Copolymerization of styrene with an α‐olefin via atom transfer radical polymerization

This investigation reports the preparation of styrene–α‐olefinic random copolymers, using 1‐octene as an α‐olefin, via atom transfer radical polymerization. Atom transfer radical copolymerization of styrene with 1‐octene was successfully carried out using phenylethyl bromide as initiator and CuBr as catalyst in combination with N, N, N′, N″, N″‐pentamethyldiethylenetriamine as ligand. The copolymers had controlled molecular weight, narrow dispersity and well‐defined end groups with significant 1‐octene incorporation in the polymer. Incorporation of 1‐octene in the copolymers was confirmed using ¹H NMR and matrix‐assisted laser desorption ionization time‐of‐flight mass spectroscopy. An increase in 1‐octene content in the monomer feed led to an increase in the level of incorporation of the α‐olefin in the copolymer. An increase in the concentration of 1‐octene led to a decrease in the rate of polymerization and an increase in dispersity. The glass transition temperature of the copolymer gradually decreased as the incorporation of 1‐octene increased. Copyright © 2011 Society of Chemical Industry     

Effects of Post-Weld Heat Treatment on the Microstructure and Toughness of Flash Butt Welded High-Strength Low-Alloy Steel

Metallurgical and Materials Transactions A - Effect of post-weld heat treatment on the weld microstructure, texture, and its correlation to the toughness of flash butt welded joints were...     

Correction to: Comparative analysis of limit equilibrium and numerical methods for prediction of a landslide

Bulletin of Engineering Geology and the Environment - Landslides that occur due to the rapid motion of a rock-mass are a primary risk in mountainous terrains and are a danger to human life and...     

Directly smelted lead-tin alloys: A historical perspective

This paper discusses evidence related to the genesis and occurrence of mixed lead-tin ore deposit consisting of cassiterite and the secondary minerals formed from galena. These evidences belong to a very long time period ranging from pre-historic to as late as the nineteenth century a.d. This type of mixed ore deposits was smelted to prepare lead-tin alloys. The composition of the alloy depended on the composition of the starting ore mixture. A nineteenth century evidence for the production of directly smelted lead-tin alloys in southern Thailand is discussed. A unique and rather uncommon metallurgical terminology in Sanskrit language—Nāgaja—was introduced in India for the tin recovered from impure lead. This suggests that Indians developed a process for recovering tin from lead-tin alloys, which in all probability was based on the general principle of fire refining. It has been shown that in the context of India the possibility of connection between the word Nāgaja and the directly smelted lead-tin alloys cannot be ruled out.     

Genetic programming for simultaneous feature selection and classifier design.

This paper presents an online feature selection algorithm using genetic programming (GP). The proposed GP methodology simultaneously selects a good subset of features and constructs a classifier using the selected features. For a c-class problem, it provides a classifier having c trees. In this context, we introduce two new crossover operations to suit the feature selection process. As a byproduct, our algorithm produces a feature ranking scheme. We tested our method on several data sets having dimensions varying from 4 to 7129. We compared the performance of our method with results available in the literature and found that the proposed method produces consistently good results. To demonstrate the robustness of the scheme, we studied its effectiveness on data sets with known (synthetically added) redundant/bad features.     

Phase transformations in thermally exposed Au-Al ball bonds

Gold-aluminum ball bonds were thermally exposed at constant elevated temperatures, and the resultant phase transformations studied in detail. The as-bonded microstructure of a Au-Al ball bond essentially consisted of a reaction zone (termed “alloyed zone” (AZ) in the as-bonded condition) between the Au bump and the bonded Al metallization. It is the growth of the reaction zone between the Au bump and the bonded Al metallization and also the nonbonded Al metallization during thermal exposure that gave rise to the various phase transformations. Au₄Al, Au₈Al₃, and Au₂Al are the predominant phases that grew across the ball bond until the bonded Al metallization is available to take part in the interdiffusion reactions. After the complete consumption of the bonded Al metallization, the Au-Al phases reverse transformed resulting in the formation of the Au₄Al phase in the entire reaction zone across the ball bond (RZ-A). The lateral interdiffusion reactions resulted in the nucleation and the growth of all of the Au-Al phases given by the phase diagram. Kidson’s analysis and Tu et al.’s treatment were extended to a five-phase binary system to explain the phase transformations in thermally exposed Au-Al ball bonds. It is possible for all of the Au-Al phases to grow across a ball bond uninhibited as long as the bonded metallization is available. However, the supply limitation of the bonded metallization gives rise to reverse transformations where Al-rich phases transform to Au-rich phases and eventually result in the formation of the Au₄Al phase in the entire RZ-A. If infinite time is allowed, Au₄Al would dissolve; the extent of which is dependent on the solubility of Al in Au. No supply of Au lateral to the bond causes the reverse transformation of the Au₄Al phase, giving rise to the lateral growth of the remaining Au-Al phases. If infinite time is allowed, the lateral phase transformations would result in the formation of a phase that is dependant on the relative proportion of Au and Al present in the nonbonded metallization (NBM) and Au₄Al below the void line. Hence, the presence of a phase in a particular location of a ball bond is dependent on the time and temperature of thermal exposure.     

Probing the dynamics of nanoparticle growth in a flame using synchrotron radiation

Flame synthesis is one of the most versatile and promising technologies for large-scale production of nanoscale materials. Pyrolysis has recently been shown to be a useful route for the production of single-walled nanotubes, quantum dots and a wide variety of nanostructured ceramic oxides for catalysis and electrochemical applications. An understanding of the mechanisms of nanostructural growth in flames has been hampered by a lack of direct observations of particle growth, owing to high temperatures (2,000 K), rapid kinetics (submillisecond scale), dilute growth conditions (10(-6) volume fraction) and optical emission of synthetic flames. Here we report the first successful in situ study of nanoparticle growth in a flame using synchrotron X-ray scattering. The results indicate that simple growth models, first derived for colloidal synthesis, can be used to facilitate our understanding of flame synthesis. Further, the results indicate the feasibility of studies of nanometre-scale aerosols of toxicological and environmental concern.     

Hierarchical learning architecture with automatic feature selection for multiclass protein fold classification

The structure classification of proteins plays a very important role in bioinformatics, since the relationships and characteristics among those known proteins can be exploited to predict the structure of new proteins. The success of a classification system depends heavily on two things: the tools being used and the features considered. For the bioinformatics applications, the role of appropriate features has not been paid adequate importance. In this investigation we use three novel ideas for multiclass protein fold classification. First, we use the gating neural network, where each input node is associated with a gate. This network can select important features in an online manner when the learning goes on. At the beginning of the training, all gates are almost closed, i.e., no feature is allowed to enter the network. Through the training, gates corresponding to good features are completely opened while gates corresponding to bad features are closed more tightly, and some gates may be partially open. The second novel idea is to use a hierarchical learning architecture (HLA). The classifier in the first level of HLA classifies the protein features into four major classes: all alpha, all beta, alpha + beta, and alpha/beta. And in the next level we have another set of classifiers, which further classifies the protein features into 27 folds. The third novel idea is to induce the indirect coding features from the amino-acid composition sequence of proteins based on the N-gram concept. This provides us with more representative and discriminative new local features of protein sequences for multiclass protein fold classification. The proposed HLA with new indirect coding features increases the protein fold classification accuracy by about 12%. Moreover, the gating neural network is found to reduce the number of features drastically. Using only half of the original features selected by the gating neural network can reach comparable test accuracy as that using all the original features. The gating mechanism also helps us to get a better insight into the folding process of proteins. For example, tracking the evolution of different gates we can find which characteristics (features) of the data are more important for the folding process. And, of course, it also reduces the computation time.     

Microwave Studies on Strontium Ferrite Based Absorbers

Single layer microwave absorbers based on strontium ferrite-epoxy composites have been fabricated and their reflection loss characteristics studied in the X-band (8–12.4 GHz) of microwave frequencies. Permittivity (_r – j_r) and permeability (_r – j_r) of Co and Ti added strontium ferrite SrCo _x Ti _x Fe_{12 – 2x} O₁₉ (x = 0.1 to 0.9 in steps of 0.2), have been measured. Thickness of the absorber is an important criterion influencing the absorption characteristics. Composites of 3 mm thickness are found to absorb over a reasonable range of X-band frequencies. A minimum reflection loss of –36.5 dB is observed for the composite with x = 0.3.