Synthesis,characterization and photovoltaic properties of phase pure Cu<Subscript>2</Subscript>SnSe<Subscript>3</Subscript> nanostructures using molecular precursors

Phase pure Cu₂SnSe₃ (CTSe) nanostructures have been synthesized using metal selenolates, [Me₂Sn{SeC₅H₃(Me-3)N}₂] (molecular structure of [Me₂Sn{SeC₅H₃(Me-3)N}₂] has been established by single crystal X-ray analysis) and [Cu{SeC₅H₃(Me-3)N}]₄ as molecular precursors in oleylamine (OLA) and tri-n-octylphosphine oxide (TOPO). The effect of temperature on final composition of nanostructures has also been investigated. Powder X-ray diffraction, Raman and X-ray photoelectron spectroscopy (XPS) were used to investigate the crystal structure, phase purity and homogeneity of the nanostructures while the electron microscopy revealed the morphology of the nanostructures. Superconducting quantum interference device (SQUID) measurements of CTSe nanostructures showed weak magnetic ordering at low temperature (below 50 K) attributed to the presence of some defect centres in the sample. The CTSe nanostructure possess an optical band gap of 1.7 eV deduced from diffuse reflectance spectroscopy (DRS) and showed photo response which makes them promising candidate for alternative low cost photon absorber material.     

Development of Fuzzy Enabled Coverage Hole Detection Algorithm in Wireless Sensor Networks

Wireless Personal Communications - The coverage hole problem in Wireless Sensor Networks (WSNs) is one of the most important problem. The data has to be communicated through the base station that...     

Reliability feedback–aided low‐complexity detection in uplink massive MIMO systems

Massive multiple‐input multiple‐output (MIMO) plays a crucial role in realizing the demand for higher data rates and improved quality of service for 5G and beyond communication systems. Reliable detection of transmitted information bits from all the users is one of the challenging tasks for practical implementation of massive‐MIMO systems. The conventional linear detectors such as zero forcing (ZF) and minimum mean square error (MMSE) achieve near‐optimal bit error rate (BER) performance. However, ZF and MMSE require large dimensional matrix inversion which induces high computational complexity for symbol detection in such systems. This motivates for devising alternate low‐complexity near‐optimal detection algorithms for uplink massive‐MIMO systems. In this work, we propose an ordered sequential detection algorithm that exploits the concept of reliability feedback for achieving near‐optimal performance in uplink massive‐MIMO systems. In the proposed algorithm, symbol corresponding to each user is detected in an ordered sequence by canceling the interference from all the other users, followed by reliability feedback‐based decision. Incorporation of the sequence ordering and the reliability feedback‐based decision enhances the interference cancellation, which reduces the error propagation in sequential detection, and thus, improves the BER performance. Simulation results show that the proposed algorithm significantly outperforms recently reported massive‐MIMO detection techniques in terms of BER performance. In addition, the computational complexity of the proposed algorithm is substantially lower than that of the existing algorithms for the same BER. This indicates that the proposed algorithm exhibits a desirable trade‐off between the complexity and the performance for massive‐MIMO systems.     

Performance analysis of image steganography using wavelet transform for safe and secured transaction

Multimedia Tools and Applications - Internet applications are increased and growing at efficient way. By this technological growth, data communication in the internet in secured way has got a...     

Comparative investigation on using cryogenic machining in CNC milling of Ti-6Al-4V titanium alloy

Ti-6Al-4V titanium alloy is one of the most important materials in industry, 80% of which is used in aerospace industry. Titanium alloys are also notoriously difficult-to-machine materials owing to their unique material properties imposing a major bottleneck in manufacturing systems. Cryogenic cooling has been acknowledged as an alternative technique in machining to improve the machinability of different materials. Although milling is considered to be the major machining operation for the manufacture of titanium components in aerospace industries, studies in cryogenic machining of titanium alloys are predominantly concentrated on turning operations. To address this gap, this article provides an investigation on the viability of cryogenic cooling in CNC end-milling of aerospace-grade Ti-6Al-4V alloy using liquid nitrogen in comparison with traditional machining environments. A series of machining experiments were conducted and surface roughness, tool life, power consumption, and specific machining energy were investigated for cryogenic milling as opposed to conventional dry and flood cooling. Analysis revealed that cryogenic machining using liquid nitrogen has the potential to significantly improve the machinability of Ti-6Al-4V alloy in CNC end-milling using solid carbide cutting tools and result in a paradigm shift in machining of titanium products. The analysis demonstrated that cryogenic cooling has resulted in almost three times increased tool life and the surface roughness was reduced by 40% in comparison with flood cooling.     

Effect of silk nano‐disc dispersion on mechanical,thermal, and barrier properties of poly(lactic acid) based bionanocomposites

Exacerbated environmental concerns about petroleum‐based plastics provide the impetus to foster sustainable poly(lactic acid) (PLA) based food packaging. Nonetheless, PLA has its foibilities such as its brittleness, higher gas permeability, and slow crystallization. With the intent to mitigate the above shortcomings, we report a maiden effort for the fabrication of PLA/crystalline silk nano‐discs (CSNs) based bionanocomposites by melt‐extrusion for high temperature engineering and food packaging applications. Acid hydrolyzed silk fibroin from muga silk (Antheraea assama) yields CSNs, a crystalline hydrophobic discotic nanofiller with diameter of ～50 nm and thickness ～3 nm. At optimum loadings of 1 wt % uniform dispersed CSNs with percolated network structures covering the entire matrix can be seen. Due to enhanced crystal nucleation density, water vapor, and oxygen permeability reduced by ～30% and ～70%, respectively. Enhancement in toughness, percentage elongation, and tensile strength up to ～65%, ～40%, and ～10%, respectively, is obtained. Onset of thermal decomposition for the PLA/CSN improved ～10 °C, confirming the role of CSN in enhancing melt stability. Accordingly, this investigation renders a novel non‐invasive approach for increasing the crystallinity with improvement in thermomechanical and barrier properties which make this bionanocomposite, a promising candidate for food packaging applications. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018 , 135, 46671.     

An adaptive modeling of petroleum emulsion formation and stability by a heuristic multiobjective artificial neural network-genetic algorithm

In this work, experimental and statistical modeling and optimization of process parameters for maximizing the o/w emulsion stability was carried out using the multiobjective artificial neural network-genetic algorithm (ANN-GA) coupled with response surface methodology. The independent model constrains were oil concentration (10–50% v/v), surfactant concentration (2–10% v/v), stirring intensity (2000–6000 rpm), stirring time (5–20 min), and pH (2–12). The responses were turbidity (τ) and emulsion stability index (ESI₂₄). This fact that there is a reasonably good agreement between the experimental data and the predicted values was shown by the modeling results. The optimized conditions predicted by hybrid ANN-GA model to maximize ESI₂₄ ( = 94.71) with 4.8% error were: oil concentration 50% v/v, surfactant concentration 5.571% v/v, stirring speed 6000 rpm, stirring time 5.97 min, and pH 12. The accuracy of the model is confirmed by the comparison between the predicted and experimental data. The proposed hybrid ANN-GA model was found to be useful for the modeling and optimization of process parameters for emulsion stability analysis and the other emulsification process.     

Outage Probability and Average Channel Capacity of Amplify-and-Forward in Conventional Cooperative Communication Networks over Rayleigh Fading Channels

Performance of conventional cooperative communication networks using amplify-and-forward relay over independent and identically distributed Rayleigh fading channels is investigated in this paper. An expression for probability density function by using moment generating function is obtained. Further closed-form expressions of the outage probability and the average channel capacity are derived. The derived analytical results are compared with Monte Carlo simulations to confirm correctness of the derived expressions.     

Truss topology optimization with static and dynamic constraints using modified subpopulation teaching–learning-based optimization

In this study, an improved version of the teaching–learning-based optimization (TLBO) algorithm is proposed for truss topology optimization (TTO), with static and dynamic constraints on planar and space trusses. The basic TLBO algorithm is improved to enhance its exploration and exploitation abilities by considering various factors such as the number of teachers, adaptive teaching, tutorial learning and self-motivated learning. The TTO problems are considered with multiple load conditions and subjected to constraints for natural frequencies, element stresses, nodal displacements, Euler buckling criteria and kinematic stability conditions. TTO is achieved with the removal of superfluous elements and nodes from the ground structure, and results in a mass saving. In this method, difficulties arise owing to singular solution and unnecessary analysis; therefore, the finite element model is reformed to resolve these issues. A single-stage optimization approach is used, in which size and topology optimization are considered simultaneously. The results obtained are compared with the best solutions obtained by the algorithm. The results reveal that the modified subpopulation teaching–learning-based optimization (MS-TLBO) algorithm is more effective than other state-of-the-art algorithms.     

Single-phase fluid flow and mixing in microchannels

In the last decade there has been an exponential increase in microfluidic applications due to high surface-to-volume ratios and compactness of microscale devices, which makes them attractive alternatives to conventional systems. The continuing growing trends of microfluidic highlights the importance to understand the mechanism and fundamental differences involved in fluid flow and mixing at microscale. In the present article, the experimental research efforts in the area of microscale single-phase fluid flow and issues associated with investigations at microscale flow have been summarized. The experimental data are being analyzed in terms of friction factor, laminar-to-turbulent transition, and the effect of roughness on fluid hydrodynamics for different cross-sectional geometries. The differences in the uncharacteristic behavior of the transport mechanisms through microchannels due to compressibility and rarefaction, relative roughness, property variations and viscous dissipation effects are discussed. Finally, progress on recent development of micromixers has been reported for different micromixer types and designs. The micromixers have been quantified based on their operating ranges (in terms of characteristic dimensionless numbers such as Reynolds number Re, Peclet number Pe, and Strouhal number St) and mixing characteristics.