Dynamical configurations and bistability of helical nanostructures under external torque

We study the motion of a ferromagnetic helical nanostructure under the action of a rotating magnetic field. A variety of dynamical configurations were observed that depended strongly on the direction of magnetization and the geometrical parameters, which were also confirmed by a theoretical model, based on the dynamics of a rigid body under Stokes flow. Although motion at low Reynolds numbers is typically deterministic, under certain experimental conditions the nanostructures showed a surprising bistable behavior, such that the dynamics switched randomly between two configurations, possibly induced by thermal fluctuations. The experimental observations and the theoretical results presented in this paper are general enough to be applicable to any system of ellipsoidal symmetry under external force or torque.

Combining heterogeneous classifiers for relational databases

Practical usage of machine learning is gaining strategic importance in enterprises looking for business intelligence. However, most enterprise data is distributed in multiple relational databases with expert-designed schema. Using traditional single-table machine learning techniques over such data not only incur a computational penalty for converting to a flat form (mega-join), even the human-specified semantic information present in the relations is lost. In this paper, we present a practical, two-phase hierarchical meta-classification algorithm for relational databases with a semantic divide and conquer approach. We propose a recursive, prediction aggregation technique over heterogeneous classifiers applied on individual database tables. The proposed algorithm was evaluated on three diverse datasets. namely TPCH, PKDD and UCI benchmarks and showed considerable reduction in classification time without any loss of prediction accuracy. (C) 2012 Elsevier Ltd. All rights reserved.

Microwave-assisted, surfactant-free synthesis of air-stable copper nanostructures and their SERS study

A simple, rapid, and surfactant-free synthesis of crystalline copper nanostructures has been carried out through microwave irradiation of a solution of copper acetylacetonate in benzyl alcohol. The structures are found to be stable against oxidation in ambient air for several months. High-resolution electron microscopy (SEM and TEM) reveals that the copper samples comprise nanospheres measuring about 150 nm in diameter, each made of copper nanocrystals similar to 7 nm in extension. The nanocrystals are densely packed into spherical aggregates, the driving force being minimization of surface area and surface energy, and are thus immune to oxidation in ambient air. Such aggregates can also be adherently supported on SiO2 and Al2O3 when these substrates are immersed in the irradiated solution. The air-stable copper nanostructures exhibit surface enhanced Raman scattering, as evidenced by the detection of 4-mercaptobenzoic acid at 10(-6) M concentrations.

Morphology Controlled Growth of Meso-Porous Co3O4 Nanostructures and Study of Their Electrochemical Capacitive Behavior

The morphology of nanocrystalline Co3O4 synthesized through microwave irradiation of a solution of a cobalt complex is found to depend reproducibly on the conditions of synthesis and, in particular, on the composition of the solvent used. Despite the rapidity of the process, oriented aggregation occurs under certain conditions, depending on solvent composition. Annealing the oriented samples leads to microstructures with significant porosity, rendering the material suitable as electrodes for electrochemical capacitors. Electrochemical analysis of the oxide samples was carried out in 0.1M Na2SO4 electrolyte vs. Ag/AgCl electrode. A stable specific capacitance of 221 F/g was measured for a meso-porous sample displaying oriented aggregation. Stability of these oxide materials were checked for longer charge-discharge cycling. (C) 2012 The Electrochemical Society. DOI: 10.1149/2.002210jes] All rights reserved.

Automatic compilation of MATLAB programs for synergistic execution on heterogeneous processors

MATLAB is an array language, initially popular for rapid prototyping, but is now being increasingly used to develop production code for numerical and scientific applications. Typical MATLAB programs have abundant data parallelism. These programs also have control flow dominated scalar regions that have an impact on the program's execution time. Today's computer systems have tremendous computing power in the form of traditional CPU cores and throughput oriented accelerators such as graphics processing units(GPUs). Thus, an approach that maps the control flow dominated regions to the CPU and the data parallel regions to the GPU can significantly improve program performance. In this paper, we present the design and implementation of MEGHA, a compiler that automatically compiles MATLAB programs to enable synergistic execution on heterogeneous processors. Our solution is fully automated and does not require programmer input for identifying data parallel regions. We propose a set of compiler optimizations tailored for MATLAB. Our compiler identifies data parallel regions of the program and composes them into kernels. The problem of combining statements into kernels is formulated as a constrained graph clustering problem. Heuristics are presented to map identified kernels to either the CPU or GPU so that kernel execution on the CPU and the GPU happens synergistically and the amount of data transfer needed is minimized. In order to ensure required data movement for dependencies across basic blocks, we propose a data flow analysis and edge splitting strategy. Thus our compiler automatically handles composition of kernels, mapping of kernels to CPU and GPU, scheduling and insertion of required data transfer. The proposed compiler was implemented and experimental evaluation using a set of MATLAB benchmarks shows that our approach achieves a geometric mean speedup of 19.8X for data parallel benchmarks over native execution of MATLAB.

Substrate impact on the growth of InN nanostructures by droplet epitaxy

The substrate effect on InN nanostructures grown by droplet epitaxy has been studied. InN nanostructures were fabricated on Si(111), silicon nitride/Si(111), AlN/Si(111) and Ge(100) substrates by droplet epitaxy using an RF plasma nitrogen source. The morphologies of InN nanostructures were investigated by field emission scanning electron microscopy (FESEM). The chemical bonding configurations of InN nanostructures were examined by x-ray photoelectron spectroscopy (XPS). Photoluminescence spectrum slightly blue shifted compared to the bulk InN, indicating a strong Burstein-Moss effect due to the presence of high electron concentration in the InN dots.

Growth and characterization of micro and nanostructures of lead telluride (PbTe) by thermal evaporation method

Lead telluride micro and nanostructures have been grown on silicon and glass substrates by a simple thermal evaporation of PbTe in high vacuum of 3 x 10(-5) mbar. Growth was carried out for two different distances between the evaporation source and the substrates. Synthesized products consist of nanorods and micro towers for 2.4 cm and 3.4 cm of distance between the evaporation source and the substrates respectively. X-ray diffraction and transmission electron microscopy studies confirmed crystalline nature of the nanorods and micro towers. Nanorods were grown by vapor solid mechanism. Each micro tower consists of nano platelets and is capped with spherical catalyst particle at their end, suggesting that the growth proceeds via vapor-liquid-solid (VLS) mechanism. EDS spectrum recorded on the tip of the micro tower has shown the presence of Pb and Te confirming the self catalytic VLS growth of the micro towers. These results open up novel synthesis methods for PbTe nano and microstructures for various applications.

Bimetallic core-shell nanocomposites using weak reducing agent and their transformation to alloy nanostructures

An in situ seeding growth methodology towards the preparation of core-shell nanoparticles composed of noble metals has been developed by employing trimethylamine borane (TMAB) as the reducing agent. Being a weak reducing agent, TMAB is able to distinguish the smallest reduction potential window of any two metals which renders selective reduction of metal ions thus affording a core-shell architecture of the nanoparticles. A dramatic effect of solvent was noted during the reduction of Ag+ ions: an immediate reduction took place at room temperature when dry THF was used as solvent however, usage of wet THF (THF used directly from the bottle) brings out the reduction only at reflux conditions. In the case of Au and Pd nanoparticles, preparation was found to be independent of the quality of solvent used. Au nanoparticles are realized at room temperature whereas reflux conditions are required in the case of Pd nanoparticles. This difference in behavior of the monometallic nanoparticles was successfully exploited to construct different noble metal nanoparticles with core-shell architectures such as Au@Ag, Ag@Au, and Ag@Pd. Transformation of these core-shell nanoparticles to their thermodynamically stable alloy counterparts is also demonstrated under very mild conditions reported to date.

Spectroscopic studies of In2O3 nanostructures; photovoltaic demonstration of In2O3/p-Si heterojunction

The thermal oxidation process of the indium nitride (InN) nanorods (NRs) was studied. The SEM studies reveal that the cracked and burst mechanism for the formation of indium oxide (In2O3) nanostructures by oxidizing the InN NRs at higher temperatures. XRD results confirm the bcc crystal structure of the as prepared In2O3 nanostructures. Strong and broad photoluminescence spectrum located at the green to red region with maximum intensity at 566 nm along with a weak ultraviolet emission at 338 nm were observed due to oxygen vacancy levels and free excitonic transitions, respectively. The valence band onset energy of 2.1 eV was observed from the XPS valence band spectrum, clearly justifies the alignment of Fermi level to the donor level created due to the presence of oxygen vacancies which were observed in the PL spectrum. The elemental ratio In:O in as prepared In2O3 was found to be 42:58 which is in close agreement with the stoichiometric value of 40:60. A downward shift was observed in the Raman peak positions due to a possible phonon confinement effect in the nanoparticles formed in bursting mechanism. Such single junction devices exhibit promising photovoltaic performance with fill factor and conversion efficiency of 21% and 0.2%, respectively, under concentrated AM1.5 illumination.

Synthesis and characterization of porous flowerlike alpha-Fe2O3 nanostructures for supercapacitor application

Porous flower-like alpha-Fe2O3 nanostructures synthesized by an ethylene glycol mediated self-assembly process are crystalline and porous with BET surface area of 64.6 m(2) g(-1). The discharge capacitance is 127 F g(-1) when the electrodes are cycled in 0.5 M Na2SO3 at a current density of 1 A g(-1). Capacitance retention after 1000 cycles is about 80% of the initial capacitance. The high discharge capacitance and its retention are attributed to high surface area and porosity of the iron oxide. As the iron oxides are inexpensive, the nano alpha-Fe2O3 is expected to be of potential use for supercapacitor application.

Runtime dependence computation and execution of loops on heterogeneous systems

GPUs have been used for parallel execution of DOALL loops. However, loops with indirect array references can potentially cause cross iteration dependences which are hard to detect using existing compilation techniques. Applications with such loops cannot easily use the GPU and hence do not benefit from the tremendous compute capabilities of GPUs. In this paper, we present an algorithm to compute at runtime the cross iteration dependences in such loops. The algorithm uses both the CPU and the GPU to compute the dependences. Specifically, it effectively uses the compute capabilities of the GPU to quickly collect the memory accesses performed by the iterations by executing the slice functions generated for the indirect array accesses. Using the dependence information, the loop iterations are levelized such that each level contains independent iterations which can be executed in parallel. Another interesting aspect of the proposed solution is that it pipelines the dependence computation of the future level with the actual computation of the current level to effectively utilize the resources available in the GPU. We use NVIDIA Tesla C2070 to evaluate our implementation using benchmarks from Polybench suite and some synthetic benchmarks. Our experiments show that the proposed technique can achieve an average speedup of 6.4x on loops with a reasonable number of cross iteration dependences.

Heterogeneous locational optimisation using a generalised Voronoi partition

In this paper a generalisation of the Voronoi partition is used for locational optimisation of facilities having different service capabilities and limited range or reach. The facilities can be stationary, such as base stations in a cellular network, hospitals, schools, etc., or mobile units, such as multiple unmanned aerial vehicles, automated guided vehicles, etc., carrying sensors, or mobile units carrying relief personnel and materials. An objective function for optimal deployment of the facilities is formulated, and its critical points are determined. The locally optimal deployment is shown to be a generalised centroidal Voronoi configuration in which the facilities are located at the centroids of the corresponding generalised Voronoi cells. The problem is formulated for more general mobile facilities, and formal results on the stability, convergence and spatial distribution of the proposed control laws responsible for the motion of the agents carrying facilities, under some constraints on the agents' speed and limit on the sensor range, are provided. The theoretical results are supported with illustrative simulation results.

Ethylenediamine assisted synthesis of wurtzite zinc sulphide nanosheets and porous zinc oxide nanostructures: near white light photoluminescence emission and photocatalytic activity under visible light irradiation

Porous fungus-like ZnO nanostructures have been synthesized by simple thermal annealing of the hydrothermally synthesized sheet-like ZnS(en)(0.5) complex precursor in air at 600 degrees C. Structural and morphological changes occurring during ZnS(en)(0.5) -> ZnS -> ZnO transformations have been observed closely by annealing the as-synthesized precursor at 100-600 degrees C. Wurtzite ZnS nanosheets and ZnS-ZnO composites are obtained at temperatures of 400 degrees C and 500 degrees C, respectively. Thermal decomposition and oxidation of the ZnS(en) 0.5 nanosheets have been confirmed by differential scanning calorimetry and thermo-gravimetric analysis. The visible light driven photocatalytic degradation of methylene blue dye has been demonstrated in the synthesized samples. ZnS-ZnO composite shows the highest dye degradation efficiency of 74% due to the formation of surface complex as well as higher visible light absorption as a result of band-gap narrowing effect. The porous ZnO nanostructures show efficient visible photoluminescence (PL) emission with a colour coordinate of (0.29, 0.35), which is close to that of white light (0.33, 0.33). The efficient visible PL emission as well as visible light driven photocatalytic activity of the materials synthesized in the present work might be very attractive for their applications in future optoelectronic devices, including in white light emitting devices.

An electron rich porous extended framework as a heterogeneous catalyst for Diels-Alder reactions

An electron rich porous metal-organic framework (MOF) has been synthesized, which acts as an effective heterogeneous catalyst for Diels-Alder reactions through encapsulation of the reactants in confined nano-channels of the framework.

The dual role of Zn-acid medium for one-step rapid synthesis of M@rGO (M = Au, Pt, Pd and Ag) hybrid nanostructures at room temperature

We report a new protocol for the synthesis of M@rGO (M = Au, Pt, Pd, Ag and rGO = reduced graphene oxide) hybrid nanostructures at room temperature in Zn-acid medium. The roles of Zn-acid are to reduce the GO by generated hydrogen and the deposition of metal nanoparticles on rGO by galvanic replacement reaction between Zn and Mn+.