Quantum Fisher Information of Several Qubitsin the Superposition of A GHZ and two W Stateswith Arbitrary Relative Phase

We study the quantum Fisher information (QFI) of a system of several particles which is in a superposition of a GHZ and two W states with arbitrary relative phase. We show that as the number of particles increases from 3 to 4, the behavior of QFI drastically changes. We also show how the dependence of QFI on the relative phase weakens as the number of particles increases. We also analyze the QFI for the state for several instances of N due to the change of the relative phases.     

The dip effect under integer quantized Hall conditions

In this work we investigate an unusual transport phenomenon observed in two-dimensional electron gas under integer quantum Hall effect conditions. Our calculations are based on the screening theory, using a semi-analytical model. The transport anomalies are dip and overshoot effects, where the Hall resistance decreases (or increases) unexpectedly at the quantized resistance plateaus intervals. We report on our numerical findings of the dip effect in the Hall resistance, considering GaAs/AlGaAs heterostructures in which we investigated the effect under different experimental conditions. We show that, similar to overshoot, the amplitude of the dip effect is strongly influenced by the edge reconstruction due to electrostatics. It is observed that the steep potential variation close to the physical boundaries of the sample results in narrower incompressible strips, hence, the experimental observation of the dip effect is limited by the properties of these current carrying strips. By performing standard Hall resistance measurements on gate defined narrow samples, we demonstrate that the predictions of the screening theory is in well agreement with our experimental findings.     

A novel method for multivariate data modelling: Piecewise Generalized EMPR

A multivariate data modelling problem consists of a number of nodes with associated function values. Increase in multivariance urges us to use divide-and-conquer algorithms in modelling process of these problems. High dimensional model representation based methods can partition a given multivariate data set into less-variate data sets and have the ability of building a model through these partitioned data sets. Generalized HDMR (GHDMR) is one of these methods and it is known that it works well for dominantly and purely additive natures. Piecewise Generalized HDMR is an alternative method and was developed to increase the efficiency of GHDMR but the performance of the method for modelling multiplicative natures is still not sufficient and acceptable. This work aims to develop a new piecewise method based on enhanced multivariance product representation which works well for representing multiplicative natures.     

Iron oxide film growth under ultrathin polysiloxane networks

This study focuses on the preparation and characterization of magnetic switchable thin iron oxide–polymer films. In a series of experiments, the formation and growth of iron oxide under ultrathin polysiloxane layers was controlled by changing the concentration of iron ions in the aqueous subphase or by varying the residence time of ammonia in the gas phase above the liquid sample. The growth of the combined film structures is studied in situ by interfacial rheology, optical microscopy, and x-ray scattering experiments and ex situ by scanning electron microscopy. Different stages of iron oxide aggregation, from a very thin layer of amorphous iron oxide with thickness of a few nanometers up to micrometer thick coatings of crystalline maghemite (γ-Fe₂O₃) were investigated. The specific interactions between the inorganic iron oxide and the polymer membranes cause the creation of new composite materials which are sensitive to magnetic forces.

A high dimensional model representation based numerical method for solving ordinary differential equations

A new numerical method for solving ordinary differential equations by using High Dimensional Model Representation (HDMR) has been developed in this work. Higher order ordinary differential equations can be reduced to a set of first order ODEs. Although HDMR is generally used for multivariate functions, univariate functions are taken into account throughout the work because of the ODEs’ natures. Not the numerical solution but its image under an appropriately chosen linear ordinary differential operator is expressed as a linear combination of the positive deviation powers of independent variable from its initial value. The linear combination of these image functions are expected to form a basis set under consideration. The unknown constants in the linear combination are found by maximizing the constancy measurer formed in terms of the HDMR components after they are evaluated. Results are compared with well-known step size based numerical methods. A semi qualitative error analysis of the proposed method is also established.     

Cyclobutane-substituted diacetamido sulfides and 2,5-diacylthiophenes

A general and convenient route for the synthesis of 2,5-di[1-methyl-1-arylcyclobutane-3-yl]- thiophenes 4a–c and bis[1-methyl-1-arylcyclobutane-3-yl]-2-(2-oxyethtylamido)thiazole sulfides 7a–c is reported. The characterization of these compounds was obtained by elemental analyses, IR, ¹³C, and ¹H NMR techniques. © 2003 Wiley Periodicals, Inc. 15:26–31, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/.hc10207     

Catalytic dehydrogenation of natural terpenes via CuPd alloy nanoparticles generated on mesoporous graphitic carbon nitride

A facile wet-chemical protocol for the synthesis of bimetallic CuPd alloy nanoparticles (NPs) anchored on mesoporous graphitic carbon nitride (m-gCN), serving as both stabilizer and support material, was presented herein. The presented protocol allowed to synthesize nearly monodisperse CuPd alloy NPs with an average particle size of 3.9 ± 0.9 nm without use of any additional surfactants and to prepare CuPd/m-gCN nanocatalysts with different Cu/Pd compositions (Cu₂₅Pd₇₅/m-gCN, Cu₃₅Pd₆₅/m-gCN, Cu₁₆Pd₇₄/m-gCN, Cu₃₂Pd₆₈/m-gCN, Cu₁₀Pd₉₀/m-gCN, and Cu₅₀Pd₅₀/m-gCN). After the detailed characterization of CuPd/m-gCN nanocatalysts, they were utilized as catalysts in the dehydrogenation of terpenes. Among all tested nanocatalysts, Cu₅₀Pd₅₀/m-gCN showed the highest activity in terms of the product yields within the same reaction time. Various parameters influencing the catalytic activity of Cu₅₀Pd₅₀/m-gCN were studied using himachalene as a model substrate and the optimum conditions were determined. Under the optimized reaction conditions, the catalytic application of Cu₅₀Pd₅₀/m-gCN nanocatalysts was extended to nine different terpenes and the corresponding products were obtained in high conversion yields (>90%) under mild conditions. A reusability test showed that Cu₅₀Pd₅₀/m-gCN nanocatalysts can be re-used up to four cycles without significant loss in their initial activity.