Nucleation and Growth of GaN/AlN Quantum Dots

We study the nucleation of GaN islands grown by plasma-assisted molecular-beam epitaxy on AlN(0001) in a Stranski-Krastanov mode. In particular, we assess the variation of their height and density as a function of GaN coverage. We show that the GaN growth passes four stages: initially, the growth is layer-by-layer; subsequently, two-dimensional precursor islands form, which transform into genuine three-dimensional islands. During the latter stage, island height and density increase with GaN coverage until the density saturates. During further GaN growth, the density remains constant and a bimodal height distribution appears. The variation of island height and density as a function of substrate temperature is discussed in the framework of an equilibrium model for Stranski-Krastanov growth.Comment: Submitted to PRB, 10 pages, 15 figure

Step by step capping and strain state of GaN/AlN quantum dots studied by grazing incidence diffraction anomalous fine structure

The investigation of small size embedded nanostructures, by a combination of complementary anomalous diffraction techniques, is reported. GaN Quantum Dots (QDs), grown by molecular beam epitaxy in a modified Stranski-Krastanow mode, are studied in terms of strain and local environment, as a function of the AlN cap layer thickness, by means of grazing incidence anomalous diffraction. That is, the X-ray photons energy is tuned across the Ga absorption K-edge which makes diffraction chemically selective. Measurement of \textit{hkl}-scans, close to the AlN (30-30) Bragg reflection, at several energies across the Ga K-edge, allows the extraction of the Ga partial structure factor, from which the in-plane strain of GaN QDs is deduced. From the fixed-Q energy-dependent diffracted intensity spectra, measured for diffraction-selected iso-strain regions corresponding to the average in-plane strain state of the QDs, quantitative information regarding composition and the out-of-plane strain has been obtained. We recover the in-plane and out-of-plane strains in the dots. The comparison to the biaxial elastic strain in a pseudomorphic layer indicates a tendency to an over-strained regime.Comment: submitted to PR

Self-assembled zinc blende GaN quantum dots grown

Zinc blende ~ZB! GaN quantum dots have been grown by plasma-assisted molecular-beam epitaxy on AlN buffer layers using 3C-SiC~001! substrates. The two- to three-dimensional growth mode transition is studied by following the evolution of the reflection high-energy electron diffraction pattern. ZB GaN island layers are further examined by atomic force microscopy and transmission electron microscopy, extracting a mean island height of 1.6 nm and a mean diameter of 13 nm at a density of 1.331011 cm22. Embedded ZB GaN quantum dots show strong ultraviolet photoluminescence without any thermal quenching up to room temperature.SFERERegion Rhône-AlpesConsejo Nacional de Ciencia y Tecnologí

The interplay of microscopic and mesoscopic structure in complex networks

Not all nodes in a network are created equal. Differences and similarities exist at both individual node and group levels. Disentangling single node from group properties is crucial for network modeling and structural inference. Based on unbiased generative probabilistic exponential random graph models and employing distributive message passing techniques, we present an efficient algorithm that allows one to separate the contributions of individual nodes and groups of nodes to the network structure. This leads to improved detection accuracy of latent class structure in real world data sets compared to models that focus on group structure alone. Furthermore, the inclusion of hitherto neglected group specific effects in models used to assess the statistical significance of small subgraph (motif) distributions in networks may be sufficient to explain most of the observed statistics. We show the predictive power of such generative models in forecasting putative gene-disease associations in the Online Mendelian Inheritance in Man (OMIM) database. The approach is suitable for both directed and undirected uni-partite as well as for bipartite networks

Eu3+ optical activation engineering in AlxGa(1-x)N nanowires for red solid-state nano-emitters

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Deciphering the connectivity structure of biological networks using MixNet

<p>Abstract</p> <p>Background</p> <p>As biological networks often show complex topological features, mathematical methods are required to extract meaningful information. Clustering methods are useful in this setting, as they allow the summary of the network's topology into a small number of relevant classes. Different strategies are possible for clustering, and in this article we focus on a model-based strategy that aims at clustering nodes based on their connectivity profiles.</p> <p>Results</p> <p>We present MixNet, the first publicly available computer software that analyzes biological networks using mixture models. We apply this method to various networks such as the <it>E. coli </it>transcriptional regulatory network, the macaque cortex network, a foodweb network and the <it>Buchnera aphidicola </it>metabolic network. This method is also compared with other approaches such as module identification or hierarchical clustering.</p> <p>Conclusion</p> <p>We show how MixNet can be used to extract meaningful biological information, and to give a summary of the networks topology that highlights important biological features. This approach is powerful as MixNet is adaptive to the network under study, and finds structural information without any a priori on the structure that is investigated. This makes MixNet a very powerful tool to summarize and decipher the connectivity structure of biological networks.</p

Statistical methodology for the analysis of dye-switch microarray experiments

<p>Abstract</p> <p>Background</p> <p>In individually dye-balanced microarray designs, each biological sample is hybridized on two different slides, once with <it>Cy3 </it>and once with <it>Cy5</it>. While this strategy ensures an automatic correction of the gene-specific labelling bias, it also induces dependencies between log-ratio measurements that must be taken into account in the statistical analysis.</p> <p>Results</p> <p>We present two original statistical procedures for the statistical analysis of individually balanced designs. These procedures are compared with the usual ML and REML mixed model procedures proposed in most statistical toolboxes, on both simulated and real data.</p> <p>Conclusion</p> <p>The UP procedure we propose as an alternative to usual mixed model procedures is more efficient and significantly faster to compute. This result provides some useful guidelines for the analysis of complex designs.</p