Nanomaterials based biofuel cells: A review

Biofuel cells (BFCs) are the devices made to transform the chemical energy of organic matter to electrical energy utilizing metabolic reactions occurring in microorganisms during degradation of organic contaminants. In spite of having many applications such as waste water treatment, biosensors and portable uses of BFCs, promoting the uses of BFCs is very challenging because of short life-time and low-power density. Most of the BFC developed till date is only capable to fulfill energy needs of biomedical short-term implanted devices. Use of materials with nano dimensions in the construction of BFCs has been studied extensively and reported as a worthwhile strategy to increase its efficiency. Usually, it is difficult to achieve efficient electron transfer on planar electrode from biocatalyst due to its non-specific orientational the interface. Nonmaterials provide close wiring for the electron transfer between biocatalyst and electrode. Use of various nanomaterials is the most effective way to decrease the gap between active sites (electron producing area)deep inside the enzyme or proteins and the electrodes to achieve better electron transfer. Also, various nanomaterials are utilized to improve the membrane materials for better electron barrier. Many carbon nanostructures, conducting polymers, metal and metal oxides are promising nonmaterials to enhance the current output from BFC. This review highlights recent progress registered in the development of various nanomaterials for construction of electrode and membranes of biofuel cells for better efficiency. It also emphasized the utilization of different metallic nanomaterials, inorganic nanomaterials, conducting polymer-based nanomaterials and carbon-based nanomaterials such as graphene, fullerenes, and carbon nanotubes.     

Natural disasters management using social internet of things

Multimedia Tools and Applications - Natural disasters are very unexpected in human life. The best prevention from such natural disasters is an early warning system which gives a good period to take...     

Metamaterials: Advancement and Futuristic Design Approach

Nowadays, one amongst the Semantic Web (SW), called the Social Media (SM), renders a platform for quick and also faultless access to information. Millions of individuals considered it as the chief news source. With the escalating popularity of SM and web-based forums, the distribution of Fake News (FN) has grown enormously in current times, which become a chief threat to an assortment of sectors and agencies. Numerous solutions and techniques were commenced for identifying FN quickly enough to end the spreading of FN. Nevertheless, they all failed on account of a lack of classification accuracy and high training time. To trounce these issues, this paper proposes a FastText Modified Embeddings from Language Models (FT-MELMo) based efficient FN identification system (FNIS). Specifically, the proposed system learns the semantic representation of data by utilizing FT-MELMo embedding. FT and MELMo utilize character n-grams and Bi-GRU, correspondingly to maximize the classification accuracies and also minimize the training time. Lastly, a higher-dimensional embedding feature illustration is inputted to the AMSGradAdamNC based Convolutional Neural Network (A²CNN) classifier, which classifies the news as FN or real news effectively. The suggested A²CNN classifier achieved a classification accuracy of 96.54 percent with a low FDR of 4.32 percent. When compared to existing classifiers, the suggested A²CNN exhibits a low FPP and FNR. On the basis of the performance comparison, the suggested FNIS is found to be extremely accurate, robust, and faster than other classifiers.

     

Performance analysis of torus optical interconnect with data center traffic

Two-dimensional torus network nodes are typically interconnected using XY routing algorithm for transmitting a packet from a source node to a destination node. In XY routing, if all the paths are used efficiently, the throughput and latency can be improved. In this paper, to utilize all the paths efficiently, we propose a novel binary optical routing algorithm (BORA) to improve the throughput and latency. The throughput is calculated according to the injection rate and number of packets received at the destination. The XY routing algorithm and proposed BORA are implemented using objective modular network testbed in C++ simulation software and the results are analyzed and compared. In this paper, the simulation results show that the network latency reduces to 50% while using the proposed algorithm; moreover, the throughput is also improved.     

Terminal and broadcast reliability analysis of direct 2-D symmetric torus network

The Journal of Supercomputing - Reliability analysis is one of the crucial issues for any scalable optical interconnection network. Torus is a highly scalable optical interconnect for data centre...     

Design and Application of 2,2‐Dibromodimedone as Organic Brominating Reagent for Asymmetric Bromination of 1,3‐Dicarbonyl Compounds and Ketones Catalysed by Chiral Amino Acids

A green and ecofriendly enantioselective α‐bromination of carbonyl and 1,3‐dicarbonyl compounds is reported involving the synthesis of a novel organic brominating source. The organic brominating reagent can be recovered after each cycle, rebrominated and reused. The reaction is catalysed by chiral amino acids and completed within a short reaction time with good enantioselectivity and exclusive formation of only α‐monobrominated carbonyl compounds.     

Preparation and characterization of tetracycline‐loaded interpenetrating polymer networks of carboxymethyl cellulose and poly(acrylic acid): water sorption and drug release study

Tetracycline (TC)‐loaded ionic interpenetrating polymer networks (IPNs) of carboxymethyl cellulose (CMC) and crosslinked poly(acrylic acid) (PAA) were prepared and characterized by infrared spectral analysis, differential scanning calorimetry and scanning electron microscopy techniques. The prepared IPNs were evaluated for in vitro blood compatibility by clot formation and hemolysis methods and their water imbibitions capacity was determined. Fractional release dynamics of tetracycline was also investigated from loaded IPNs of CMC and PAA. The entrapped drug was examined for antibacterial activity and structural integrity and effects of various parameters such as percentage loading of the drug, chemical composition of the carrier IPN, pH and temperature of the release medium were investigated on the release profiles of TC. The drug was also released in different simulated biological fluids. Copyright © 2005 Society of Chemical Industry     

Experimental study of the restoring force mechanism in the self-centering beam (SCB)

In the past, several self-centering (SC) seismic systems have been developed. However, examples of selfcentering systems used in practice are limited due to unusual field construction practices, high initial cost premiums and deformation incompatibility with the gravity framing. A self centering beam moment frame (SCB-MF) has been developed that mitigates several of these issues while adding to the advantages of a typical SC system. The self-centering beam (SCB) is a shop-fabricated, self-contained structural component that when implemented in a moment resisting frame can bring a building back to plumb after an earthquake. This paper describes the SCB concepts and experimental program on five SCB specimens at two-third scale relative to a prototype building. Experimental results are presented including the global force-deformation behavior. The SCBs are shown to undergo 5%–6% story drift without any observable damage to the SCB body and columns. Strength equations developed for the SCB predict the moment capacity well, with a mean difference of 6% between experimental and predicted capacities. The behavior of the restoring force mechanism is described. The limit states that cause a loss in system's restoring force which lead to a decrease in the selfcentering capacity of the SCB-MF, are presented.