Thermal buckling analysis of functionally graded perforated annular sector plates using 3D elasticity theory

Thermal buckling of annular sector plates with circular cutouts made of functionally graded material is analyzed in this article. Graded material is considered with two parts of ceramic structure made of zirconia and metal structure which is aluminum. Unlike most conducted research, the direction of property change is observed in three main directions. Thermal loading is assumed as a uniform increase in temperature influencing the whole sector. 3D-finite element method based on elasticity theory is used in this analysis, which resets first and second variations of potential energy of the sector to zero to find equilibrium and stability equations, respectively. Green’s nonlinear strain–displacement relations are used to obtain geometrical stiffness matrix. In the finite element method, unlike most studies, a 3D eight-nodded element is used, which has nodes in the direction of thickness. The circular cutouts of the sector have added to the complexity of the analysis. The finite element formulation is coded in MATLAB. Finally, the effect of different parameters such as dimension and number of cutouts, power law index, and orientation of graded material on the critical thermal buckling temperature is studied.     

Polypyrrole grafting onto the surface of pyrrole-modified silica nanoparticles prepared by one-step synthesis

The grafting of polypyrrole onto the surface of modified silica nanoparticles has been investigated. These silica nanoparticles were modified with pyrrole moieties prepared by the well-known Stober method in one-step starting from TEOS and a pyrrole-bearing trialkoxysilane compound. The effects of various reaction conditions, including reaction time, solvent, and molar ratio of water to alkoxy groups, have been investigated in order to obtain pyrrole-modified silica nanoparticles with the optimal core–shell structure and the smallest possible particle size. The grafting was carried out in aqueous FeCl₃ solution containing the modified silica nanoparticles, with pyrrole monomers already adsorbed on the surface of the particles by soaking. Several analytical tools have been employed to characterize the particles and to assess the degree of grafting, namely TEM, SEM, TGA, FTIR, and XPS. The final polypyrrole-grafted silica nanoparticles obtained had a mean diameter of about 220 nm and 50 wt.% of grafted polypyrrole with respect to the total weight of polypyrrole formed around the surface of the cores.     

Application of ionic liquids as an electrolyte additive on the electrochemical behavior of lead acid battery

Ionic liquids (ILs) belong to new branch of salts with unique properties which their applications have been increasing in electrochemical systems especially lithium-ion batteries. In the present work, for the first time, the effects of four ionic liquids as an electrolyte additive in battery's electrolyte were studied on the hydrogen and oxygen evolution overpotential and anodic layer formation on lead–antimony–tin grid alloy of lead acid battery. Cyclic and linear sweep voltammetric methods were used for this study in aqueous sulfuric acid solution. The morphology of grid surface after cyclic redox reaction was studied using scanning electron microscopy. The results show that most of added ionic liquids increase hydrogen overpotential and whereas they have no significant effect on oxygen overpotential. Furthermore ionic liquids increase antimony dissolution that might be related to interaction between Sb³⁺ and ionic liquids. Crystalline structure of PbSO₄ layer changed with presence of ionic liquids and larger PbSO₄ crystals were formed with some of them. These additives decrease the porosity of PbSO₄ perm selective membrane layer at the surface of electrode. Also cyclic voltammogram on carbon–PbO paste electrode shows that with the presence of ionic liquids, oxidation and reduction peak current intensively increased.     

Investigation of the micro-step control positioning system performance affected by random input signals

This paper gives the results of simulation and experimental investigation on the effects of random signals on the accuracy of micro-stepping control positioning. For studying and simulation of the effect of random noise signals on performance of the accurate position control systems, such as Hybrid Stepper Motors (HSMs), a micro-step driver and controlling unit using PID controller has been designed and constructed. Several parametric studies have been carried out including different white noise power and micro-step per revolution. Tracking problem for a HSM model has been simulated, and the experimental study for similar cases has been carried out by implementing the designed controller in real-time operation by using Real Time Windows Target Toolbox of Matlab software and Simulink. Simulation and experimental results show that random noise source changes current profile and affects the accuracy of positioning. Performance of the proposed PID controller under the implementation of random noise on phases one and two of stepper motor has been proved to be accurate enough even under disturbance load currents, on the system. Experimental and simulation results show the good performance of designed controller in tracking problem, affected by various random noise powers and motor speeds in different micro-step positions. Moreover there is an excellent agreement between experimental and simulation results.     

Phenylhydrazinium Iodide for Surface Passivation and Defects Suppression in Perovskite Solar Cells

In recent years, hybrid perovskite solar cells (HPSCs) have received considerable research attention due to their impressive photovoltaic performance and low‐temperature solution processing capability. However, there remain challenges related to defect passivation and enhancing the charge carrier dynamics of the perovskites, to further increase the power conversion efficiency of HPSCs. In this work, the use of a novel material, phenylhydrazinium iodide (PHAI), as an additive in MAPbI₃ perovskite for defect minimization and enhancement of the charge carrier dynamics of inverted HPSCs is reported. Incorporation of the PHAI in perovskite precursor solution facilitates controlled crystallization, higher carrier lifetime, as well as less recombination. In addition, PHAI additive treated HPSCs exhibit lower density of filled trap states (10¹⁰ cm^?2) in perovskite grain boundaries, higher charge carrier mobility (≈11 × 10^?4 cm² V^?1 s), and enhanced power conversion efficiency (≈18%) that corresponds to a ≈20% improvement in comparison to the pristine devices.     

Estimation of flotation rate constant and collision efficiency using regression and artificial neural networks

The effects of particle characteristics and hydrodynamic conditions on the flotation rate constant (k) and bubble–particle collision efficiency (E_c) of pyrite and chalcopyrite particles were investigated. Experimental results showed that k increases with increase of bubble surface area flux (S_b) and E_c. Artificial neural network (ANN) and multivariable linear regression procedures were used to predict both k and E_c based on the particle characteristics and hydrodynamic conditions. Multivariable linear regression resulted in R² of 0.6 and 0.93 for k and E_c, respectively. Using an ANN model, R² as high as 0.98 was achieved in modeling the E_c with regard to the available parameters. The proposed ANN model can be reliably used to determine both k and E_c parameters in froth flotation.     

PTC effect in HDPE filled with carbon blacks modified by Ni and Au metallic particles

Several types of positive temperature coefficient (PTC) composites are prepared by using Ni and Au modified carbon black. The major aim of this work was the lowering of the room temperature resistivity of the composites by enhancement of electrical conduction of the CB particles by incorporating metallic particles. Investigations showed that the metal particles fill the cavities and surface defects of the CB and thus surface free area reduces after modification. Metallic particles also change the nature of the CB particles after modification. Several types of PTC composites were prepared by using modified and unmodified CB by several loading level of filler. Measuring electrical properties of the PTC samples showed that the Ni modified CB reduce room temperature resistivity to lower than that of PTC composites prepared by unmodified CB. PTC composites prepared by Au modified CBs showed very different properties compared to PTC composites prepared by Ni modified CB. Their resistivities were quite higher than the others showing a poor compatibility between the matrix and Au modified CB. The effect of thermal annealing also investigated on the electrical properties of the prepared composites. Room temperature resistivities reduced for most of the samples while PTC intensities increased after annealing. Theoretical bases are employed to discuss the room temperature resistivity and PTC behavior of the composites before and after annealing. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 2007     

Application of temporal difference learning rules in short‐term traffic flow prediction

Studying dynamic behaviours of a transportation system requires the use of the system mathematical models as well as prediction of traffic flow in the system. Therefore, traffic flow prediction plays an important role in today's intelligent transportation systems. This article introduces a new approach to short‐term daily traffic flow prediction based on artificial neural networks. Among the family of neural networks, multi‐layer perceptron (MLP), radial basis function (RBF) neural network and wavenets have been selected as the three best candidates for performing traffic flow prediction. Moreover, back‐propagation (BP) has been adapted as the most efficient learning scheme in all the cases. It is shown that the coefficients produced by temporal signals improve the performance of the BP learning (BPL) algorithm. Temporal signals provide researchers with a new model of temporal difference BP learning algorithm (TDBPL). The capability and performance of TDBPL algorithm are examined by means of simulation in order to prove that the wavelet theory, with its multi‐resolution ability in comparison to RBF neural networks, is a suitable algorithm in traffic flow forecasting. It is also concluded that despite MLP applications, RBF neural networks do not provide negative forecasts. In addition, the local minimum problems are inevitable in MLP algorithms, while RBF neural networks and wavenet networks do not encounter them.