Graphene-based in-plane micro-supercapacitors with high power and energy densities

Micro-supercapacitors are important on-chip micro-power sources for miniaturized electronic devices. Although the performance of micro-supercapacitors has been significantly advanced by fabricating nanostructured materials, developing thin-film manufacture technologies and device architectures, their power or energy densities remain far from those of electrolytic capacitors or lithium thin-film batteries. Here we demonstrate graphene-based in-plane interdigital micro-supercapacitors on arbitrary substrates. The resulting micro-supercapacitors deliver an area capacitance of 80.7 μF cm(−2) and a stack capacitance of 17.9 F cm(−3). Further, they show a power density of 495 W cm(−3) that is higher than electrolytic capacitors, and an energy density of 2.5 mWh cm(−3) that is comparable to lithium thin-film batteries, in association with superior cycling stability. Such microdevices allow for operations at ultrahigh rate up to 1,000 V s(−1), three orders of magnitude higher than that of conventional supercapacitors. Micro-supercapacitors with an in-plane geometry have great promise for numerous miniaturized or flexible electronic applications

Biolistic transformation of Saccharomyces cerevisiae with β-glucosidase gene from Cellulomonas biazotea

A β-glucosidase genomic DNA from Cellulomonas biazotea NIAB 442 was isolated and coated onto tungsten microprojectiles for direct transformation of the gene into Saccharomyces cerevisiae. Transformation of β-glucosidase into S. cerevisae conferred the ability to hydrolyse esculin and cellobiose, indicated that the gene is expressed in the bombarded yeast. Key Words: Biolistic transformation, β-glucosidase, Cellulomonas biazotea, Saccharomyces cerevisiae. African Journal of Biotechnology Vol.3(1) 2004: 112-11

DistB-Condo: Distributed Blockchain-based IoT-SDN Model for Smart Condominium

Condominium network refers to intra-organization networks, where smart buildings or apartments are connected and share resources over the network. Secured communication platform or channel has been highlighted as a key requirement for a reliable condominium which can be ensured by the utilization of the advanced techniques and platforms like Software-Defined Network (SDN), Network Function Virtualization (NFV) and Blockchain (BC). These technologies provide a robust, and secured platform to meet all kinds of challenges, such as safety, confidentiality, flexibility, efficiency, and availability. This work suggests a distributed, scalable IoT-SDN with Blockchain-based NFV framework for a smart condominium (DistB-Condo) that can act as an efficient secured platform for a small community. Moreover, the Blockchain-based IoT-SDN with NFV framework provides the combined benefits of leading technologies. It also presents an optimized Cluster Head Selection (CHS) algorithm for selecting a Cluster Head (CH) among the clusters that efficiently saves energy. Besides, a decentralized and secured Blockchain approach has been introduced that allows more prominent security and privacy to the desired condominium network. Our proposed approach has also the ability to detect attacks in an IoT environment. Eventually, this article evaluates the performance of the proposed architecture using different parameters (e.g., throughput, packet arrival rate, and response time). The proposed approach outperforms the existing OF-Based SDN. DistB-Condo has better throughput on average, and the bandwidth (Mbps) much higher than the OF-Based SDN approach in the presence of attacks. Also, the proposed model has an average response time of 5% less than the core model

Objective No-Reference Stereoscopic Image Quality Prediction Based on 2D Image Features and Relative Disparity

Stereoscopic images are widely used to enhance the viewing experience of three-dimensional (3D) imaging and communication system. In this paper, we propose an image feature and disparity dependent quality evaluation metric, which incorporates human visible system characteristics. We believe perceived distortions and disparity of any stereoscopic image are strongly dependent on local features, such as edge (i.e., nonplane areas of an image) and nonedge (i.e., plane areas of an image) areas within the image. Therefore, a no-reference perceptual quality assessment method is developed for JPEG coded stereoscopic images based on segmented local features of distortions and disparity. Local feature information such as edge and non-edge area based relative disparity estimation, as well as the blockiness and the edge distortion within the block of images are evaluated in this method. Subjective stereo image database is used for evaluation of the metric. The subjective experiment results indicate that our metric has sufficient prediction performance