Temperature dependence of copper diffusion in different thickness amorphous tungsten/tungsten nitride layer

The amorphous W/WN films with various thickness (10, 30 and 40 nm) and excellent thermal stability were successfully prepared on SiO₂/Si substrate with evaporation and reactive evaporation method. The W/WN bilayer has technological importance because of its low resistivity, high melting point, and good diffusion barrier properties between Cu and Si. The thermal stability was evaluated by X-ray diffractometer (XRD) and Scanning Electron Microscope (SEM). In annealing process, the amorphous W/WN barrier crystallized and this phenomenon is supposed to be the start of Cu atoms diffusion through W/WN barrier into Si. With occurrence of the high-resistive Cu₃Si phase, the W/WN loses its function as a diffusion barrier. The primary mode of Cu diffusion is the diffusion through grain boundaries that form during heat treatments. The amorphous structure with optimum thickness is the key factor to achieve a superior diffusion barrier characteristic. The results show that the failure temperature increased by increasing the W/WN film thickness from 10 to 30 nm but it did not change by increasing the W/WN film thickness from 30 to 40 nm. It is found that the 10 and 40 nm W/WN films are good diffusion barriers at least up to 800°C while the 30 nm W/WN film shows superior properties as a diffusion barrier, but loses its function as a diffusion barrier at about 900°C (that is 100°C higher than for 10 and 40 nm W/WN films).     

Development of vapor generation combined with potentiometric detection for determination of sulfite in beverages

This paper describes a simple, sensitive and cost effective method developed for determination of sulfite in beverages. The procedure is based on chemical vapor generation of hydrogen sulfide, collection of the vapor in an alkaline solution, and then potentiometric determination of the sulfide using a commercial sulfide selective electrode. The influential parameters on analytical performance such as concentration of reagents, flow rate of carrier gas, reaction temperature and gas collection time are evaluated and discussed. In the optimum conditions, the method was linear in the range of 2–25 μg mL^?1 of sulfite with a detection limit of 0.7 μg mL^?1. The relative standard deviation of the method was 1.2 % for five replicate experiments at 10 μg mL^?1 concentration level. The method was successfully applied for determination of sulfite in different beverages with the relative recoveries between 92.2 and 96.2 %. The proposed method can be applied for quality control and consumer safety in food and beverages industrial plants.     

Challenges in atherosclerotic plaque characterization with intravascular ultrasound (IVUS): from data collection to classification

In vivo plaque characterization is an important research field in interventional cardiology. We will study the realistic challenges to this goal by deploying 40 MHz single-element, mechanically rotating transducers. The intrinsic variability among the transducers' spectral parameters as well as tissue signals will be demonstrated. Subsequently, we will show that global data normalization is not suited for data calibration, due to the aforementioned variations as well as the stringent characteristics of spectral features. We will describe the sensitivity of an existing feature extraction algorithm based on eight spectral signatures (integrated backscatter coefficient, slope, midband-fit (MBF), intercept, and maximum and minimum powers and their relative frequencies) to a number of factors, such as the window size and order of the autoregressive (AR) model. It will be further demonstrated that the variations in the transducer's spectral parameters (i.e., center frequency and bandwidth) cause inconsistencies among extracted features. In this paper, two fundamental questions are addressed: 1) what is the best reliable way to extract the most informative features? and 2) which classification algorithm is the most appropriate for this problem? We will present a full-spectrum analysis as an alternative to the eight-feature approach. For the first time, different classification algorithms, such as k-nearest neighbors (k-NN) and linear Fisher, will be employed and their performances quantified. Finally, we will explore the reliability of the training dataset and the complexity of the recognition algorithm and illustrate that these two aspects can highly impact the accuracy of the end result, which has not been considered until now.     

A Non-Stationary Reconnaissance Drought Index (NRDI) for Drought Monitoring in a Changing Climate

Traditionally, drought indices are calculated under stationary condition, the assumption that is not true in a changing environment. Under non-stationary conditions, it is assumed the probability distribution parameters vary linearly/non-linearly with time or other covariates. In this study, using the GAMLSS algorithm, a time-varying location parameter of lognormal distribution fitted to the initial values (α₀) of the traditional Reconnaissance Drought Index (RDI) was developed to establish a new index called the Non-Stationary RDI (NRDI), simplifying drought monitoring under non-stationarity. The fifteen meteorological stations having the longest records (1951–2014) in Iran were chose to evaluate the NRDI performances for drought monitoring. Trend analysis of the α₀ series at multiple time windows was tested by using the Mann-Kendall statistics. Although all stations detected decreasing trend in the α₀ series, eight of them were significant at the 5% probability level. The results showed that the time-dependent relationship is adequate to model the location parameter at the stations with the significant temporal trend. There were remarkable differences between the NRDI and the RDI, especially for the time windows larger than 6 months, implying monitoring droughts using the NRDI under non-stationarity. The study suggests using the NRDI where the significant time trend appears in the initial values of RDI due to changing climate.     

Effective recovery of pure aluminum from waste composite laminates by sub- and super-critical water

An innovative and environment-friendly sub- and super-critical water processes have been proposed for successful recovery of aluminum from composite laminated wastes. The effects of reaction temperature (190-400 °C) and residence time (10 and 30 min) were mainly investigated to recover aluminum in an intact form from two commercially available packaging materials, which were made by dry and extrusion lamination methods (samples A and B), respectively. After the reaction, carbon and oxygen contents in the solid-phase were measured by CHNS/O analyzer and the aqueous-phase was subjected for TOC, ICP and ethylene glycol analyses. The results suggested that, the dry laminated ‘sample A’ exhibited a gradual decrease in carbon and oxygen level with increase in reaction temperatures indicating separation of polymers from aluminum. At 10 min, temperature ranges from 340 to 400 °C and at 30 min, from 310 to 340 °C were optimum for the aluminum recovery, showing the lowest percentage of carbon and oxygen in the solid-phase. The aluminum was oxidized above the optimum temperatures. On the other hand, the extrusion laminated ‘sample B’ showed resistance to separation of polymers and the carbon and oxygen level decreased drastically only near the critical point of water. At 30 min, 370-400 °C led to an efficient recovery of aluminum. The results revealed that, temperature, reaction time and the lamination method were the key parameters that determined the recovery of aluminum. This study provides a framework for the effective material cycling of huge wastes of laminated composites using sub- and super-critical water technology.     

Linear Optical Response of Silicon Nanotubes Under Axial Magnetic Field

We investigated the optical properties of silicon nanotubes (SiNTs) in the low energy region, E < 0.5 eV, and middle energy region, 1.8 eV < E < 2 eV. The dependence of optical matrix elements and linear susceptibility on radius and magnetic field, in terms of one-dimensional (1-d) wavevector and subband index, is calculated using the tight-binding approximation. It is found that, on increasing the nanotube diameter, the low-energy peaks show red-shift and their intensities are decreased. Also, we found that in the middle energy region all tubes have two distinct peaks, where the energy position of the second peak is approximately constant and independent of the nanotube diameter. Comparing the band structure of these tubes in different magnetic fields, several differences are clearly seen, such as splitting of degenerate bands, creation of additional band-edge states, and bandgap modification. It is found that applying the magnetic field leads to a phase transition in zigzag silicon hexagonal nanotubes (Si h-NTs), unlike in zigzag silicon gear-like nanotubes (Si g-NTs), which remain semiconducting in any magnetic field. We found that the axial magnetic field has two effects on the linear susceptibility spectrum, namely broadening and splitting. The axial magnetic field leads to the creation of a peak with energy less than 0.2 eV in metallic Si h-NTs, whereas in the absence of a magnetic field such a transition is not allowed.     

An Ultra-Low-Power 9T SRAM Cell Based on Threshold Voltage Techniques

This paper presents a new nine-transistor (9T) SRAM cell operating in the subthreshold region. In the proposed 9T SRAM cell, a suitable read operation is provided by suppressing the drain-induced barrier lowering effect and controlling the body–source voltage dynamically. Proper usage of low-threshold voltage (L-\(V_{\mathrm{t}}\)) transistors in the proposed design helps to reduce the read access time and enhance the reliability in the subthreshold region. In the proposed cell, a common bit-line is used in the read and write operations. This design leads to a larger write margin without using extra circuits. The simulation results at 90 nm CMOS technology demonstrate a qualified performance of the proposed SRAM cell in terms of power dissipation, power–delay product, write margin, read access time and sensitivity to process, voltage and temperature variations as compared to the other most efficient low-voltage SRAM cells previously presented in the literature.     

Mechanical strength and setting times estimation of hydroxyapatite cement by using neural network

In this study, the mechanical strength, the initial and the final setting times in hydroxyapatite (HA) bone cement are estimated by designing a back-propagation neural network (BPNN) which has 2 inputs and 3 outputs. Firstly, some experimental samples have been prepared to train the BPNN to get it to estimate the output parameters. Then BPNN is tested using some experimental samples that have not been used in the training stage. To prepare the training and testing data sets, some experiments were performed. In these experiments, the β-tricalcium phosphate (β-TCP), the calcium carbonate and the dicalcium phosphate are used to prepare the powder part of the HA bone cement. Also the liquid part of the cement consists of the NaH₂PO₄⋅2H₂O solution with different concentrations. The effects of liquid phase concentration and the liquid/powder ratio of the cement, as input parameters, have been investigated on the setting times and the mechanical strength of the cement, as output parameters. The comparison of the predicted values and the experimental data indicates that the developed model has an acceptable performance to estimation of the setting times and the mechanical strength in HA bone cement. Also three neural networks with 2-inputs and 1-output was developed, similar to above method, and were compared with 3-outputs model. It is found that the prediction accuracy of 3-outputs model is better than those of other 1-output models.