Enhanced Energy Storage of Dielectric Nanocomposites at Elevated Temperatures

Enhancing the performance of dielectric capacitors toward higher energy density and higher operating temperatures has been drawing increased interest. Therefore, in this investigation, research efforts were dedicated to the fabrication and characterization of nanocomposites in order to enhance the energy density at both room temperature and elevated temperature. The dielectric capacitors are fabricated using nanocomposites composed of BaTiO nanoparticles with polyimide (PI) matrix aiming at combining the high relative dielectric permittivity of the ceramic filler and the high breakdown strength of the polymeric matrix. Dielectric energy storage performance is assessed for nanocomposites with volume fractions ranging from 0 to 20% under operating frequency from 20 Hz to 1 MHz and temperatures ranging from 20 to 120C. It is observed that with the increase of temperature, the capacitance increased while the energy density slightly decreased but significantly higher than pure polymer samples. The highest energy density was found for BaTiO/PI nanocomposites with 20% volume fraction, 9.63 J/cm at 20C and 6.79 J/cm at 120C. Overall, testing results indicate that using nanocomposites of BaTiO/PI as a dielectric component shows promise for implementation to preserve high energy density values up to temperatures of 120C.     

The Time Dependence of the Detachment Force of Ground Micrometer-Size Particles from a Planar Substrate: Effect of Particle Rotation

The time dependence of the detachment force applied to 7 µm ground polyester particles coated with silica nanoparticles was determined by ultracentrifugation. It was found that the force required to separate the particles from the substrate increased during the first 24 hours and changed very little thereafter. Scanning electron microscopy (SEM) results suggest that the increase in adhesion is due to the particles rotating from their initial positions obtained during deposition. The role of the silica nanoparticles in determining the time dependence of the detachment force is discussed in terms of the JKR and Fuller–Tabor models.     

The electrochemical behavior of gold in ammoniacal solutions at 75 °C

The dissolution behavior of gold in ammoniacal solutions at 75 °C has been investigated. The variables investigated include concentration of lixiviants, pH of the solution, and various oxidants. The anodic dissolution of gold was studied in the absence of oxidants using the potentiodynamic polarization technique. The cathodic reaction of oxidants were studied on a platinum electrode. Tafel polarization tests were also performed to determine the dissolution rate. The overall dissolution of gold in ammoniacal solutions was examined using a mixed potential theory. A comparative study on the role of cupric ammine, oxygen, hydrogen peroxide, and sodium hypochlorite as oxidants for the dissolution of gold in ammoniacal solutions has been carried out.     

Top gate ZnO-Al2O3 thin film transistors fabricated using a chemical bath deposition technique

ZnO thin films were prepared by a simple chemical bath deposition technique using an inorganic solution mixture of ZnCl₂ and NH₃ on glass substrates and then were used as the active material in thin film transistors (TFTs). The TFTs were fabricated in a top gate coplanar electrode structure with high-k Al₂O₃ as the gate insulator and Al as the source, drain and gate electrodes. The TFTs were annealed in air at 500 ℃ for 1 h. The TFTs with a 50 μm channel length exhibited a high field-effect mobility of 0.45 cm²/(V·s) and a low threshold voltage of 1.8 V. The sub-threshold swing and drain current ON-OFF ratio were found to be 0.6 V/dec and 10⁶, respectively.     

FT-NIR spectroscopy coupled with multivariate analysis for detection of starch adulteration in turmeric powder

This paper investigates the feasibility of Fourier transform near-infrared (FT-NIR) spectroscopy, a fast and easy method based on chemometric methods to detect corn starch illegally added to turmeric powder. In this work, the pure turmeric powders were blended with corn starch to generate different concentrations (1–30%)(w/w) of starch-adulterated turmeric samples. The reflectance spectra of total of 224 samples were taken by FT-NIR spectroscopy. The exploratory data analysis was done by principal component analysis (PCA). The starch related peaks were selected by variable importance in projection (VIP) method and were explored by examination of original reflectance spectra, 1^st derivative spectra, PCA loadings and β coefficients plot of the partial least square regression (PLSR) model. The coefficient of determination (R²) and root-mean-square error of partial least square regression (PLSR) models were found to be 0.91–0.99 and 0.23–1.3%, respectively, depending on the pre-processing techniques of spectral data. The figure of merit (FOM) of the model was found with the help of net analyte signal (NAS) theory.     

Short and Medium Range Forecast of Soil Moisture for the Different Climatic Regions of India Using Temporal Networks

Spatio-temporal evolution of soil moisture is a complex process and controlled by several factors including hydro-meteorological forcings. This study borrows a recently developed concept of temporal networks to capture the time-varying association between hydro-meteorological forcings and spatio-temporal evolution of soil moisture. Climate change and dynamic terrestrial environment cause slow but continuous change in the characteristics of hydro-meteorological forcings leading to variation in spatio-temporal distribution of soil moisture. Keeping this in the focus of the study, temporal networks based time-varying modelling framework is adapted for one-month to one-season (three-months) in advance prediction of monthly soil moisture for entire Indian mainland. Results indicate that the association among the hydro-meteorological forcings varies with both space and time. With the increase in prediction lead-time, the strength of association with the variables, such as pressure, wind and temperature, decreases and that with the variables like leaf area index remains informative. Among different seasons, the model shows superior performance for the monsoon and post monsoon periods. Next, the soil moisture based extremes are assessed by utilizing two deficit indices and two wetness indices. The model performance is highly satisfactory but varies over space and seasons, with a marginally better performance for the wetness indices. Overall, given the vast spatial extent of the Indian mainland, the proposed model performs robustly for almost all the climatic regions and may be promising for other parts of the world as well.

     

Development of an Improved Pollution Source Identification Model Using Numerical and ANN Based Simulation-Optimization Model

The identification of unknown pollution sources is an important and challenging task for the engineers working on pollution management of a groundwater aquifer. The locations and transient magnitude of unknown contaminant sources can be identified using inverse optimization technique. In this approach, the absolute difference between the simulated and the observed contaminant concentration at the observation locations of the aquifer is minimized by using an optimization algorithm. The simulated concentrations is calculated using the aquifer simulation model. As such, there is a need to incorporate the aquifer simulation model with the optimization model. Thus the performance of the model is highly related to the aquifer simulation model. The incorporation of the sophisticated numerical simulation model will give better performance, but the model will be computationally expensive. On the other hand, the model will be computationally less expensive if an approximate simulation model is used in place of the numerical simulation model. However, in this case, the predictive performance of the model will decline. For achieving efficiency in both computational time as well as in predicting the performance, this study presents a new genetic algorithms based simulation-optimization method incorporating both the numerical and the approximate simulation models. The efficiency and field applicability of the model is demonstrated using illustrative study areas. The performance evaluation of the model shows that the proposed model has the potential for real-world field applications.     

Estimation of theaflavin content in black tea using electronic tongue

Biochemical components like theaflavins (TF) play very important role in the quality of finished CTC (cut, torn, and curled) variety of tea. TF are known to provide characteristic astringency to the taste of finished CTC tea. The quality indicators like brightness, briskness, strength, color and overall quality of tea liquor are also due to the amount of TF present. A positive correlation is normally observed between the amount of TF and the quality scores of finished tea. Biochemical tests that yield the percentage of TF are often time consuming, require meticulous effort of sample preparation, storage and measurement. This paper proposes an alternative approach of quality evaluation of CTC tea by predicting the amount of TF that may be present in a given tea sample, using a voltammetric electronic tongue.