Origin of visible photoluminescence from porous silicon as studied by Raman spectroscopy

In this paper we discuss the different models proposed to explain the visible luminescence in porous silicon (PS). We review our recent photoluminescence and Raman studies on PS as a function of different preparation conditions and isochronal thermal annealing. Our results can be explained by a hybrid model which incorporates both nanostructures for quantum confinement and silicon complexes (such as SiH _x and siloxene) and defects at Si/SiO₂ interfaces as luminescent centres.     

Deposition and characterization of alumina films produced by combustion flame pyrolysis

Alumina coatings have been deposited by combustion flame pyrolysis on amorphous silica and stainless steel substrates with the objective to study the effect of solvent composition, Fe³⁺ addition and determine the thermal fatigue lifetime. The effect of solvent composition on crystallinity, transformation temperature and hardness are studied, three different solvent compositions are chosen for preparing the aluminium nitrate solution. Using 100% water, as-deposited films are amorphous and transform to α-alumina only upon annealing, while this equilibrium phase is directly obtained but with a porous microstructure by using 100% methanol. The hardness of the coatings varies with the flammability of the solvents. The effect of Fe³⁺ addition on the crystallization of alumina is studied by combustion pyrolysis of aqueous solutions of Al³⁺ and Fe³⁺ nitrates. Small amount of ferric nitrate reduced the transformation temperature by 100 °C. Thermal cycling of as-deposited amorphous alumina on stainless steel substrate is carried out at different temperatures to determine the thermal fatigue lifetime.     

Mixed Micelles of Surface Active Ionic Liquid (SAIL)–Octylphenol Ethoxylate: A Novel Reaction Medium for Selective Oxidation of Toluene to Benzaldehyde

Ionic liquids have been found to be suitable alternatives to volatile organic solvents in chemical transformation. Through a proper choice of cations and anions, the properties of an ionic liquid can be tuned so that it resembles an amphiphile. Such specially designed molecules are known as surface-active ionic liquids (SAIL). Like conventional surfactants, SAIL also form aggregates in an aqueous medium. Studies show that the mixing of SAIL with conventional surfactants leads to synergistic micellization. However, very few reports are available on the application of such systems as reaction media. Present study focuses on the application of mixed micelles of 1-tetradecyl-3-methylimidazol-1-ium bromide, ([C₁₄mim]Br) with nonionic surfactant, Octylphenol ethoxylate with 10 moles of ethylene oxide (OPE-10). Enhanced solubilization and selective catalytic oxidation of toluene using hydrogen peroxide as an oxidant and tungstic acid as a catalyst have been studied in detail using this system.     

Intensity standardization simplifies brain MR image segmentation

Typically, brain MR images present significant intensity variation across patients and scanners. Consequently, training a classifier on a set of images and using it subsequently for brain segmentation may yield poor results. Adaptive iterative methods usually need to be employed to account for the variations of the particular scan. These methods are complicated, difficult to implement and often involve significant computational costs. In this paper, a simple, non-iterative method is proposed for brain MR image segmentation. Two preprocessing techniques, namely intensity-inhomogeneity-correction, and more importantly MR image intensity standardization, used prior to segmentation, play a vital role in making the MR image intensities have a tissue-specific numeric meaning, which leads us to a very simple brain tissue segmentation strategy.Vectorial scale-based fuzzy connectedness and certain morphological operations are utilized first to generate the brain intracranial mask. The fuzzy membership value of each voxel within the intracranial mask for each brain tissue is then estimated. Finally, a maximum likelihood criterion with spatial constraints taken into account is utilized in classifying all voxels in the intracranial mask into different brain tissue groups. A set of inhomogeneity corrected and intensity standardized images is utilized as a training data set. We introduce two methods to estimate fuzzy membership values. In the first method, called SMG (for simple membership based on a gaussian model), the fuzzy membership value is estimated by fitting a multivariate Gaussian model to the intensity distribution of each brain tissue whose mean intensity vector and covariance matrix are estimated and fixed from the training data sets. The second method, called SMH (for simple membership based on a histogram), estimates fuzzy membership value directly via the intensity distribution of each brain tissue obtained from the training data sets. We present several studies to evaluate the performance of these two methods based on 10 clinical MR images of normal subjects and 10 clinical MR images of Multiple Sclerosis (MS) patients. A quantitative comparison indicates that both methods have overall better accuracy than the k-nearest neighbors (kNN) method, and have much better efficiency than the Finite Mixture (FM) model-based Expectation-Maximization (EM) method. Accuracy is similar for our methods and EM method for the normal subject data sets, but much better for our methods for the patient data sets.     

Improved generative semisupervised learning based on finely grained component-conditional class labeling

We introduce new inductive, generative semisupervised mixtures with more finely grained class label generation mechanisms than in previous work. Our models combine advantages of semisupervised mixtures, which achieve label extrapolation over a component, and nearest-neighbor (NN)/nearest-prototype (NP) classification, which achieve accurate classification in the vicinity of labeled samples or prototypes. For our NN-based method, we propose a novel two-stage stochastic data generation, with all samples first generated using a standard finite mixture and then all class labels generated, conditioned on the samples and their components of origin. This mechanism entails an underlying Markov random field, specific to each mixture component or cluster. We invoke the pseudo-likelihood formulation, which forms the basis for an approximate generalized expectation-maximization model learning algorithm. Our NP-based model overcomes a problem with the NN-based model that manifests at very low labeled fractions. Both models are advantageous when within-component class proportions are not constant over the feature space region "owned by" a component. The practicality of this scenario is borne out by experiments on UC Irvine data sets, which demonstrate significant gains in classification accuracy over previous semisupervised mixtures and also overall gains, over KNN classification. Moreover, for very small labeled fractions, our methods overall outperform supervised linear and nonlinear kernel support vector machines.     

Erosion resistance of nano-filled silicone rubber

The paper presents the experimental results obtained on the erosion resistance of silicone rubber (SIR) filled with 12 nm size fumed silica (nano filler) to those filled with 5 /spl mu/m size silica filler (micro filler). The ASTM 2303 inclined plane tracking and erosion test was used in the comparison as well as an infrared laser as the source of heat to erode the SIR samples. The erosion resistance of the SIR materials increased with increasing percentage of the fillers, and it was observed that 10% by weight of nano-filled SIR gives a performance that is similar to that obtained with 50% by weight of micro-filled SIR. The low frequency components of leakage current and the eroded mass are used to evaluate the relative erosion resistance of the composites and the third harmonic component of the leakage current shows good correlation to the measured eroded mass. The paper discusses the possible reasons for the improvement in the erosion resistance of nano-filled silicone composites.     

Correlation of damage, dry band arcing energy, and temperature in inclined plane testing of silicone rubber for outdoor insulation

Silicone rubber samples, filled with 30% alumina trihydrate, subjected to the ASTM inclined plane test, show good correlation between the measured harmonic power components of dry band arcing and the surface temperature recorded with a thermovision camera. The simultaneous study of the low frequency fast Fourier transform and variations in temperature shows that when the temperature rises above 100/spl deg/C there is a well defined increase in the low frequency harmonic components of the measured power; however, this trend is not verified by the fundamental component. The eroded mass of the samples is measured independently by a laser approach and a model to rank the tested samples using the inclined plane test is developed. The model is validated by applying it to both the recorded harmonic power components and temperature, resulting in the correct ranking of damage on the samples tested in the inclined plane test.     

Effect of Humidity on Wear of TiN Coatings: Role of Capillary Condensation

Metallurgical and Materials Transactions A - Coated ball-on-disk wear configuration was used to study the effect of relative humidity, water vapor pressure, and water on wear of TiN coatings in the...     

Relative importance of contingency variables for advanced manufacturing technology

The academic literature on manufacturing technology was reviewed to identify key contingency variables that can moderate the influence of advanced manufacturing technologies on manufacturing performance. Using this literature as a basis, this research proposes an ordering of relative importance of individual contingency variables among a set of contingency variables that influence the advanced manufacturing technologies–manufacturing performance relationship. Plant level data from a large multi-industry sample of firms are analysed, using regression and Chow tests, to confirm the hypothesized ordering among the contingency variables. The findings indicate that lean manufacturing and work organization practices are the primary contingency variables that affect the advanced manufacturing technologies–manufacturing performance relationship. The implications of the results are discussed in the light of past findings and their impact on practice.