Experimental investigation on heat transfer behavior of the novel ternary eutectic PCM embedded with MWCNT for thermal energy storage systems

Journal of Thermal Analysis and Calorimetry - In this paper, the variation of thermophysical properties such as the thermal conductivity, thermal energy storage capacity, viscosity, and phase...     

Alpha-phenyl-N-tert-butylnitrone-type derivatives bound to beta-cyclodextrins: syntheses, thermokinetics of self-inclusion and application to superoxide spin-trapping

alpha-Phenyl-N-tert-butylnitrone (PBN) derivatives bound to beta-cyclodextrin derivatives have been synthesized. Inclusion of the PBN group into the beta-cyclodextrin moiety is host- and temperature-dependent. In the case of the nitrone linked to permethylated cyclodextrin (Me3CD-PBN), the thermokinetic parameters are in favour of a slow chemical exchange between a tight and a loose complex. In contrast, 2,6-di-O-Me-beta-cyclodextrin-grafted PBN (Me2CD-PBN) exists either in a fast exchange or as a strongly self-associated complex. The covalent cyclodextrin-PBN compounds have been used to trap carbon and oxygen-centred free radicals. The self-associated forms of the beta-CD-spin-traps are compatible with effective spin-trapping, affording spin-adducts with enhanced EPR signal intensities relative to noncovalent CD-nitrone systems or the nitrone alone. This kind of cyclodextrin-bound nitrone is the first type of covalent supramolecular spin-trap and should open new possibilities for the study of biological free radicals in vivo.     

Numerical evaluation of zirconium reinforced aluminium matrix composites for sustainable environment

Due to the momentous advantages of composite materials, recent years many studies focused on reinforcing different new materials to the existing ones to improve their conventional strength and life time within the concern of application status. In the row, reinforcements on Al6061 become a fancy topic among researchers due to its wide applications including automobiles, yachts, electrical fittings and so on. This study continues this innovation by reinforcing three different reinforcement materials including zirconia (ZrO₂), zirconia + aluminium oxide (ZrO₂ +Al₂O₃) and fused zirconia aluminum (40FZA). These three reinforcing materials are included with the proposition of varying particle reinforcements as 5, 10 and 15%. The testing specimens were experimented to explore its mechanical, wear and corrosion behavior. Further the experimental results are given as inputs to the numerical analysis, PROMETHEE. By combining the experimental and numerical methodologies the reliability of the results were improved. However, from this study it can be evident that inclusion of 15% particle reinforcement of zirconia fused alumina in Al6061 provides greater strength, toughness, high resistance to wear and corrosion on both experimental and numerical analysis. There is ample room that this proposed material inclusion be a better option for the reinforcement of Al6061 among available alternatives for sustainable development.

     

Development of high quality carrier materials for field delivery of key microorganisms used as bio-fertilisers and bio-pesticides

High quality inoculants used as bio-fertilisers and bio-pesticides depend on having high concentrations of the microorganism(s), long shelf-life and a formulation appropriate for field delivery. To maintain the microorganisms in a viable state, commercially available carrier materials are typically based on milled peat, clays, rice, bran, seeds, or other complex organic matrices. To manufacture a high quality microbial product, it is essential that the carrier material is pre-packaged and pre-sterilised. This allows for non-competitive multiplication and maintenance of the microorganisms in a nutrient rich environment. This paper reports on the efficacy and problems inherent in the sterilisation of complex carbon-based carrier materials such as peat. Resident microbial survivors of gamma irradiation doses in excess of 50 kGy, commonly Gram positive spore-formers such as Bacillus or actinomycetes were consistently observed.     

Purification and Characterization of Agarase from Marine Bacteria <Emphasis Type="Italic">Acinetobacter</Emphasis> sp. PS12B and Its Use for Preparing Bioactive Hydrolysate from Agarophyte Red Seaweed <Emphasis Type="Italic">Gracilaria verrucosa</Emphasis>

Acinetobacter strain PS12B was isolated from marine sediment and was found to be a good candidate to degrade agar and produce agarase enzyme. The extracellular agarase enzyme from strain PS12B was purified by ammonium sulfate precipitation followed by DEAE-cellulose ion-exchange chromatography. The specific activity of the crude enzyme which was 1.52 U increased to 45.76 U, after two-stage purification, with an enzyme yield of 9.76%. Purified enzyme had a molecular mass of 24 kDa. The optimum pH and temperature for activity of purified agarase were found to be 8.0 and 40 °C, respectively. The K_m and V_max values for agarase were 4.69 mg/ml and 0.5 μmol/min, respectively. Treatment with EDTA reduced the agarase activity by 58% at 5 mM concentration. The enzyme activity was stimulated by the presence of Fe²⁺, Mn²⁺, and Ca2⁺ ions while reducing reagents (β-mercaptoethanol and dithiothreitol, DTT) enhanced its activity by 30–40%. The purified agarase exhibited tolerance to both detergents and organic solvents. Major hydrolysis products of agar were DP4 and also a mixture of longer oligosaccharides DP6 and DP7. The enzyme hydrolysed seaweed (Gracilaria verrucosa) exhibited strong antioxidant activity in vitro. Successful hydrolysis of seaweed indicates the potential use of the enzyme to produce seaweed hydrolysate having health benefits as well as the industrial application like the production of biofuels.     

An Efficient DenseNet-Based Deep Learning Model for Malware Detection

Recently, there has been a huge rise in malware growth, which creates a significant security threat to organizations and individuals. Despite the incessant efforts of cybersecurity research to defend against malware threats, malware developers discover new ways to evade these defense techniques. Traditional static and dynamic analysis methods are ineffective in identifying new malware and pose high overhead in terms of memory and time. Typical machine learning approaches that train a classifier based on handcrafted features are also not sufficiently potent against these evasive techniques and require more efforts due to feature-engineering. Recent malware detectors indicate performance degradation due to class imbalance in malware datasets. To resolve these challenges, this work adopts a visualization-based method, where malware binaries are depicted as two-dimensional images and classified by a deep learning model. We propose an efficient malware detection system based on deep learning. The system uses a reweighted class-balanced loss function in the final classification layer of the DenseNet model to achieve significant performance improvements in classifying malware by handling imbalanced data issues. Comprehensive experiments performed on four benchmark malware datasets show that the proposed approach can detect new malware samples with higher accuracy (98.23% for the Malimg dataset, 98.46% for the BIG 2015 dataset, 98.21% for the MaleVis dataset, and 89.48% for the unseen Malicia dataset) and reduced false-positive rates when compared with conventional malware mitigation techniques while maintaining low computational time. The proposed malware detection solution is also reliable and effective against obfuscation attacks.