Blue luminescence of Au nanoclusters embedded in silica matrix

Photoluminescence study using the 325 nm He-Cd excitation is reported for the Au nanoclusters embedded in SiO2 matrix. Au clusters are grown by ion beam mixing with 100 KeV Ar+ irradiation on Au [40 nm]/SiO2 at various fluences and subsequent annealing at high temperature. The blue bands above ~3 eV match closely with reported values for colloidal Au nanoclusters and supported Au nanoislands. Radiative recombination of sp electrons above Fermi level to occupied d-band holes are assigned for observed luminescence peaks. Peaks at 3.1 eV and 3.4 eV are correlated to energy gaps at the X- and L-symmetry points, respectively, with possible involvement of relaxation mechanism. The blue shift of peak positions at 3.4 eV with decreasing cluster size is reported to be due to the compressive strain in small clusters. A first principle calculation based on density functional theory using the full potential linear augmented plane wave plus local orbitals (FP-LAPW+LO) formalism with generalized gradient approximation (GGA) for the exchange correlation energy is used to estimate the band gaps at the X- and L-symmetry points by calculating the band structures and joint density of states (JDOS) for different strain values in order to explain the blueshift of ~0.1 eV with decreasing cluster size around L-symmetry point.Comment: 13 pages, 7 Figures Only in PDF format; To be published in J. of Chem. Phys. (Tentative issue of publication 8th December 2004

Determining appropriate approaches for using data in feature selection

Feature selection is increasingly important in data analysis and machine learning in big data era. However, how to use the data in feature selection, i.e. using either ALL or PART of a dataset, has become a serious and tricky issue. Whilst the conventional practice of using all the data in feature selection may lead to selection bias, using part of the data may, on the other hand, lead to underestimating the relevant features under some conditions. This paper investigates these two strategies systematically in terms of reliability and effectiveness, and then determines their suitability for datasets with different characteristics. The reliability is measured by the Average Tanimoto Index and the Inter-method Average Tanimoto Index, and the effectiveness is measured by the mean generalisation accuracy of classification. The computational experiments are carried out on ten real-world benchmark datasets and fourteen synthetic datasets. The synthetic datasets are generated with a pre-set number of relevant features and varied numbers of irrelevant features and instances, and added with different levels of noise. The results indicate that the PART approach is more effective in reducing the bias when the size of a dataset is small but starts to lose its advantage as the dataset size increases

Lattice-gas simulations of Domain Growth, Saturation and Self-Assembly in Immiscible Fluids and Microemulsions

We investigate the dynamical behavior of both binary fluid and ternary microemulsion systems in two dimensions using a recently introduced hydrodynamic lattice-gas model of microemulsions. We find that the presence of amphiphile in our simulations reduces the usual oil-water interfacial tension in accord with experiment and consequently affects the non-equilibrium growth of oil and water domains. As the density of surfactant is increased we observe a crossover from the usual two-dimensional binary fluid scaling laws to a growth that is {\it slow}, and we find that this slow growth can be characterized by a logarithmic time scale. With sufficient surfactant in the system we observe that the domains cease to grow beyond a certain point and we find that this final characteristic domain size is inversely proportional to the interfacial surfactant concentration in the system.Comment: 28 pages, latex, embedded .eps figures, one figure is in colour, all in one uuencoded gzip compressed tar file, submitted to Physical Review

Combination of linear classifiers using score function -- analysis of possible combination strategies

In this work, we addressed the issue of combining linear classifiers using their score functions. The value of the scoring function depends on the distance from the decision boundary. Two score functions have been tested and four different combination strategies were investigated. During the experimental study, the proposed approach was applied to the heterogeneous ensemble and it was compared to two reference methods -- majority voting and model averaging respectively. The comparison was made in terms of seven different quality criteria. The result shows that combination strategies based on simple average, and trimmed average are the best combination strategies of the geometrical combination

Mechanical and Electronic Properties of MoS2_2 Nanoribbons and Their Defects

We present our study on atomic, electronic, magnetic and phonon properties of one dimensional honeycomb structure of molybdenum disulfide (MoS$_2$) using first-principles plane wave method. Calculated phonon frequencies of bare armchair nanoribbon reveal the fourth acoustic branch and indicate the stability. Force constant and in-plane stiffness calculated in the harmonic elastic deformation range signify that the MoS$_2$ nanoribbons are stiff quasi one dimensional structures, but not as strong as graphene and BN nanoribbons. Bare MoS$_2$ armchair nanoribbons are nonmagnetic, direct band gap semiconductors. Bare zigzag MoS$_2$ nanoribbons become half-metallic as a result of the (2x1) reconstruction of edge atoms and are semiconductor for minority spins, but metallic for the majority spins. Their magnetic moments and spin-polarizations at the Fermi level are reduced as a result of the passivation of edge atoms by hydrogen. The functionalization of MoS$_2$ nanoribbons by adatom adsorption and vacancy defect creation are also studied. The nonmagnetic armchair nanoribbons attain net magnetic moment depending on where the foreign atoms are adsorbed and what kind of vacancy defect is created. The magnetization of zigzag nanoribbons due to the edge states is suppressed in the presence of vacancy defects.Comment: 11 pages, 5 figures, first submitted at November 23th, 200

Atomistic simulations of self-trapped exciton formation in silicon nanostructures: The transition from quantum dots to nanowires

Using an approximate time-dependent density functional theory method, we calculate the absorption and luminescence spectra for hydrogen passivated silicon nanoscale structures with large aspect ratio. The effect of electron confinement in axial and radial directions is systematically investigated. Excited state relaxation leads to significant Stokes shifts for short nanorods with lengths less than 2 nm, but has little effect on the luminescence intensity. The formation of self-trapped excitons is likewise observed for short nanostructures only; longer wires exhibit fully delocalized excitons with neglible geometrical distortion at the excited state minimum.Comment: 10 pages, 4 figure

What is a Cool-Core Cluster? A Detailed Analysis of the Cores of the X-ray Flux-Limited HIFLUGCS Cluster Sample

We use the largest complete sample of 64 galaxy clusters (HIghest X-ray FLUx Galaxy Cluster Sample) with available high-quality X-ray data from Chandra, and apply 16 cool-core diagnostics to them, some of them new. We also correlate optical properties of brightest cluster galaxies (BCGs) with X-ray properties. To segregate cool core and non-cool-core clusters, we find that central cooling time, t_cool, is the best parameter for low redshift clusters with high quality data, and that cuspiness is the best parameter for high redshift clusters. 72% of clusters in our sample have a cool core (t_cool < 7.7 h_{71}^{-1/2} Gyr) and 44% have strong cool cores (t_cool <1.0 h_{71}^{-1/2} Gyr). For the first time we show quantitatively that the discrepancy in classical and spectroscopic mass deposition rates can not be explained with a recent formation of the cool cores, demonstrating the need for a heating mechanism to explain the cooling flow problem. [Abridged]Comment: 45 pages, 19 figures, 7 tables. Accepted for publication in A&A. Contact Person: Rupal Mittal ([email protected]

An evaluative baseline for geo-semantic relatedness and similarity

In geographic information science and semantics, the computation of semantic similarity is widely recognised as key to supporting a vast number of tasks in information integration and retrieval. By contrast, the role of geo-semantic relatedness has been largely ignored. In natural language processing, semantic relatedness is often confused with the more specific semantic similarity. In this article, we discuss a notion of geo-semantic relatedness based on Lehrer’s semantic fields, and we compare it with geo-semantic similarity. We then describe and validate the Geo Relatedness and Similarity Dataset (GeReSiD), a new open dataset designed to evaluate computational measures of geo-semantic relatedness and similarity. This dataset is larger than existing datasets of this kind, and includes 97 geographic terms combined into 50 term pairs rated by 203 human subjects. GeReSiD is available online and can be used as an evaluation baseline to determine empirically to what degree a given computational model approximates geo-semantic relatedness and similarity

Predicting gene function using hierarchical multi-label decision tree ensembles

<p>Abstract</p> <p>Background</p> <p><it>S. cerevisiae</it>, <it>A. thaliana </it>and <it>M. musculus </it>are well-studied organisms in biology and the sequencing of their genomes was completed many years ago. It is still a challenge, however, to develop methods that assign biological functions to the ORFs in these genomes automatically. Different machine learning methods have been proposed to this end, but it remains unclear which method is to be preferred in terms of predictive performance, efficiency and usability.</p> <p>Results</p> <p>We study the use of decision tree based models for predicting the multiple functions of ORFs. First, we describe an algorithm for learning hierarchical multi-label decision trees. These can simultaneously predict all the functions of an ORF, while respecting a given hierarchy of gene functions (such as FunCat or GO). We present new results obtained with this algorithm, showing that the trees found by it exhibit clearly better predictive performance than the trees found by previously described methods. Nevertheless, the predictive performance of individual trees is lower than that of some recently proposed statistical learning methods. We show that ensembles of such trees are more accurate than single trees and are competitive with state-of-the-art statistical learning and functional linkage methods. Moreover, the ensemble method is computationally efficient and easy to use.</p> <p>Conclusions</p> <p>Our results suggest that decision tree based methods are a state-of-the-art, efficient and easy-to-use approach to ORF function prediction.</p

Direct bandgap optical transitions in Si nanocrystals

The effect of quantum confinement on the direct bandgap of spherical Si nanocrystals has been modelled theoretically. We conclude that the energy of the direct bandgap at the $\Gamma$-point decreases with size reduction: quantum confinement enhances radiative recombination across the direct bandgap and introduces its "red" shift for smaller grains. We postulate to identify the frequently reported efficient blue emission (F-band) from Si nanocrystals with this zero-phonon recombination. In a dedicated experiment, we confirm the "red" shift of the F-band, supporting the proposed identification