Distribution of mineral species in different coal seams of Talcher coalfield and its transformation behavior at varying temperatures

International Journal of Coal Science & Technology - Mineral phase characterization and thorough understanding of its transformation behavior during combustion are imperative to know the...     

Generalized Method for Gun Propellant Formulation Design

Determination of propellant formulation by ballistic requirement is an important area of research in recent times. In this study, a theoretical method for the design of gun propellant formulation using primary data of ingredients and necessary thermochemical properties of the resultant propellant was established. The employed method is based on a mathematical model of thermochemical properties of the propellant by optimizing the heat of explosion of the propellant using the fmincon tool in MATLAB. A graphical user interface (GUI) based code was generated and developed for the formulation design of solid gun propellants. The designed code was verified by available data in the literature. Such code will be useful to the researchers working in the area of high energy materials for the design of unknown propellant compositions. Further, it can be extended to redesign the existing propellant formulation in order to enhance the ballistic performance.     

Retracted: Mechanical and thermal properties enhancement of polycarbonate nanocomposites prepared by melt compounding

The following article from the Journal of Applied Polymer Science, “Mechanical and thermal properties enhancement of polycarbonate nanocomposites prepared by melt compounding” by Sanjay K. Nayak, Smita Mohanty, and Sushanta K. Samal, published online on 7 April 2010 in Wiley Online Library (J. Appl. Polym. Sci. 2010 , 117, 2101; http://onlinelibrary.wiley.com/doi/10.1002/app.31222/full ), has been retracted by agreement between the authors, the journal's editors, and Wiley Periodicals, Inc. The retraction has been agreed due to significant overlap with respect to another article, “Effect of hydrogen bonding on the rheology of polycarbonate/organoclay nanocomposites,” by Kyung Min Lee, and Chang Dae Han, published online on 19 June 2003 in Polymer ( 2003 , 44, 4573).     

Experimental studies and empirical models for the prediction of bed expansion in gas–solid tapered fluidized beds

Studies in the expansion behaviour of tapered fluidized bed systems are important for specifying the height of the bed. Data have been obtained on the expanded heights of tapered fluidized beds and bed expansion ratios for spherical and non-spherical particles have been calculated. Based on dimensional analysis, models have been developed as a function of geometry of tapered bed, static bed height, particle diameter, density of solid and gas and superficial velocity of the fluidizing medium. The data used to derive the models cover a wide range of operating conditions, with varying fluidization velocities. Effects of static bed height, particle diameter, density, tapered angle and superficial gas velocity over minimum fluidization velocity on bed expansion ratios have been investigated experimentally. A comparison has been made between the calculated values of bed expansion ratios using proposed models and the experimental data. It has been seen that calculated values by models agree well with the experimental values. Models have also been compared with literature data of conventional bed and found its applicability at higher gas velocities with good accuracy.     

The Effect of Nanoparticle Shape on the Photocarrier Dynamics and Photovoltaic Device Performance of Poly(3‐hexylthiophene):CdSe Nanoparticle Bulk Heterojunction Solar Cells

The charge separation and transport dynamics in CdSe nanoparticle:poly(3‐hexylthiophene) (P3HT) blends are reported as a function of the shape of the CdSe‐nanoparticle electron acceptor (dot, rod, and tetrapod). For optimization of organic photovoltaic device performance it is crucial to understand the role of various nanostructures in the generation and transport of charge carriers. The sample processing conditions are carefully controlled to eliminate any processing‐related effects on the carrier generation and on device performance with the aim of keeping the conjugated polymer phase constant and only varying the shape of the inorganic nanoparticle acceptor phase. The electrodeless, flash photolysis time‐resolved microwave conductivity (FP‐TRMC) technique is used and the results are compared to the efficiency of photovoltaic devices that incorporate the same active layer. It is observed that in nanorods and tetrapods blended with P3HT, the high aspect ratios provide a pathway for the electrons to move away from the dissociation site even in the absence of an applied electric field, resulting in enhanced carrier lifetimes that correlate to increased efficiencies in devices. The processing conditions that yield optimum performance in high aspect ratio CdSe nanoparticles blended with P3HT result in poorly performing quantum dot CdSe:P3HT devices, indicating that the latter devices are inherently limited by the absence of the dimensionality that allows for efficient, prolonged charge separation at the polymer:CdSe interface.     

Artificial Neural Network Modeling for Groundwater Level Forecasting in a River Island of Eastern India

Forecasting of groundwater levels is very useful for planning integrated management of groundwater and surface water resources in a basin. In the present study, artificial neural network models have been developed for groundwater level forecasting in a river island of tropical humid region, eastern India. ANN modeling was carried out to predict groundwater levels 1 week ahead at 18 sites over the study area. The inputs to the ANN models consisted of weekly rainfall, pan evaporation, river stage, water level in the drain, pumping rate and groundwater level in the previous week, which led to 40 input nodes and 18 output nodes. Three different ANN training algorithms, viz., gradient descent with momentum and adaptive learning rate backpropagation (GDX) algorithm, Levenberg–Marquardt (LM) algorithm and Bayesian regularization (BR) algorithm were employed and their performance was evaluated. As the neural network became very large with 40 input nodes and 18 output nodes, the LM and BR algorithms took too much time to complete a single iteration. Consequently, the study area was divided into three clusters and the performance evaluation of the three ANN training algorithms was done separately for all the clusters. The performance of all the three ANN training algorithms in predicting groundwater levels over the study area was found to be almost equally good. However, the performance of the BR algorithm was found slightly superior to that of the GDX and LM algorithms. The ANN model trained with BR algorithm was further used for predicting groundwater levels 2, 3 and 4 weeks ahead in the tubewells of one cluster using the same inputs. It was found that though the accuracy of predicted groundwater levels generally decreases with an increase in the lead time, the predicted groundwater levels are reasonable for the larger lead times as well.     

High Thermal Conductivity Polymer Composites Fabrication through Conventional and 3D Printing Processes: State-of-the-Art and Future Trends

The lifespan and the performance of flexible electronic devices and components are affected by the large accumulation of heat, and this problem must be addressed by thermally conductive polymer composite films. Therefore, the need for the development of high thermal conductivity nanocomposites has a strong role in various applications. In this article, the effect of different particle reinforcements such as single and hybrid form, coated and uncoated particles, and chemically treated particles on the thermal conductivity of various polymers are reviewed and the mechanism behind the improvement of the required properties are discussed. Furthermore, the role of manufacturing processes such as injection molding, compression molding, and 3D printing techniques in the production of high thermal conductivity polymer composites is detailed. Finally, the potential for future research is discussed, which can help researchers to work on the thermal properties enhancement for polymeric materials.     

Competing Effects of Radio Frequency Fields on Carbon Nanotube/Resin Systems: Alignment versus Heating

This work shows that radio-frequency (RF) fields can simultaneously align carbon nanotubes (CNTs) dispersed in a resin and induce Joule heating to cure the resin. The timescales of alignment and curing using RF heating are numerically computed and compared at different field strengths in order to determine a temperature where alignment happens before the matrix crosslinks. Composites are experimentally fabricated at the desired target temperature and are optically analyzed and quantified; the CNT network is successfully aligned in the direction of the applied electric field. This methodology can be used to create composites where the local alignment can be varied across the sample. Composites fabricated using RF fields have higher electrical conductivity in the direction of the aligned CNTs than an oven-cured, randomly aligned sample. Also, RF-cured nanocomposites exhibit higher tensile strength and modulus in the direction of alignment compared to an oven-cured sample. Finally, it is further demonstrated how this methodology can be coupled with a direct ink writing additive manufacturing process to induce alignment in any desired direction, even orthogonal to the shear forces in the extrusion direction.