Growth mechanism of the interdiffusion zone between platinum modified bond coats and single crystal superalloys

Pt-modified beta-NiAl bond coats are applied over the superalloys for oxidation protection in jet engine applications. However, as shown in this study, it also enhances the growth of the interdiffusion zone developed between the bond coat and the superalloy along with brittle precipitates. Location of the Kirkendall plane indicates that a precipitate free sublayer grows from the bond coat, whereas another sublayer grows from the superalloy containing very high volume fraction of precipitates. With increasing Pt content, thickness of both the sublayers increases because of an increase in diffusion rates of the components. Quantitative electron probe microanalysis indicates high concentration of refractory components in the precipitates. Transmission electron microscopy shows that Rene N5 superalloy produces TCP phases mu and P, whereas CMSX-4 superalloy produces mu and sigma in the interdiffusion zone. With increasing Pt content in the bond coat, the average size of the precipitates decreases when coupled with Rene N5. Precipitates become much finer when the same bond coats are coupled with CMSX-4. (C) 2015 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

CuIn1-xAlxSe2 Thin Films Grown by Co-Sputtering and Modified Selenization: Application in Flexible Solar Cells

Thin films of CuIn1-xAlxSe2 (CIAS) were grown on the flexible 10 micrometer thin stainless steel substrates, by dc co-sputtering from the elemental cathodes, followed by annealing with modified selenization. CuInAl alloyed precursor films were selenized both by noble gas assisted Se vapor transport in a tubular furnace and vacuum evaporation of Se in an evaporation chamber. CIAS thin films were optimized for better adhesion. X-ray diffraction, scanning electron microscopy, and UV-visible absorption spectroscopy were used to characterize the selenized films. The composition of CIAS films was varied by substituting In with Al in CuInSe2 (CIS) from 0 <= x <= 0.65 (x = Al/Al+In). Lattice parameters, average crystallite sizes, and compact density of the films, decreased when compared to CIS and (112) peak shifted to higher Bragg's angle, upon Al incorporation. The dislocation density and strain were found to increase with Al doping. Solar cells with SS/Mo/CIAS/CdS/iZnO: AZnO/Al configuration were fabricated and were tested for current-voltage characteristics for various `x' values, under Air Mass 1.5 Global one sun illumination. The best CIAS solar cell showed the efficiency of 6.8%, with x = 0.13, Eg = 1.17 eV, fill factor 45.04, and short circuit current density J(sc) 30 mA/cm(2).

l(1)-K-SVD: A robust dictionary learning algorithm with simultaneous update

We develop a new dictionary learning algorithm called the l(1)-K-svp, by minimizing the l(1) distortion on the data term. The proposed formulation corresponds to maximum a posteriori estimation assuming a Laplacian prior on the coefficient matrix and additive noise, and is, in general, robust to non-Gaussian noise. The l(1) distortion is minimized by employing the iteratively reweighted least-squares algorithm. The dictionary atoms and the corresponding sparse coefficients are simultaneously estimated in the dictionary update step. Experimental results show that l(1)-K-SVD results in noise-robustness, faster convergence, and higher atom recovery rate than the method of optimal directions, K-SVD, and the robust dictionary learning algorithm (RDL), in Gaussian as well as non-Gaussian noise. For a fixed value of sparsity, number of dictionary atoms, and data dimension, l(1)-K-SVD outperforms K-SVD and RDL on small training sets. We also consider the generalized l(p), 0 < p < 1, data metric to tackle heavy-tailed/impulsive noise. In an image denoising application, l(1)-K-SVD was found to result in higher peak signal-to-noise ratio (PSNR) over K-SVD for Laplacian noise. The structural similarity index increases by 0.1 for low input PSNR, which is significant and demonstrates the efficacy of the proposed method. (C) 2015 Elsevier B.V. All rights reserved.

Voltammetric determination of paracetamol, tramadol and caffeine using poly(Nile blue) modified glassy carbon electrode

A poly(Nile blue) modified glassy carbon electrode (PNBMGCE) was fabricated by electropolymerisation of Nile blue (NB) monomer using cyclic voltammetry (CV) and was used for the determination of paracetamol (ACOP), tramadol (TRA) and caffeine (CAF). The electrochemical investigations showed that PNB - film formed on the surface of glassy carbon electrode (GCE) improved the electroactive surface area and displayed a remarkable increase in the peak current and a substantial decrease in over potential of ACOP, TRA and CAF when compared to bare GCE. The dependence of peak current and potential on pH, sweep rate and concentration were also investigated at the surface of PNBMGCE. It showed good sensitivity and selectivity in a wide linear range from 2.0 x 10(-7) to 1.62 x 10(-5) M, 1.0 x 10(-6) to 3.1 x 10(-4) M and 8.0 x 10(-7) to 2.0 x 10(-5) M, with detection limits of 0.08, 0.5 and 0.1 mu M, for ACOP, TRA and CAF, respectively. The PNBMGCE was also successfully applied for the determination of ACOP, TRA and CAF in pharmaceutical dosage forms. (C) 2016 Elsevier B.V. All rights reserved.

A Stochastic Approximation Algorithm for Quantile Estimation

In this paper, we present two new stochastic approximation algorithms for the problem of quantile estimation. The algorithms uses the characterization of the quantile provided in terms of an optimization problem in 1]. The algorithms take the shape of a stochastic gradient descent which minimizes the optimization problem. Asymptotic convergence of the algorithms to the true quantile is proven using the ODE method. The theoretical results are also supplemented through empirical evidence. The algorithms are shown to provide significant improvement in terms of memory requirement and accuracy.

Comparison of Matching Pursuit Algorithm with Other Signal Processing Techniques for Computation of the Time-Frequency Power Spectrum of Brain Signals

Signals recorded from the brain often show rhythmic patterns at different frequencies, which are tightly coupled to the external stimuli as well as the internal state of the subject. In addition, these signals have very transient structures related to spiking or sudden onset of a stimulus, which have durations not exceeding tens of milliseconds. Further, brain signals are highly nonstationary because both behavioral state and external stimuli can change on a short time scale. It is therefore essential to study brain signals using techniques that can represent both rhythmic and transient components of the signal, something not always possible using standard signal processing techniques such as short time fourier transform, multitaper method, wavelet transform, or Hilbert transform. In this review, we describe a multiscale decomposition technique based on an over-complete dictionary called matching pursuit (MP), and show that it is able to capture both a sharp stimulus-onset transient and a sustained gamma rhythm in local field potential recorded from the primary visual cortex. We compare the performance of MP with other techniques and discuss its advantages and limitations. Data and codes for generating all time-frequency power spectra are provided.

A Novel Approach for Multispectral Satellite Image Classification Based on the Bat Algorithm

Among the multiple advantages and applications of remote sensing, one of the most important uses is to solve the problem of crop classification, i.e., differentiating between various crop types. Satellite images are a reliable source for investigating the temporal changes in crop cultivated areas. In this letter, we propose a novel bat algorithm (BA)-based clustering approach for solving crop type classification problems using a multispectral satellite image. The proposed partitional clustering algorithm is used to extract information in the form of optimal cluster centers from training samples. The extracted cluster centers are then validated on test samples. A real-time multispectral satellite image and one benchmark data set from the University of California, Irvine (UCI) repository are used to demonstrate the robustness of the proposed algorithm. The performance of the BA is compared with two other nature-inspired metaheuristic techniques, namely, genetic algorithm and particle swarm optimization. The performance is also compared with the existing hybrid approach such as the BA with K-means. From the results obtained, it can be concluded that the BA can be successfully applied to solve crop type classification problems.

Minuscule weight percent of graphene oxide and reduced graphene oxide modified Ag3PO4: new insight into improved photocatalytic activity

Designing and fabricating hybrid systems with a visible light active semiconductor as one of its components is an important research area for the development of highly efficient photocatalysts. Herein, we report visible-light driven photocatalytic activity of graphene oxide (GO) and controllably reduced GO (rGO) modified Ag3PO4 composites fabricated by an in situ method. Concentration of graphene derivatives in GO/rGO-Ag3PO4 composites was in the range of 0.13-0.52 wt% which is very minute compared to those reported previously. The optimal concentration of GO in Ag3PO4 with a kinetics (k = 1.23 +/- 0.04 min(-1)) for the degradation of rhodamine B is 0.26 wt%. GO-Ag3PO4 photocatalysts display an improved catalytic activity compared with pristine and rGOs modified Ag3PO4. In line with this, GO/rGO-Ag3PO4 composites show improved photocatalytic activity for the degradation of 2-chlorophenol compared with Degussa P-25. Our experiments with GO reduced to different extents show that, rGO with more polar functional groups exhibits a higher photocatalytic efficiency. The photocatalytic activity in the presence of different scavengers reveals that holes and O-2(-center dot) reactive species play major roles in the degradation phenomenon. In view of our experimental results and reported theoretical studies, a change in conduction band energy level and variation in the contribution of different charge orbitals (C 2p and O 2p) to the conduction band in the composite favours electron flow from graphene derivatives to the semiconductor, enhancing its photocatalytic response.

IMAGE DENOISING USING OPTIMALLY WEIGHTED BILATERAL FILTERS: A SURE AND FAST APPROACH

The bilateral filter is known to be quite effective in denoising images corrupted with small dosages of additive Gaussian noise. The denoising performance of the filter, however, is known to degrade quickly with the increase in noise level. Several adaptations of the filter have been proposed in the literature to address this shortcoming, but often at a substantial computational overhead. In this paper, we report a simple pre-processing step that can substantially improve the denoising performance of the bilateral filter, at almost no additional cost. The modified filter is designed to be robust at large noise levels, and often tends to perform poorly below a certain noise threshold. To get the best of the original and the modified filter, we propose to combine them in a weighted fashion, where the weights are chosen to minimize (a surrogate of) the oracle mean-squared-error (MSE). The optimally-weighted filter is thus guaranteed to perform better than either of the component filters in terms of the MSE, at all noise levels. We also provide a fast algorithm for the weighted filtering. Visual and quantitative denoising results on standard test images are reported which demonstrate that the improvement over the original filter is significant both visually and in terms of PSNR. Moreover, the denoising performance of the optimally-weighted bilateral filter is competitive with the computation-intensive non-local means filter.