Routing Protocols in Delay Tolerant Networks: Comparative and Empirical Analysis

Wireless Personal Communications - A Delay Tolerant Network (DTN) is a network capable of handling long delays and data loss efficiently in various challenged scenarios and environments. Owing to...     

Signature of Meyer-Neldel compensation rule in iso-conversional crystallization under the influence of γ-ray irradiation

In the current article, the influence of three different doses of gamma-rays on the thermally assisted crystal growth in a novel ternary chalcogenide glassy Se₇₈Te₂₀Sn₂ semiconductor has been reported in terms of the re-crystallization. The iso-conversional kinetic approach is used in the present study. The variation of crystal growth rate with temperature obeys the Arrhenius relation for all the doses of gamma-rays irradiation. Further analysis confirms that pre-factor K₀ of crystal growth rate and the corresponding energy ΔE involved in thermally governed crystallization follows Meyer-Neldel compensation rule (MNCR). Further, we have observed a linear correlation i.e., Further Meyer-Neldel compensation rule (FMNCR) between Meyer-Neldel pre-factor K₀₀ and Meyer-Neldel energy kT₀. The observed correlations between these significant parameters (MNCR between K₀ & ΔE and FMNCR between K₀₀ & kT₀) may open a new gateway for revealing the external effects on crystal growth rate during the iso-conversional analysis of crystallization kinetics.     

Silver monoliths and their applications in catalytic reduction of 4-NP to 4-AP and sensing against As3+

Journal of Porous Materials - Silver monoliths using non-ionic surfactant Triton X-102 as reducing agent with and without additives such as Dextran and Ludox (SiNPs) were synthesized by modified...     

Recent development and challenges of multifunctional structural supercapacitors for automotive industries

In the last decades, fuel scarcity and increasing pollution level pave the way for an extensive interest in alternatives to petroleum‐based fuels such as biodiesel, solar cells, lithium ion batteries, and supercapacitors. Among them, structural supercapacitors have been considered as promising candidates for automotive industries in present time. Herein, the use of carbon fiber‐based supercapacitors in automotive applications is reviewed. Carbon fiber is an excellent candidate for vehicle body applications, and its composites could be widely used in the development of supercapacitors that could provide both structural and energy storage functions. Different surface modification processes of the carbon fiber electrode to enhance the electrochemical as well as mechanical performances are discussed. The advantages of the glass fiber separator and its comparison with other types of dielectric media have been incorporated. The synthesis procedures of the multifunctional solid polymer electrolyte and its significance have been also elaborated. The fabrication process, component selection, limitations, and future challenges of these supercapacitors are briefly assimilated in this review. Copyright © 2017 John Wiley & Sons, Ltd.     

An intelligent wavelet transform-based framework to detect subsurface fires with NOAA–AVHRR images

Subsurface coal fires (in this article, termed as hotspots), responsible for atmospheric pollution, human fatalities and perilous land subsidence, pose a big threat to major coal-producing countries in the world. The majority of the research performed to date has focused on providing hotspot allocation information for a specific region of interest and most has explored quite expensive high-resolution Landsat Thematic Mapper (TM) satellite images for the same. This article aims to investigate the applicability of a wavelet transform-based model to detect subsurface fires (hotspots) with freely available National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA–AVHRR) images and find out the possibility of extracting novel hotspot features by applying a wavelet transform-based analysis technique. The proposed feature vector consists of wavelet variance coefficients (WVCs) obtained from scale-by-scale decomposition of the AVHRR image variance and builds up a strong base for designing an accurate classification system. Furthermore, the support vector machine (SVM), an efficient machine learning tool, is applied to the proposed feature vector in order to develop a classification model. The demonstrated results successfully prove the effectiveness of the proposed framework as the classified images show a good correspondence with records of subsurface fires mapped by the Bharat Coking Coal Limited (BCCL), India. The effectiveness of the SVM method is also evaluated in comparison with the classical neural network-based approach.     

Vinyl carbazole (VC) functionalized mesoporous silica polymer nanocomposites (SBA/VC) for the antibacterial activity studies

Vinyl carbazole (VC) functionalized ordered mesoporous silica polymer nanocomposites (SBA/VC) were synthesized by in situ radical polymerization of monomers inside the mesoporous framework and characterized for the antibacterial activity studies against gram positive and gram negative bacteria. Powder X-ray diffraction and N₂ adsorption isotherms of SBA/VC nanocomposites showed the presence of mesoporous nature. The antimicrobial activity results showed increasing trend with the increase in the concentration of vinyl carbazole (VC) and the maximum antibacterial activity was achieved with SBA/VC₆₄ nanocomposites.