Characterization of RF sputtered thin film potassium sodium niobate (KNN) with silicon and nickel electrodes

A low cost recipe for thin film deposition of Potassium Sodium Niobate, (Na,K)NbO₃ (KNN) is pursued. The use of expensive noble metals as electrodes was avoided and instead highly doped silicon was used for both the structural layer and the bottom electrode. Nickel was used for the top electrode. In order to evaluate the outcome, the films were studied in terms of stoichiometry, crystal structure and leakage current density. RF sputtering of thin films of KNN at room temperature was successfully done. Proper crystal structure (Perovskite structure) was achieved after post deposition annealing. Though the leakage current density exhibited high dependency on the polarity of the applied voltage, a leakage current density of 1 × 10⁻⁶ A/cm² at 100 kV/cm was measured. A stoichiometry study revealed that the relative ratio of the volatile elements (Na and K) in the samples was within the acceptable range, however, a total loss of about 25–33 % was observed.

     

A Sliding Mode Multiobserver Based on an Uncoupled Multimodel: An Application on a Transesterification Reaction

This paper concerns the design of robust sliding mode multiobserver for nonlinear systems. A discrete uncoupled multimodel structure is retained for the modeling of nonlinear systems. Unlike the classically used multimodel structures, the retained uncoupled multimodel is known by its flexibility of modeling, thus, the structures of the partial models are adapted to the complexity of the local models in each operating zone. Sufficient conditions are provided, in terms of linear matrix inequalities (LMIs), to ensure the asymptotic stability of the proposed sliding mode multiobserver. A convergence analysis is achieved to obtain the convergence radius. A numerical example and a real time application on a transesterification reactor are carried out to illustrate, once again, the performance of the proposed sliding mode multiobserver in terms of precision and rapidity of convergence.     

Sliding wear transition for the CW614 brass alloy

Dry sliding wear tests were performed on a CW614 brass alloy using a pin-on-ring configuration. Wear kinetics were measured within a load range of 20–80 N and sliding velocity ranging from 1 to 7 m/s. Chemical compositions, morphologies and microstructures of worn surfaces and wear debris were characterised by scanning electron microscope (SEM) and energy dispersive X-ray spectrometer (EDS). Two main wear regimes have been observed: severe wear and mild wear. The results of wear tests and metallographic investigations on worn surfaces have been summarised in a wear mechanism map. It was found that the wear transition is controlled by a critical temperature at the contact surface.     

An optimization strategy based on a metamodel applied for the prediction of the initial blank shape in a deep drawing process

The transformation of the sheet into a product without failure and excess of material in a deep drawing operation means that the initial blanks should be correctly designed. Therefore, the initial blank design is a critical step in deep drawing design procedure. Consequently, an easy approach for engineers in predicting the initial blank shape is necessary to reduce wastage in material and to overcome the large time consumed in the classical approaches. Thus, the aim of the present investigation is to propose an automatic procedure for the quick sheet metal forming optimization. In fact, a metamodel will be build based on artificial neural networks which will be coupled then with an optimization procedure in order to predict the initial blank shape in a rectangular cup deep drawing operation. The metamodel is built from the finite element simulations using ABAQUS commercial code. This procedure allows a significant reduce of the CPU time compared to classical optimization one. The results show that the desired shape is in good agreement with the one calculated using the optimized blank shape.     

Almost Sure Practical Exponential Stability of Nonlinear Disturbed Stochastic Systems with Guaranteed Decay Rate

In this paper, we consider nonlinear stochastic differential equations driven by multiplicative noises and affected by exogenous disturbances. Sufficient conditions are investigated for almost sure practical exponential stability of the non trivial solutions of these equations. A lower bound of the decay rate of these solutions is guaranteed.     

Aqueous hydrazine borane N2H4BH3 and nickel-based catalyst: An effective couple for the release of hydrogen in near-ambient conditions

Nickel-based bimetallic catalysts were screened using the sodium borohydride NaBH₄ hydrolysis and the aqueous hydrazine borane N₂H₄BH₃ dehydrogenation. A total of 22 bimetallic catalysts were synthesized according to an easy process while focusing on metals like Fe, Co, Ni, Cu, Rh, Pd, Ag, Ir, Pt and Au. In the end, the bimetallic candidate Ni_87.5Pt_12.5 showed to be the most active and the most selective for the dehydrogenation of N₂H₄BH₃. At 70?°C, it is able to decompose N₂H₄BH₃ into 5.8 equivalents of H₂+N₂ in less than 12?min such as: N₂H₄BH₃?+?3H₂O?→?0.95 N₂?+?0.1 NH₃?+?B(OH)₃?+?4.85H₂. Durability and stability tests were also performed. In our conditions, Ni_87.5Pt_12.5 was found to suffer from small loss of performance because of an electronic evolution of the catalytic surface leading to modified sorption properties of the catalytic sites. Our main results are reported and discussed herein.     

A deep stacked wavelet auto-encoders to supervised feature extraction to pattern classification

The major issue in pattern classification is in the extraction of features in the training phase. The focus of this work is on combining the ability of wavelet networks and the deep learning techniques to propose a new supervised feature extraction method to pattern classification. This new approach allows the classification of all classes of the dataset by the reconstruction of a Deep Stacked wavelet Auto-Encoder. This Network is obtained after a series of wavelet Auto-Encoders followed by a Softmax classifier at the last layer. Finally, a fine-tuning is applied for the improvement of our result using a back propagation algorithm. Our approach is tested with different image datasets which are the COIL-100, the APTI and the ImageNet datasets and is also tested with two other audio corpuses that contain Arabic words and French words. The experimental test demonstrates the efficiency of our network for image and audio classification compared to other methods.     

Traction–separation laws for progressive failure of bonded scarf repair of composite panel

Repair of composites has become of considerable importance recently as modern commercial airliners employ much more composites in their airframes then previously. Major maintenance, repair, and overhaul (MRO) centers must contend with issues of damage tolerance, efficiency, integrity and cost of repairs. Computational methods have been developed to sufficiently sophisticated levels to aid in the design, evaluation and optimization of proposed repair schemes before they are implemented, potentially saving time and cost. In this paper, parametric studies on progressive failure analysis of a bonded scarf repair of a composite panel was performed. The study finds that finite element models with an appropriate material property degradation scheme using the micromechanics of failure criterion are able to predict the failure load of undamaged and damaged specimen. Results of the parametric studies on adhesive properties suggest that the failure stress of a repaired composite panel is more sensitive to the strength of the cohesive elements than to its toughness when a linear or trapezoidal softening traction–separation law is used, but the influence of adhesive strength is not significant when exponential softening traction–separation law is used.     

Modified microwave method for the synthesis of visible light-responsive TiO2/MWCNTs nanocatalysts

Recently, TiO₂/multi-walled carbon nanotube (MWCNT) hybrid nanocatalysts have been a subject of high interest due to their excellent structures, large surface areas and peculiar optical properties, which enhance their photocatalytic performance. In this work, a modified microwave technique was used to rapidly synthesise a TiO₂/MWCNT nanocatalyst with a large surface area. X-ray powder diffraction, field-emission scanning electron microscopy, transmission electron microscopy and Brunauer-Emmett-Teller measurements were used to characterise the structure, morphology and the surface area of the sample. The photocatalytic activity of the hybrid nanocatalysts was evaluated through a comparison of the degradation of methylene blue dye under irradiation with ultraviolet and visible light. The results showed that the TiO₂/MWCNT hybrid nanocatalysts degraded 34.9% of the methylene blue (MB) under irradiation with ultraviolet light, whereas 96.3% of the MB was degraded under irradiation with visible light.