The effect of local electronic interaction on the optical properties of boron–nitride nanotubes

We investigate the behavior of optical absorption of boron–nitride nanotubes (6,0) in the context of Hubbard model at the paramagnetic sector. GW approximation has been implemented in order to make self-energy matrix of electronic system. Afterwards, the real and imaginary parts of transverse dielectric functions have been obtained using linear response theory. The results show that the frequency gap in the optical absorption decreases with Coulomb repulsion strength. Moreover the results show that the local Coulomb interaction leads to the appearance of the excitonic effects in the optical spectrum. Finally the effects of electronic concentration on the frequency behavior of imaginary part of dielectric function have been investigated.     

The Ablowitz–Ladik hierarchy integrability analysis revisited: the vertex operator solution representation structure

A regular gradient-holonomic approach to studying the Lax type integrability of the Ablowitz–Ladik hierarchy of nonlinear Lax type integrable discrete dynamical systems in the vertex operator representation is presented. The relationship to the Lie-algebraic integrability scheme is analyzed and the connection with the τ-function representation is discussed.     

From 2-dimensional surfaces to cosmological solutions

We construct perfect fluid metrics with two symmetries by means of a recently developed geometrical method [1]. The Einstein equations are reduced to a single equation for a conformal factor. Under additional assumptions we obtain new cosmological solutions of Bianchi type II, VI₀ and VII₀. The solutions depend on an arbitrary function of time, which can be specified in order to satisfy an equation of state.     

Peripatetic Highlights in Bern

This tour of significant scientific sites in Bern uses the local legacy of its most illustrious scientists, Albert Einstein (1879–1955) and Fritz Houtermans (1903–1966), as its guiding thread through the old town and the university district.Ann M.Hentschel is translator of the correspondence volumes of The Collected Papers of Albert Einstein and is currently employed by the Institute for the History and Philosophy of Science at the University of Bern to write the guide for a city tour of Einsteins old haunts in Bern for the coming jubilee of special relativity in 2005. For the historical companion guidebook, see Hentschel and Grasshoff, Albert Einstein (ref. 4) and the website <.     

Micellar core-shell-type acrylic-polyurethane hybrid materials with self-polishing property

Abstract

Marine fouling can be a serious problem in the shipping industry, since it increases the surface roughness of the hull and hence its frictional resistance to its movement through water. Antifouling paint can be defined as preventing the attachment of marine organisms onto surfaces. However, the most commonly used antifouling coating which is the tributyltin-based self-polishing copolymer causes the severe pollution of marine environment. Ammonium salt-based paints include tertiary amines as biocides which have effective biocidal and biodegradable properties without accumulation in the sea environment. However, ammonium salt-based coatings were too sensitive to seawater and became swollen. In this study, polyurethane-acrylic copolymers were synthesized by radical polymerization. These hybrid materials were found to form core–shell structures in aqueous media. Synthesis and properties of copolymers were investigated by Fourier transform-infrared spectrometer, proton-nuclear magnetic resonance, transmission electron microscopy, and dynamic light scattering. The polishing rate of self-polishing copolymer was determined from the reduction in dry film thickness after artificial seawater immersion under a dynamic condition.     

A comparative first-principles study on electronic structures and mechanical properties of ternary intermetallic compounds Al8Cr4Y and Al8Cu4Y: Pressure and tension effects

An investigation into the bulk properties, elastic properties and Debye temperature under pressure, and deformation mode under tension of Al₈Cu₄Y and Al₈Cr₄Y compounds was investigated by using first principles calculations based on density functional theory. The calculated lattice constants for the ternary compounds (Al₈Cu₄Y and Al₈Cr₄Y) are in good agreement with the experimental data. It can be seen from interatomic distances that the bonding between Al1 atom and Cr, Y, and Al2 atoms in Al₈Cr₄Y are stronger than Al₈Cu₄Y. The results of cohesive energy show that Al₈Cr₄Y should be easier to be formed and much stronger chemical bonds than Al₈Cu₄Y. The bulk modulus B, shear modulus G, Young's modulus E and Poisson's ratio ν can be obtained by using the Voigt–Reuss–Hill averaging scheme. From the results of elastic properties, Al₈Cr₄Y has the stronger mechanical behavior than Al₈Cu₄Y. Our calculations also show that pressure has a greater effect on mechanical behavior for both compounds. The ideal tensile strength are obtained by stress-strain relationships under [001](001) uniaxial tensile deformation, which are 15.4 and 23.4 GPa for Al₈Cu₄Y and Al₈Cr₄Y, respectively. The total and partial density of states and electron charge density under uniaxial tensile deformations for Al₈Cu₄Y and Al₈Cr₄Y compounds are also calculated and discussed in this work.     

Using Short Wave Visible–Near Infrared Reflectance Spectroscopy to Predict Soil Properties and Content

In this study, short wave visible–near infrared reflectance spectroscopy was evaluated for prediction of diverse soil properties related to four different soil series of several regions in Jiangxi, China. A total of 240 soil samples were collected for the calibration (n = 168) and prediction (n = 72) sets. The used wavelength range of short wave visible–near infrared reflectance spectroscopy is 325–1075 nm. Partial least squares regression and back propagation neural network were used to develop models for soil properties such as organic matter and extractable forms of calcium, magnesium, and potassium. Performance of these models was also compared and analyzed. The input of back propagation neural network was the first six principal components resulted from the principal component analysis and the optimal number of latent variables obtained from partial least squares regression. The overall results showed that the performance of partial least squares regression model was inferior to all back propagation neural network models. The best prediction was obtained with latent variables as input of back propagation neural network model for organic matter (determination coefficient = 0.84 and relative predictive determinant = 2.38), which was classified as very good model predictions. The prediction of calcium, magnesium, and potassium was classified as fair (determination coefficient = 0.56–0.68 and relative predictive determinant = 1.51–1.61), where quantitative predictions were considered possible. It is recommended to adopt latent variables as input for back propagation neural network model predicting soil properties with short wave visible–near infrared reflectance spectroscopy. In conclusion, short wave visible–near infrared reflectance spectroscopy was variably successful in estimating soil properties and showed potential for substituting laboratory analyses.     

Sonochemical temperature controlled synthesis of pellet-, laminate- and rice grain-like morphologies of a Cu(II) porous metal–organic framework nano-structures

Nano-structures of the Cu(II) metal–organic framework, {Cu(BDT)(DMF)·CH₃OH·0.25DMF}_n (1), which BDT²⁻ is 1,4-benzeneditetrazolate, have been synthesized by the reaction of H₂BDT with Cu(NO₃)₂·6H₂O via ultrasonic irradiation in three different temperatures, which causes different morphologies. The products were characterized by IR spectroscopy, elemental analysis, scanning electron microscopy and X-ray powder diffraction. This study demonstrates that sonochemistry is a suitable method for preparation of metal–organic framework nano-structures and temperature is an effective parameter on morphologies of Cu(II) metal–organic framework nano-structures.     

An intelligent approach for engine fault diagnosis based on Hilbert–Huang transform and support vector machine

Based on the techniques of Hilbert–Huang transform (HHT) and support vector machine (SVM), a noise-based intelligent method for engine fault diagnosis (EFD), so-called HHT–SVM model, is developed in this paper. The noises of a sample engine under normal and several fault states are first measured and denoised by using the wavelet packet threshold method to initially lower the noise level with negligible signal distortion. To extract fault features of the engine, then, the HHT is selected and applied to the measured noise signals. A nine-dimensional vector, which consists of seven intrinsic mode functions (IMFs) from the empirical mode decomposition (EMD), maximum value of HHT marginal spectrum and its corresponding frequency component, is specified to represent each engine fault feature. Finally, an optimal SVM model is established and trained for engine failure classification by using the fault feature vectors of the noise signals. Cross-validation results show that the proposed noise-based HHT–SVM method is accurate and effective for engine fault diagnosis. Due to outstanding time–frequency characteristics and pattern recognition capacity of the HHT and SVM, the newly proposed HHT–SVM can be used to deal with both the stationary and nonstationary signals, and even the transient ones. In the view of applications, the HHT–SVM technique may be suggested not only to detect the abnormal states of vehicle engines, but also to be extended to other fields for failure diagnosis in engineering.