temperature, while resulting in a decrease of the PDI value. Meanwhile, the DP and PDI values varied inversely under the influence of initial catalyst particle size within a period of time similar to the one needed in the catalyst decay. The simulated results in the study agree well with experimental data of SPS.     

Determining and characterizing the reused text for plagiarism detection

An important task in plagiarism detection is determining and measuring similar text portions between a given pair of documents. One of the main difficulties of this task resides on the fact that reused text is commonly modified with the aim of covering or camouflaging the plagiarism. Another difficulty is that not all similar text fragments are examples of plagiarism, since thematic coincidences also tend to produce portions of similar text. In order to tackle these problems, we propose a novel method for detecting likely portions of reused text. This method is able to detect common actions performed by plagiarists such as word deletion, insertion and transposition, allowing to obtain plausible portions of reused text. We also propose representing the identified reused text by means of a set of features that denote its degree of plagiarism, relevance and fragmentation. This new representation aims to facilitate the recognition of plagiarism by considering diverse characteristics of the reused text during the classification phase. Experimental results employing a supervised classification strategy showed that the proposed method is able to outperform traditionally used approaches.     

PdAgAu alloy with high resistance to corrosion by H2S

PdAgAu alloy films were prepared on porous stainless steel supports by sequential electroless deposition. Two specific compositions, Pd₈₃Ag₂Au₁₅ and Pd₇₄Ag₁₄Au₁₂, were studied for their sulfur tolerance. The alloys and a reference Pd foil were exposed to 1000H₂S/H₂ at 623 K for periods of 3 and 30 h. The microstructure, morphology and bulk composition of both non-exposed and H₂S-exposed samples were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS). XRD and SEM analysis revealed time-dependent growth of a bulk Pd₄S phase on the Pd foil during H₂S exposure. In contrast, the PdAgAu ternary alloys displayed the same FCC structure before and after H₂S exposure. In agreement with the XRD and SEM results, sulfur was not detected in the bulk of either ternary alloy samples by EDS, even after 30 h of H₂S exposure. X-ray photoelectron spectroscopy (XPS) depth profiles were acquired for both PdAgAu alloys after 3 and 30 h of exposure to characterize sulfur contamination near their surfaces. Very low S 2p and S 2s XPS signals were observed at the top-surfaces of the PdAgAu alloys, and those signals disappeared before the etch depth reached ∼10 nm, even for samples exposed to H₂S for 30 h. The depth profile analyses also revealed silver and gold segregation to the surface of the alloys; preferential location of Au on the alloys surface may be related to their resistance to bulk sulfide formation. In preliminary tests, a PdAgAu alloy membrane displayed higher initial H₂ permeability than a similarly prepared pure Pd sample and, consistent with resistance to bulk sulfide formation, lower permeability loss in H₂S than pure Pd.     

Discrete distributions for which the regression of the first record on the second is linear

The linearity of regression of the first record on the second is examined for discrete random variables. Both ordinary and weak records are considered. The analysis involves the determination of all possible linear relationships and all possible probability distributions. Several characterizations of geometric distributions are also shown.     

Asymmetric synthesis of the (2S,4S,6S)-2,4,6-trimethylnonyl subunit of siphonarienes

The paper describes an asymmetric approach to the synthesis of the (2S,4S,6S)-2,4,6-trimethylnonyl segment of siphonarienes. The key step used is a newly developed methodology to obtain sec-dialkyl acetylenes based on the intramolecular hydride transfer from a secondary γ-benzyloxy group with well-defined absolute stereochemistry, ensured by a Sharpless asymmetric epoxidation reaction, to a cation generated by Lewis acid treatment of a tertiary Co₂(CO)₆-complexed propargylic alcohol.     

Shear-thickening in aqueous surfactant-associative thickener mixtures

Associative thickeners represent an important class of rheology modifiers used in waterborne coatings. Understanding molecular level interactions between associative thickeners and surfactants has been the subject of a number of prior studies. Our recent studies focused on the behavior of a hydrophobically modified, aminoplast ether (HEAT) associative thickener and a highly hydrophobic ethoxylated octylphenol surfactant in aqueous solution. Aqueous blends of these two materials exhibit shear-thinning, as well as rarely reported, transient, shear-induced thickening behavior. In addition, the same compositions exhibit both thixotropy and antithixotropy. The shear-induced thickening is shown to be the result of transient aggregated structures formed under shear. Addition of a third component, β-cyclodextrin—a molecule known to disrupt hydrophobic associations—to the mixture helped us advance the understanding of the nature of associative thickener–surfactant interactions that cause the transient shear-thickening behavior. Results indicate that, while overall viscosity of the HEAT/surfactant mixtures is decreased by β-cyclodextrin, the shear-induced thickening is unaffected. An intermolecular interaction model to describe the transient thickening mechanism is presented.     

Superconductor Losses and Damping Effects Under Zero Field Cooling and Field Cooling Conditions in a HTSC-Magnet Levitation System

The levitation and guidance forces of an HTS-Maglev vehicle are studied for different zero field cooling (ZFC) and field cooling (FC) conditions, with both forces showing hysteresis behavior in the FC condition. The levitation force has very different magnitudes depending on the cooling conditions employed in the Maglev vehicle. We also report and analyze a damping phenomenon in the levitation and guidance forces and associate the occurrence of damping with power losses located in the YBCO material when the Maglev vehicle operates in the FC condition. These results are very important for further HTS-Maglev vehicle system designs since power losses can disrupt YBCO superconductivity.     

Optimal feature selection for support vector machines

Selecting relevant features for support vector machine (SVM) classifiers is important for a variety of reasons such as generalization performance, computational efficiency, and feature interpretability. Traditional SVM approaches to feature selection typically extract features and learn SVM parameters independently. Independently performing these two steps might result in a loss of information related to the classification process. This paper proposes a convex energy-based framework to jointly perform feature selection and SVM parameter learning for linear and non-linear kernels. Experiments on various databases show significant reduction of features used while maintaining classification performance.     

Nonoriented Electrical Steels

The physical origins of the magnetic properties of nonoriented electrical steels; its relations to microstructural features like grain size, nonmetallic inclusions, dislocation density distribution, crystallographic texture, and residual stresses; and its processing by cold rolling and annealing are overviewed, using quantitative relations whenever available.