Investigation of Electrical and Structural Properties of Ag/TiO2/n-InP/Au Schottky Diodes with Different Thickness TiO2 Interface

Silicon - In this study, structural and electrical properties of Ag/TiO2/n-InP/Au Schottky barrier diodes, constructed with sputtering method on n-InP wafer, are investigated. Particle size, d-...     

Toughening of unmodified polyvinylchloride through the addition of nanoparticulate calcium carbonate and titanate coupling agent

PVC/CaCO₃ polymer nanocomposites of differing compositions were produced using a two‐roll mill and compression molding. In all formulations, 0.6 phr of titanate was incorporated to assist dispersion during processing. The morphology was observed using transmission electron microscopy, and the static and dynamic mechanical and fracture properties were determined. Fracture toughness examination was performed according to strain energy release test method. The presence of nanometer‐sized CaCO₃ particles led to a slight decrease in the tensile strength but improved the impact energy absorption, storage modulus, and fracture toughness. The use of titanate coupling agent softened the polymer matrix and reduced the matrix's modulus. Fracture surface examinations by scanning electron microscopy showed that the coupling agent improved particle–matrix bonding and inhibited void formation around the particles. Finite element analysis suggested that the improved particle–matrix bonding reduced the matrix's plasticity around the particles, which decreased the toughening efficiency of the composites. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013     

Microcomposites of Poly(ε‐caprolactone) and Poly(methyl methacrylate) Prepared by Suspension Polymerization in the Presence of Poly(ε‐caprolactone) Macromonomer

Poly(methyl methacrylate)‐poly(ε‐caprolactone) (PMMA/PCL) microparticles were synthesized by suspension polymerization of methyl methacrylate in the presence of PCL. The incorporation of a small amount of a macromonomer, methacryloyl‐terminated PCL (M‐PCL), into the reaction mixture, led to the formation of grafted systems, namely PMMA‐g‐PCL/PCL. The synthesis of the macromonomer and its characterization by nuclear magnetic spectroscopy (¹H NMR) is described. The role of M‐PCL as an effective compatibilizing agent in the composite was investigated. PMMA/PCL and PMMA‐g‐PCL/PCL composites were fully characterized by ¹H NMR, gel permeation chromatography (GPC) and thermal analysis, including thermogravimetric analysis (TGA), conventional differential scanning calorimetry (DSC), modulated DSC (MDSC) and dynamic mechanical thermal analysis (DMTA). Finally, the morphology of the prepared systems was investigated by scanning electron microscopy (SEM). The addition of compatibilizing agent led the formation of a more homogeneous microcomposite with improved mechanical properties.

SEM picture of PMMA‐g‐PCL/PCL composite surface.     

Embedding complete binary trees in product graphs

This paper shows how to embed complete binary trees in products of complete binary trees, products of shuffle‐exchange graphs, and products of de Bruijn graphs with small dilation and congestion. In the embedding results presented here the size of the host graph can be fixed to an arbitrary size, while we define no bound on the size of the guest graph. This is motivated by the fact that the host architecture has a fixed number of processors due to its physical design, while the guest graph can grow arbitrarily large depending on the application. The results of this paper widen the class of computations that can be performed on these product graphs which are often cited as being low‐cost alternatives for hypercubes. This revised version was published online in June 2006 with corrections to the Cover Date.     

Modeling and prediction of Turkey’s electricity consumption using Artificial Neural Networks

Artificial Neural Networks are proposed to model and predict electricity consumption of Turkey. Multi layer perceptron with backpropagation training algorithm is used as the neural network topology. Tangent-sigmoid and pure-linear transfer functions are selected in the hidden and output layer processing elements, respectively. These input–output network models are a result of relationships that exist among electricity consumption and several other socioeconomic variables. Electricity consumption is modeled as a function of economic indicators such as population, gross national product, imports and exports. It is also modeled using export–import ratio and time input only. Performance comparison among different models is made based on absolute and percentage mean square error. Electricity consumption of Turkey is predicted until 2027 using data from 1975 to 2006 along with other economic indicators. The results show that electricity consumption can be modeled using Artificial Neural Networks, and the models can be used to predict future electricity consumption.     

The use of mixture experiments in tolerance allocation problems

The response surface methodology (RSM) approach can be used to determine the optimal component tolerances in an assembly. Frequently, response surface designs such as Box-Behnken design and central composite design are used in tolerance allocation problems. In this article, mixture experiments, which are essentially constructed for designing a blend composition, are proposed instead of response surface designs in order to observe the cost values. Also some advantages and disadvantages of mixture designs are discussed.     

Design of a new three-degree of freedom spherical motor for photovoltaic-tracking systems

Photovoltaic (PV) panels directly convert the solar energy to electrical energy. The amount of electrical energy converted by PV panels mainly depends on incident solar radiation. Sun tracking systems can be used to maximize energy production since they ensure keeping the photovoltaic panels perpendicular to the incoming solar radiation. Spherical motors, which have the linear and circular movement ability in three independent dimensions, can be used for precisely tracking the sun as a solution. In this study, a spherical motor controlled by a micro-controller is designed for a PV-tracking system with the ability to move on two axes. Performance of PV-tracking system over a fixed tilted one is evaluated for the climate condition of Denizli, Turkey. The designed sun tracking system is observed to be improving this performance apparently using the output voltages obtained for one day.     

A Distributed Allocation Mechanism for Network Resources Using Software Agents

Functionality of electronic commerce enabling technologies is dependent upon pervasive networks that a large number of applications share. Given the scarcity of network resources, it is imperative to allocate existing resources based on the actual needs of its users. In this paper, we study a two sided matching algorithm that enables such an allocation using tools from matching literature. Network system deploys intelligent agents that negotiate terms of allocation assignments with other intelligent agents that represent the users. We report the time complexity of the algorithm and give an example on how the mechanism performs.     

Free vibration analysis of an adhesively bonded functionally graded double containment cantilever joint

In this study, Genetic Algorithms (GAs) combined with the proposed neural networks were implemented to the free vibration analysis of an adhesively bonded double containment cantilever joint with a functionally graded plate. The proposed neural networks were trained and tested based on a limited number of data including the natural frequencies and modal strain energies calculated using the finite element method. GA evaluates a value generated iteratively by an objective function and this value is calculated by the finite element method. The iteration process restricts us apparently to use directly the finite element method in our multi-objective optimisation problem in which the natural frequency is maximised and the corresponding modal strain energy is minimised. The proposed neural networks were used accurately to predict the natural frequencies and modal strain energies instead of calculating directly them by using the finite element method. Consequently, the computation time and efforts were reduced considerably. The adhesive joint was observed to tend vertical bending modes and torsional modes. Therefore, the multi-objective optimisation problem was limited to only the first mode which appeared as a bending mode. The effects of the geometrical dimensions and the material composition variation through the plate thickness were investigated. As the material composition of the horizontal plate becomes ceramic rich, both natural frequency and modal strain energy of the adhesive joint increased regularly. The plate length and plate thickness were more effective geometrical design parameters whereas the support length and thickness were less effective. However, the adhesive thickness had a small effect on the optimal design of the adhesive joint as far as the natural frequencies and modal strain energies are concerned. The distributions of optimal solutions were also presented for the adhesive joints with fundamental joint lengths and material compositions in reference to their natural frequencies and corresponding modal strain energies.