Reduction of common-source inductance in FET/HEMT structuresutilizing wave-propagation effects

In this paper, a novel high-frequency/high-power field-effect-transistor structure is presented to reduce gain degradation caused by common-source inductance. In this structure, the reduction in common-source inductance is achieved without the need for using very thin substrates or very complicated fabrication technology, such as vias under each source finger. Using detailed transmission-line modeling, it is shown that a significant reduction in common-source inductance and improvement in RF performance can be achieved even for moderately high values of source grounding via inductance. The new structure allows simpler fabrication technology and is expected to be particularly useful to reduce the cost and improve the performance of high-power microwave and millimeter-wave devices and circuits     

Design and analysis of reflectivity for Mylar-coated solar dish

Energy is the key driver of the economy but energy crisis is a problem for the present-day source of energy. The concentrated solar power systems make use of different reflector configurations to convert the solar energy into high-temperature heat energy. Parabolic dish system is considered as the best suitable system for achieving high temperature and so it is opted for the study. Here, a solar dish is designed with an open diameter of 1.8?m (which is 2.5?m² in area), and reflective film is being used to reflect the solar irradiance and concentrated it in the focal point. A receiver of 1?mm thickness and 10?cm diameter is used at the receiver end to measure the temperature of the focal point. The setup has made to attain a maximum temperature of 940°C and average temperature of 850°C in the focal point at clear sky in a sunny day.     

First and second law analysis of solar operated combined Rankine and ejector refrigeration cycle

A combined Rankine and ejector refrigeration cycle is proposed for the production of power and refrigeration output using duratherm 600 oil as the heat transfer fluid. Thermodynamic analysis has been done to observe the effect of parameters on the performance of the combined cycle. The effect of various parameters asthe turbine inlet pressure, evaporator temperature, condenser temperature, extraction ratio and direct normal radiation per unit area on the performance of the cycle have significant effects on the net power output, refrigeration output, first law efficiency and second law efficiency. It is also observed that the maximum irreversibility occurs in central receiver as 52.5% followed by 25% in the heliostat, 5.3% in the heat recovery vapor generator, 2.6% in the ejector, and 1.6% in the turbine and around 1.1% in the other components of the cycle. The second law efficiency of the solar operated combined Rankine and ejector refrigeration cycle is 11.90% which is much lower than its first law efficiency of 14.81%.     

A comparison of statistical relational learning and graph neural networks for aggregate graph queries

Statistical relational learning (SRL) and graph neural networks (GNNs) are two powerful approaches for learning and inference over graphs. Typically, they are evaluated in terms of simple metrics such as accuracy over individual node labels. Complex aggregate graph queries (AGQ) involving multiple nodes, edges, and labels are common in the graph mining community and are used to estimate important network properties such as social cohesion and influence. While graph mining algorithms support AGQs, they typically do not take into account uncertainty, or when they do, make simplifying assumptions and do not build full probabilistic models. In this paper, we examine the performance of SRL and GNNs on AGQs over graphs with partially observed node labels. We show that, not surprisingly, inferring the unobserved node labels as a first step and then evaluating the queries on the fully observed graph can lead to sub-optimal estimates, and that a better approach is to compute these queries as an expectation under the joint distribution. We propose a sampling framework to tractably compute the expected values of AGQs. Motivated by the analysis of subgroup cohesion in social networks, we propose a suite of AGQs that estimate the community structure in graphs. In our empirical evaluation, we show that by estimating these queries as an expectation, SRL-based approaches yield up to a 50-fold reduction in average error when compared to existing GNN-based approaches.

     

NEURAL-NETWORK-AIDED DESIGN OF AUTOMOBILE EXHAUST CATALYSTS

A priori design of catalysts is not yet possible. Such task would demand unavailable scientific knowledge of the correlations among synthesis parameters and resulting solid state and surface structures, on the one hand, and among those atomic-level structural details and their catalytic functions, on the other hand. To avoid testing every possible combination, therefore, the applied chemist or chemical engineer must identify empirical correlations underlying the existing experimental data base.

The ability of artificial neural networks to identify complex correlations and to predict the result of experiments has recently generated considerable interest in various areas of science and engineering. In this paper, neural networks are used to identify composition-performance relationships in automobile exhaust catalysts.

This work employs an artificial neural network technique to do a sensitivity analysis of the conversions of pollutant gases as a function of the catalyst composition and the operating conditions. This approach converges on the optimum catalyst composition and operating condition in order to produce specified conversions of carbon monoxide, hydrocarbons and nitrogen oxides, to carbon dioxide, water and di-nitrogen respectively.     

Surface-enhanced Raman scattering nanosensors for <Emphasis Type="Italic">in vivo</Emphasis> detection of nucleic acid targets in a large animal model

Although nanotechnology has led to important advances in in vitro diagnostics, the development of nanosensors for in vivo detection remains very challenging. Here, we demonstrated the proof-of-principle of in vivo detection of nucleic acid targets using a promising type of surface-enhanced Raman scattering (SERS) nanosensor implanted in the skin of a large animal model (pig). The in vivo nanosensor used in this study involves the “inverse molecular sentinel” detection scheme using plasmonics-active nanostars, which have tunable absorption bands in the near infrared region of the “tissue optical window”, rendering them efficient as an optical sensing platform for in vivo optical detection. Ex vivo measurements were also performed using human skin grafts to demonstrate the detection of SERS nanosensors through tissue. In this study, a new core–shell nanorattle probe with Raman reporters trapped between the core and shell was utilized as an internal standard system for self-calibration. These results illustrate the usefulness and translational potential of the SERS nanosensor for in vivo biosensing.

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Elemental Analogues of Graphene: Silicene,Germanene, Stanene,and Phosphorene

The fascinating electronic and optoelectronic properties of free‐standing graphene has led to the exploration of alternative two‐dimensional materials that can be easily integrated with current generation of electronic technologies. In contrast to 2D oxide and dichalcogenides, elemental 2D analogues of graphene, which include monolayer silicon (silicene), are fast emerging as promising alternatives, with predictions of high degree of integration with existing technologies. This article reviews this emerging class of 2D elemental materials – silicene, germanene, stanene, and phosphorene – with emphasis on fundamental properties and synthesis techniques. The need for further investigations to establish controlled synthesis techniques and the viability of such elemental 2D materials is highlighted. Future prospects harnessing the ability to manipulate the electronic structure of these materials for nano‐ and opto‐electronic applications are identified.     

Thermal decomposition of doped calcium hydroxide for chemical energy storage

Thermal decomposition of Ca(OH)₂ with and without additives has been experimentally investigated for its application as a thermochemical energy storage system. The homogeneous reaction model gives a satisfactory fit for the kinetic data on pure and Ni(OH)₂---, Zn(OH)₂--- and Al(OH)₃---doped Ca(OH)₂ and the order of reaction is 0.76 in all cases except for the Al(OH)₃-doped sample for which the decomposition is zero order. These additives are shown not only to enhance the reaction rate but also to reduce the decomposition temperature significantly. Some models for solid decomposition reactions, and possible mechanisms in the decomposition of solids containing additives, are also discussed.     

Nanotwin formation and its physical properties and effect on reliability of copper interconnects

Ultra-fine grained copper with a large amount of nano-scale twin boundaries has high mechanical strength and maintains normal electrical conductivity. The combination of these properties may lead to promising applications in future Si microelectronic technology, especially as interconnect material for air-gap and free-standing copper technologies. Based on first principles calculations of total energy and in-situ stress measurements, high stress followed by stress relaxation during the Cu film deposition seems to have contributed to nanotwin formation. Nanoindentation studies have shown a larger hardness for copper with a higher nanotwin density. The effect of Cu nanotwin boundaries on grain growth was investigated by scanning electron microscopy (SEM), electron backscatter diffraction (EBSD) and transmission electron microscopy (TEM). The presence of a high density of nanotwin boundaries may improve the reliability of Cu interconnects.     

Optimization of TiO<Subscript>2</Subscript>/MWCNT composites for efficient dye sensitized solar cells

This paper deals with the effects of introducing multiwall carbon nanotubes (MWCNTs) into photoanodes of dye sensitized solar cells (DSSCs). Mesoporous titanium dioxide (TiO₂) nanoparticles were synthesized using sol–gel technique. TiO₂/MWCNT composites were prepared by adding functionalized MWCNTs to TiO₂ nanoparticles using two different surfactants (α-terpineol and Triton X-100). Nanoparticles and composites were characterized using Dynamic Light Scattering spectrophotometer, Raman spectrometer, X-ray diffractometer, field emission scanning electron microscope, Brunauer–Emmett–Teller surface area analyzer and UV–Vis spectrophotometer. FESEM depicted that particles were spherical in shape and their size decreased due to addition of MWCNTs. This was attributed to the decrease in the crystallite size which in turn confirmed by XRD. UV–Vis absorption spectra showed the better absorbance for the visible range of light, as the content of MWCNT is increased. From the Tauc plot optical band gap was calculated and noted that it declined gradually with the content of MWCNTs. BET surface area increased drastically which was attributed to the formation of more number of pores in the nanocomposites as visualized from FESEM. UV–Vis spectra of dye desorbed from the photoanode revealed that the dye adsorption increased as a function of MWCNT wt%. I–V studies were carried out under the illumination of 100 mW/cm² simulated sunlight. Photoanodes prepared by both the methods showed better performance compared to pristine TiO₂ photoanode, because of high conducting path and high surface area provided by MWCNTs. Photoanodes with 0.19 wt% MWCNTs in them were able to achieve maximum efficiency of 3.54 and 3.86% for method A and B respectively.