Magnetic Field‐Induced Ferroelectric Switching in Multiferroic Aurivillius Phase Thin Films at Room Temperature

Single‐phase multiferroic materials are of considerable interest for future memory and sensing applications. Thin films of Aurivillius phase Bi₇Ti₃Fe₃O₂₁ and Bi₆Ti_2.8Fe_1.52Mn_0.68O₁₈ (possessing six and five perovskite units per half‐cell, respectively) have been prepared by chemical solution deposition on c‐plane sapphire. Superconducting quantum interference device magnetometry reveal Bi₇Ti₃Fe₃O₂₁ to be antiferromagnetic (T_N = 190 K) and weakly ferromagnetic below 35 K, however, Bi₆Ti_2.8Fe_1.52Mn_0.68O₁₈ gives a distinct room‐temperature in‐plane ferromagnetic signature (M_s = 0.74 emu/g, μ₀H_c =7 mT). Microstructural analysis, coupled with the use of a statistical analysis of the data, allows us to conclude that ferromagnetism does not originate from second phase inclusions, with a confidence level of 99.5%. Piezoresponse force microscopy (PFM) demonstrates room‐temperature ferroelectricity in both films, whereas PFM observations on Bi₆Ti_2.8Fe_1.52Mn_0.68O₁₈ show Aurivillius grains undergo ferroelectric domain polarization switching induced by an applied magnetic field. Here, we show for the first time that Bi₆Ti_2.8Fe_1.52Mn_0.68O₁₈ thin films are both ferroelectric and ferromagnetic and, demonstrate magnetic field‐induced switching of ferroelectric polarization in individual Aurivillius phase grains at room temperature.     

Nitrofurazone‐loaded PVA–PEG semi‐IPN for application as hydrogel dressing for normal and burn wounds

Hydrogels have been used in a wide variety of biomedical devises, particularly in the field of drug delivery, tissue engineering, and wound healing. In this study, a polyvinyl alcohol (PVA)–polyethylene glycol (PEG) semi‐interpenetrating hydrogel network (IPN)‐based wound dressing system containing nitrofurazone (NFZ) was synthesized by chemical crosslinking technique. The introduction of PEG to PVA matrix led to reduction in the water vapor transmission rate, which in‐turn resulted in improved healing activity. Drug‐loaded IPNs were prepared by mixing aqueous solution of NFZ with the optimized PVA–PEG formulation subsequent to the crosslinking step. The in vitro diffusion studies of NFZ indicated a relatively slow release of drug resulting from its microencapsulation in the polymeric matrix. Subsequently, in vivo wound healing efficacy toward acute and burn wound healing in experimental rats was investigated. Semi‐IPN hydrogel loaded with NFZ dressing improved the overall healing rate in both acute and burn wounds, as evidenced by significant increase in total protein, hydroxyproline and hexosamine contents. Histological examinations also correlated well with the biochemical findings. A faster wound contraction was also observed in hydrogel treated acute and burn wounds. The results indicated that PVA–PEG semi‐IPN hydrogel based dressing systems containing NFZ could be used as an effective wound dressing material. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013     

Ultrasound assisted synthesis of metal-1,3-diketonates

A simple and convenient methodology for complex formation of wide variety of transition metals/alkaline earth metals with 1,3-diketones under sonication have been reported. The present method showed a significant rate enhancement for metal complex formation in the presence of ultrasound as compared to their silent counterpart, thereby providing higher yields. The role of ultrasound and solvent were also discussed. Moreover, the metal-1,3-diketonates were characterized by various techniques like FT-IR, ¹H NMR, mass spectrometry and magnetic susceptibility.     

Effect of Concentration of Ammonium Poly(acrylate) Dispersant and MgO on Coagulation Characteristics of Aqueous Alumina Direct Coagulation Casting Slurries

Coagulation of aqueous alumina slurries prepared using various concentrations (0.43–1.04 wt% based on alumina) of ammonium poly(acrylate) dispersant by MgO has been studied for direct coagulation casting (DCC) of alumina. The slurries prepared at dispersant concentration below 0.84 wt% are not suitable for DCC at room temperature (∼30 ° C) as they undergo premature coagulation. Mixing the slurry with MgO at a low temperature of nearly 5 ° C slows down the reactions leading to coagulation and keeps the slurry viscosity low for a sufficient period of time. Coagulation of slurries prepared at a dispersant concentration of 0.92 wt% and above at room temperature requires MgO concentrations much higher than the equivalent amount required for reaction with the dispersant. This anomalous behavior at higher dispersant concentration is explained such that the Mg-poly(acrylate) formed by the reaction between ammonium poly(acrylate) and MgO formed a sheath over the remaining MgO particles and prevented them from further dissolution at room temperature. Faster coagulation could be achieved by heating the slurries after casting in closed molds. The Mg-poly(acrylate) acts as a binder and stabilizes the coagulated bodies as their strength and stability against oscillatory stresses increase with an increase in dispersant concentration.     

Fault detection model‐based controller for process systems

This paper develops a model‐based control system for fault detection and controller reconfiguration using stochastic model predictive control (MPC). The system can determine online the optimal control actions, detect faults quickly, and reconfigure the controller accordingly. Such a system can perform its function correctly in the presence of internal faults. A fault detection model based (FDMB) controller consists of two main parts: the first is fault detection and diagnosis (FDD) and the second is controller reconfiguration (CR). Systems subject to such abrupt failures are modeled as stochastic hybrid systems with variable‐structure. This paper deals with three challenging issues: design of the fault‐model set; estimation of hybrid multiple models; and stochastic MPC of hybrid multiple models. For the first issue, we propose a simple scheme for designing a fault model set based on random variables. For the second issue, we consider and select a fast and reliable FDD system applied to the above model set. Finally, we develop a stochastic MPC scheme for multiple model CR with soft switching signals based on the weighted probabilities of the outputs of different models. Simulations for the proposed FDMB controller are illustrated and analyzed. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Statistical analysis of recombination properties of the boron-oxygen defect in p-type Czochralski silicon

This paper presents the application of lifetime spectroscopy to the study of carrier-induced degradation ascribed to the boron-oxygen (BO) defect. Specifically, a large data set of p-type silicon samples is used to investigate two important aspects of carrier lifetime analysis: ①the methods used to extract the recombination lifetime associated with the defect and ② the underlying assumption that carrier injection does not affect lifetime components unrelated to the defect. The results demonstrate that the capture cross section ratio associated with the donor level of the BO defect (k₁) vary widely depending on the specific method used to extract the defect-specific recombination lifetime. For the data set studied here, it was also found that illumination used to form the defect caused minor, but statistically significant changes in the surface passivation used. This violation of the fundamental assumption could be accounted for by applying appropriate curve fitting methods, resulting in an improved estimate of k₁ (11.90±0.45) for the fully formed BO defect when modeled using the donor level alone. Illumination also appeared to cause a minor, apparently injectionindependent change in lifetime that could not be attributed to the donor level of the BO defect alone and is likely related to the acceptor level of the BO defect. While specific to the BO defect, this study has implications for the use of lifetime spectroscopy to study other carrier induced defects. Finally, we demonstrate the use of a unit-less regression goodness-of-fit metric for lifetime data that is easy to interpret and accounts for repeatability error.     

RF Behavior of Cylindrical Cavity Based 240 GHz, 1 MW Gyrotron for Future Tokamak System

In this paper, we present the RF behavior of conventional cylindrical interaction cavity for 240 GHz, 1 MW gyrotron for futuristic plasma fusion reactors. Very high-order TE mode is searched for this gyrotron to minimize the Ohmic wall loading at the interaction cavity. The mode selection process is carried out rigorously to analyze the mode competition and design feasibility. The cold cavity analysis and beam-wave interaction computation are carried out to finalize the cavity design. The detail parametric analyses for interaction cavity are performed in terms of mode stability, interaction efficiency and frequency. In addition, the design of triode type magnetron injection gun is also discussed. The electron beam parameters such as velocity ratio and velocity spread are optimized as per the requirement at interaction cavity. The design studies presented here confirm the realization of CW, 1 MW power at 240 GHz frequency at TE_46,17 mode.     

Electricity price classification using extreme learning machines

Forecasting electricity prices has been a widely investigated research issue in the deregulated power market scenario. High price volatilities, price spikes caused by a number of factors such as weather uncertainty, fluctuating fuel prices, transmission bottlenecks, etc., make the task of accurate price forecasting a formidable challenge for the market participants. A number of models have been proposed by researchers; however, achieving high accuracy is always not possible. In some specific applications such as self-scheduling by demand side participants, certain price thresholds are more useful than accurate price forecasts. In this paper, we have investigated the application of a novel neural network-based technique called extreme learning machine for the problem of classification of future electricity prices with respect to certain price thresholds. Different models corresponding to different lead times are developed and tested with data corresponding to Ontario and PJM markets. It is observed that classification with ELM is fast, less sensitive to user defined parameters and easily implementable.     

TER-Plus: paraphrase, semantic, and alignment enhancements to Translation Edit Rate

This paper describes a new evaluation metric, TER-Plus (TERp) for automatic evaluation of machine translation (MT). TERp is an extension of Translation Edit Rate (TER). It builds on the success of TER as an evaluation metric and alignment tool and addresses several of its weaknesses through the use of paraphrases, stemming, synonyms, as well as edit costs that can be automatically optimized to correlate better with various types of human judgments. We present a correlation study comparing TERp to BLEU, METEOR and TER, and illustrate that TERp can better evaluate translation adequacy.