Degenerate Quantum Gases in Microgravity

Clouds of ultra-cold atoms and especially Bose–Einstein condensates (BEC) provide a source for coherent matter-waves in numerous earth bound experiments. Analogous to optical interferometry, matter-wave interferometers can be used for precision measurements allowing for a sensitivity orders of magnitude above their optical counterparts. However, in some respects the presence of gravitational forces in the lab limits experimental possibilities. In this article, we report about a compact and robust experiment generating Bose–Einstein condensates in the drop tower facility in Bremen, Germany. We also present the progress of building the succeeding experiment in which a two species atom interferometer will be implemented to test the weak equivalence principle with quantum matter.     

Silica Functionalized Magnetic Nickel Ferrite Nanoparticles as an Efficient Recyclable Catalyst for S-Arylation in Aqueous Medium

Silica coated NiFe₂O₄ NPs has been synthesized. It has been characterized by XRD, TEM, SEM-EDX and FT-IR. The catalytic activity of the Silica coated NiFe₂O₄ NPs has been tested for the S-arylation reaction in water with high yield. In this reaction water was used as the green solvent. The effects of solvents, reaction time and catalyst amount for the reaction was reported. This catalyst showed excellent catalytic activity and recyclability. The Silica coated NiFe₂O₄ NPs catalyst could be easily recovered by filtration and reused more than five times without appreciable loss of its initial activity.     

A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems

In a large network of computers or wireless sensors, each of the components (henceforth, peers) has some data about the global state of the system. Much of the system's functionality such as message routing, information retrieval and load sharing relies on modeling the global state. We refer to the outcome of the function (e.g., the load experienced by each peer) as the emph{model} of the system. Since the state of the system is constantly changing, it is necessary to keep the models up-to-date. Computing global data mining models e.g. decision trees, $k$-means clustering in large distributed systems may be very costly due to the scale of the system and due to communication cost, which may be high. The cost further increases in a dynamic scenario when the data changes rapidly. In this paper we describe a two step approach for dealing with these costs. First, we describe a highly efficient emph{local} algorithm which can be used to monitor a wide class of data mining models. Then, we use this algorithm as a feedback loop for the monitoring of complex functions of the data such as its $k$-means clustering. The theoretical claims are corroborated with a thorough experimental analysis.     

Collective Mining of Bayesian Networks from Distributed Heterogeneous Data

We present a collective approach to learning a Bayesian network from distributed heterogeneous data. In this approach, we first learn a local Bayesian network at each site using the local data. Then each site identifies the observations that are most likely to be evidence of coupling between local and non-local variables and transmits a subset of these observations to a central site. Another Bayesian network is learnt at the central site using the data transmitted from the local site. The local and central Bayesian networks are combined to obtain a collective Bayesian network, which models the entire data. Experimental results and theoretical justification that demonstrate the feasibility of our approach are presented.16 November 2001     

On-board Vehicle Data Stream Monitoring Using MineFleet and Fast Resource Constrained Monitoring of Correlation Matrices

This paper considers the problem of monitoring vehicle data streams in a resource-constrained environment. It particularly focuses on a monitoring task that requires frequent computation of correlation matrices using lightweight on-board computing devices. It motivates this problem in the context of the MineFleet Real-Time system and offers a randomized algorithm for fast monitoring of correlation (FMC), inner product, and Euclidean distance matrices among others. Unlike the existing approaches that compute all the entries of these matrices from a data set, the proposed technique works using a divide-and-conquer approach. This paper presents a probabilistic test for quickly detecting whether or not a subset of coefficients contains a significant one with a magnitude greater than a user given threshold. This test is used for quickly identifying the portions of the space that contain significant coefficients. The proposed algorithm is particularly suitable for monitoring correlation and related matrices computed from continuous data streams.     

Graphene supported bimetallic G–Co–Pt nanohybrid catalyst for enhanced and cost effective hydrogen generation

A highly active and stable bimetallic nano-hybrid catalyst Graphene–Cobalt–Platinum (G–Co–Pt) is proposed for the enhanced and cost effective generation of hydrogen from Sodium Borohydride. Three different nano-hybrid catalysts namely Graphene–Cobalt (G–Co), Graphene–Platinum (G–Pt) and Graphene–Cobalt–Platinum (G–Co–Pt) are synthesized, characterized using XRD, FTIR, SEM, HRTEM, EDAX and Cyclic voltammetry (CV) analysis and tested for hydrogen generation. The activity and stability of the catalysts are analyzed by estimating the turnover frequency (TOF), the electrochemically active surface area (ECSA), the percentage decay of current density over ten cycles of CV and the decay in the rate of hydrogen generation with the age of catalyst. Among the three catalysts G–Co–Pt exhibits the highest catalytic activity (TOF = 107 min⁻¹, ECSA = 75.32 m²/gm) and stability. The evaluated value of activation energy of the catalytic hydrolysis using G–Co–Pt is 16 ± 2 kJ mol⁻¹.     

Hydrogen storage on graphene using Benkeser reaction

Recently, graphene has received great attention as potential hydrogen storage media. Here, we report a new route to store/chemisorb high content of hydrogen on graphene by employing Benkeser reaction. Graphene nanosheets are produced via a soft chemistry synthetic route involving oxidation of graphite using Improved method, ultrasonic exfoliation, and chemical reduction by using hydrazine with overnight heat treatment. Graphene is hydrogenated by using lithium in ethylenediamine under Benkeser reaction at atmospheric pressure and 30 °C. Benkeser reaction overcomes the liquid ammonia handling and produced multiple layer of graphene attached to the hydrogen atoms. High‐resolution transmission electron microscopy and selected area electron diffraction analysis confirm the ordered graphite crystal structure of graphene and reveal the rough, corrugated hydrogenated graphene layers attached by hydrogen atoms. Fourier transformation infrared spectroscopy analysis confirms that hydrogen adsorption occurs at all the ortho, meta, and para positions of aromatic graphene. The degree of hydrogenation of graphene estimated by thermogravimetric analysis reveals 14.67% (weight %) hydrogen storage, which is considerably higher than the earlier reported values of percentage storage achieved using various physisorption and chemisorption techniques. Copyright © 2014 John Wiley & Sons, Ltd.     

Effect of Cu/In molar ratio on the microstructural and optical properties of microcrystalline CuInS2 prepared by solvothermal route

Synthesis and characterization of CuInS₂ powder sample prepared by a simple and convenient solvothermal method is reported. The influence of the variation of Cu/In molar ratio from 0.69 to 1.25 on the particle morphology, crystal structure and optical properties of CuInS₂ samples was studied. The X-ray diffraction studies indicated that the samples were polycrystalline in nature. SEM images of the samples revealed that the copper-rich products were uniform microspheres with smooth surfaces, whereas microspheres formed by network of interconnected flakes were obtained for indium-rich products. The optical band gaps (E_g) of the products decreased from 1.60 to 1.43 eV with variation of Cu/In molar ratio. The variation of the Urbach tail width with Cu/In molar ratio indicated that the density of the defects is much higher for the indium-rich CuInS₂, which was clearly revealed from Raman measurements.     

Distributed Clustering Using Collective Principal Component Analysis

This paper considers distributed clustering of high-dimensional heterogeneous data using a distributed principal component analysis (PCA) technique called the collective PCA. It presents the collective PCA technique, which can be used independent of the clustering application. It shows a way to integrate the Collective PCA with a given off-the-shelf clustering algorithm in order to develop a distributed clustering technique. It also presents experimental results using different test data sets including an application for web mining. Received 30 August 2000 / Revised 30 January 2001 / Accepted in revised form 16 May 2001