Impedance studies of polycrystalline tin oxide

Polycrystalline SnO₂ samples prepared in the laboratory were irradiated with 2 MeV He ions having doses of 3.62 × 10¹⁵ ion/cm². The ac small-signal electrical data acquired for the polycrystalline SnO₂ in the frequency (f) range 100 Hz ≤ f ≤ 1 MHz and temperature (T) range 26°C ≤ T ≤ 100°C revealed one semicircular relaxation in the impedance plane for the He implanted sample. However, two semicircular relaxations were obtained in the same plane for the samples without He implantation. The He implantation indicated enhancement in the donor density in the polycrystalline SnO₂ as depicted via terminal conductance (or resistance). An erratum to this article can be found at     

Highly selective determination of perchlorate by a novel potentiometric sensor based on a synthesized complex of copper

In this paper, a highly selective poly (vinyl chloride) (PVC) membrane electrode based on (1, 9-dibenzyl-1, 3, 7, 9, 11, 15-hexaaza cyclohexa decane) copper(II) perchlorate; [Cu((benzyl)₂[16]aneN₆)](ClO₄)₂; as a synthesized ionophore, for perchlorate-selective electrode is reported. The influence of membrane composition, pH and possible interfering anions were investigated on the response properties of the electrode. The sensor responds to perchlorate ion in linear range from 1.0 × 10^? 6 to 1.0 × 10^? 1 M with a slope ? 59.4 ± 0.3 mV per decade. The limit of detection of the electrode was 4.0 × 10^? 7 M ClO₄^–. Selectivity coefficients indicate a good discriminating ability towards ClO₄^– ion in comparison to other anions. The proposed sensor has a fast response time of about 7 s and can be used for at least 2 months without any considerable divergence in potential. Due to importance of analysis of perchlorate in water samples, this selective electrode was applied as potentiometric sensor in determination of perchlorate ion in real samples.     

Numerical evaluation and the effects of geometrical and operational parameters on thermal performance of the shell and double coil tube heat exchanger

Heat exchangers are extensively used in various industries. In this study, the impact of geometric and flow parameters on the performance of a shell and double helical coil heat exchanger is studied numerically. The investigated geometric parameters include external coil pitch, internal coil pitch, internal coil diameter, and coil diameter. The influences of considered geometrical parameters are analyzed on the output temperature of the hot and cold fluid, convective heat transfer coefficient, pressure drop, and average Nusselt number. Water is considered as working fluid in both shell and tube. As an innovation, double helical coils are used instead of one in the heat exchanger. To compare the obtained results accurately, in each section, the heat transfer area (coil outer surface) is kept constant in all models. The results show that the geometrical parameters of double helical coils significantly affect the heat transfer rate.     

Ion-exchange kinetics and electrical conductivity studies of polyaniline Sn(IV) tungstoarsenate; (SnO2)(WO3)(As2O5)4(C6H5NH)2·nH2O: a new semi-crystalline ‘polymeric-inorganic’ composite cation-exchange material

A new and novel electrically conducting ‘polymeric-inorganic’ composite cation-exchange material; polyaniline Sn(IV) tungstoarsenate was prepared by incorporating polyaniline into inorganic ion-exchanger material. It possessed improved ion-exchange capacity, high chemical and thermal stabilities, reproducibility and selectivity for some specific metal ions. Kinetic study of exchange for some divalent metal ions of alkaline earths and transition metals was carried out under the conditions favoring a particle diffusion-controlled ion-exchange phenomenon and some physical parameters such as self diffusion coefficient D₀, energy of activation E_a and entropy of activation ΔS* were determined. The temperature dependence of electrical conductivity of this composite material with increasing temperatures was measured by using 4-in-line-probe DC electrical conductivity measuring-technique. The conductivity values lie in the semiconductor region, i.e. in the range of 10⁻³ S cm⁻¹ that follow the Arrhenius equation. The energy of activation of electrical conduction for the composite was also calculated.     

Evaluation of suspended load transport rate using transport formulas and artificial neural network models （Case study： Chelchay Catchment）＊

Accurate estimation of sediment load or transport rate is very important to a wide range of water resources projects. This study was undertaken to determine the most appropriate model to predict suspended load in the Chelchay Watershed, northeast of Iran. In total, 59 data series were collected from four gravel bed-rivers and a sand bed river and two depth integrating suspended load samplers to evaluate nine suspended load formulas and feed forward backpropagation Artificial Neural Network (ANN) structures. Although the Chang formula with higher correlation coefficient (r = 0.69) and lower Root Mean Square Error (RMSE = 0.013) is the best suspended load predictor among the nine studied formulas, the ANN models significantly outperform traditional suspended load formulas and show their superior performance for all statistical parameters. Among different ANN structures two models including 4 inputs, 4 hidden and one output neurons, and 4 inputs, 4 and one hidden and one output neurons provide the best simulation with the RMSE values of 0.0009 and 0.001, respectively.     

Kinetic modeling of pyrolysis of three Iranian waste oils in a micro-fluidized bed

Different approaches have been used to convert the waste materials into a clean syngas or other chemicals such as methanol. Among them, pyrolysis is a good candidate to produce the synthesis gas and volatile matters for industrial and refinery applications. In this work, we studied the kinetic and chemical behavior of three Iranian waste oils through a kinetic model and an experimental study. The experiments carried out in a micro-FB reactor, which is a good option for low emissions. Results showed that the reaction temperature and reaction rate are two of the most important factors for maximum conversion level of fuel. Results also showed an optimum value for reaction rate. The modeling results validated against the experimental measurements and found to be in good agreements.     

Investigation of the Saturated Vapor Pressure of Zinc,Selenium, and Zinc Selenide

Temperature dependences of the saturated vapor pressure of elemental zinc (873–1234 K) and selenium (730–1063 K), as well as zinc selenide (673–1373 K), are investigated using the gravimetric method. Analysis of the experimental data demonstrates that these dependences can be adequately described by equations of the form logP = ?A/T+B. The saturated vapor pressures calculated for zinc from the data obtained with the developed gravimetric method are in good agreement with those available in the literature.     

A 60‐GHz on‐chip slot‐array antenna in integrated‐passive‐device technology for antenna‐in‐package applications

A new millimeter‐wave antenna structure on a low‐cost, production platform integrated passive device technology is presented. The antenna consists of a 2‐by‐1 array of slot antennas at 60 GHz. An in‐house developed on‐chip antenna measurement setup was used to characterize the fabricated antenna. The measurement results show an antenna gain of more than 5 dBi with a return loss of 18 dB at 60 GHz. The better‐than‐10‐dB impedance bandwidth of the antenna covers the 60‐GHz unlicensed band from 57 to 64 GHz. The 3‐dB beamwidths of the antenna are 105° and 76° at E‐plane and H‐plane at 60 GHz, respectively. The size of the die of the antenna is 2 mm × 4.5 mm. © 2013 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:155–160, 2014.     

Effect of Extraordinary Large Floods on at-site Flood Frequency

The peak flow of extraordinary large floods that occur during a period of systematic record is a controversial problem for flood frequency analysis (FFA) using traditional methods. The present study suggests that such floods be treated as historic flood data even though their historical period is unknown. In this paper, the extraordinary large flood peak was first identified using statistical outlier tests and normal probability plots. FFA was then applied with and without the extraordinary large floods. In this step, two goodness-of-fit tests including mean absolute relative deviation and mean squared relative deviation were used to identify the best-fit probability distributions. Next, the generalized extreme value (GEV), three-parameter lognormal (LN3), log-Pearson type III (LP3), and Wakeby (WAK) probability distributions were used to incorporate and adjust the extraordinary large floods with other systematic data. Finally, procedures with and without historical adjustment were compared for the extraordinary large floods in terms of goodness-of-fit and flood return-period quantiles. The results of this comparison indicate that historical adjustment from an operational perspective was more viable than without adjustment procedure. Furthermore, the results without adjustment were unreasonable (subject to over- and under-estimation) and produced physically unrealistic estimates that were not compatible with the study area. The proposed approach substantially improved the probability estimation of rare floods for efficient design of hydraulic structures, risk analysis, and floodplain management.     

A comparative study of GPU programming models and architectures using neural networks

Recently, General Purpose Graphical Processing Units (GP-GPUs) have been identified as an intriguing technology to accelerate numerous data-parallel algorithms. Several GPU architectures and programming models are beginning to emerge and establish their niche in the High-Performance Computing (HPC) community. New massively parallel architectures such as the Nvidia??s Fermi and AMD/ATi??s Radeon pack tremendous computing power in their large number of multiprocessors. Their performance is unleashed using one of the two GP-GPU programming models: Compute Unified Device Architecture (CUDA) and Open Computing Language (OpenCL). Both of them offer constructs and features that have direct bearing on the application runtime performance. In this paper, we compare the two GP-GPU architectures and the two programming models using a two-level character recognition network. The two-level network is developed using four different Spiking Neural Network (SNN) models, each with different ratios of computation-to-communication requirements. To compare the architectures, we have chosen the two extremes of the SNN models for implementation of the aforementioned two-level network. An architectural performance comparison of the SNN application running on Nvidia??s Fermi and AMD/ATi??s Radeon is done using the OpenCL programming model exhausting all of the optimization strategies plausible for the two architectures. To compare the programming models, we implement the two-level network on Nvidia??s Tesla C2050 based on the Fermi architecture. We present a hierarchy of implementations, where we successively add optimization techniques associated with the two programming models. We then compare the two programming models at these different levels of implementation and also present the effect of the network size (problem size) on the performance. We report significant application speed-up, as high as 1095× for the most computation intensive SNN neuron model, against a serial implementation on the Intel Core 2 Quad host. A comprehensive study presented in this paper establishes connections between programming models, architectures and applications.