Lattice diffusion and surface segregation of B during growth of SiGe heterostructures by molecular beam epitaxy: effect of Ge concentration and biaxial stress

Si1-xGex/Si1-yGey/Si(100) heterostructures grown by Molecular Beam Epitaxy (MBE) were used in order to study B surface segregation during growth and B lattice diffusion. Ge concentration and stress effects were separated. Analysis of B segregation during growth shows that: i) for layers in epitaxy on (100)Si), B segregation decreases with increasing Ge concentration, i.e. with increased compressive stress, ii) for unstressed layers, B segregation increases with Ge concentration, iii) at constant Ge concentration, B segregation increases for layers in tension and decreases for layers in compression. The contrasting behaviors observed as a function of Ge concentration in compressively stressed and unstressed layers can be explained by an increase of the equilibrium segregation driving force induced by Ge additions and an increase of near-surface diffusion in compressively stressed layers. Analysis of lattice diffusion shows that: i) in unstressed layers, B lattice diffusion coefficient decreases with increasing Ge concentration, ii) at constant Ge concentration, the diffusion coefficient of B decreases with compressive biaxial stress and increases with tensile biaxial stress, iii) the volume of activation of B diffusion () is positive for biaxial stress while it is negative in the case of hydrostatic pressure. This confirms that under a biaxial stress the activation volume is reduced to the relaxation volume

Chemical vapour deposition of freestanding sub-60 nm graphene gyroids

The direct chemical vapour deposition of freestanding graphene gyroids with controlled sub-60 nm unit cell sizes is demonstrated. Three-dimensional (3D) nickel templates were fabricated through electrodeposition into a selectively voided triblock terpolymer. The high temperature instability of sub-micron unit cell structures was effectively addressed through the early introduction of the carbon precursor, which stabilizes the metallized gyroidal templates. The as-grown graphene gyroids are self-supporting and can be transferred onto a variety of substrates. Furthermore, they represent the smallest free standing periodic graphene 3D structures yet produced with a pore size of tens of nm, as analysed by electron microscopy and optical spectroscopy. We discuss generality of our methodology for the synthesis of other types of nanoscale, 3D graphene assemblies, and the transferability of this approach to other 2D materials

Influence of the Water Content on the Diffusion Coefficients of Li⁺ and Water across Naphthalenic Based Copolyimide Cation-Exchange Membranes

The transport of lithium ions in cation-exchange membranes based on sulfonated copolyimide membranes is reported. Diffusion coefficients of lithium are estimated as a function of the water content in membranes by using pulsed field gradient (PFG) NMR and electrical conductivity techniques. It is found that the lithium transport slightly decreases with the diminution of water for membranes with water content lying in the range 14 < λ < 26.5, where λ is the number of molecules of water per fixed sulfonate group. For λ < 14, the value of the diffusion coefficient of lithium experiences a sharp decay with the reduction of water in the membranes. The dependence of the diffusion of lithium on the humidity of the membranes calculated from conductivity data using Nernst–Planck type equations follows a trend similar to that observed by NMR. The possible explanation of the fact that the Haven ratio is higher than the unit is discussed. The diffusion of water estimated by 1H PFG-NMR in membranes neutralized with lithium decreases as λ decreases, but the drop is sharper in the region where the decrease of the diffusion of protons of water also undergoes considerable reduction. The diffusion of lithium ions computed by full molecular dynamics is similar to that estimated by NMR. However, for membranes with medium and low concentration of water, steady state conditions are not reached in the computations and the diffusion coefficients obtained by MD simulation techniques are overestimated. The curves depicting the variation of the diffusion coefficient of water estimated by NMR and full dynamics follow parallel trends, though the values of the diffusion coefficient in the latter case are somewhat higher. The WAXS diffractograms of fully hydrated membranes exhibit the ionomer peak at q = 2.8 nm⁻1, the peak being shifted to higher q as the water content of the membranes decreases. The diffractograms present additional peaks at higher q, common to wet and dry membranes, but the peaks are better resolved in the wet membranes. The ionomer peak is not detected in the diffractograms of dry membranes.The authors acknowledge financial support provided by the DGICYT (Dirección General de Investigación Cientifíca y Tecnológica) through Grant MAT2011-29174-C02-02

Gold and silver diffusion in germanium: a thermodynamic approach

Diffusion properties are technologically important in the understanding of semiconductors for the efficent formation of defined nanoelectronic devices. In the present study we employ experimental data to show that bulk materials properties (elastic and expansivity data) can be used to describe gold and silver diffusion in germanium for a wide temperature range (702–1177 K). Here we show that the so-called cBΩ model thermodynamic model, which assumes that the defect Gibbs energy is proportional to the isothermal bulk modulus and the mean volume per atom, adequately metallic diffusion in germanium

Geographic Visualization in Archaeology

Archaeologists are often considered frontrunners in employing spatial approaches within the social sciences and humanities, including geospatial technologies such as geographic information systems (GIS) that are now routinely used in archaeology. Since the late 1980s, GIS has mainly been used to support data collection and management as well as spatial analysis and modeling. While fruitful, these efforts have arguably neglected the potential contribution of advanced visualization methods to the generation of broader archaeological knowledge. This paper reviews the use of GIS in archaeology from a geographic visualization (geovisual) perspective and examines how these methods can broaden the scope of archaeological research in an era of more user-friendly cyber-infrastructures. Like most computational databases, GIS do not easily support temporal data. This limitation is particularly problematic in archaeology because processes and events are best understood in space and time. To deal with such shortcomings in existing tools, archaeologists often end up having to reduce the diversity and complexity of archaeological phenomena. Recent developments in geographic visualization begin to address some of these issues, and are pertinent in the globalized world as archaeologists amass vast new bodies of geo-referenced information and work towards integrating them with traditional archaeological data. Greater effort in developing geovisualization and geovisual analytics appropriate for archaeological data can create opportunities to visualize, navigate and assess different sources of information within the larger archaeological community, thus enhancing possibilities for collaborative research and new forms of critical inquiry

Recurrent neural networks can explain flexible trading of speed and accuracy in biological vision

Contains fulltext : 225297.pdf (publisher's version ) (Open Access)Deep feedforward neural network models of vision dominate in both computational neuroscience and engineering. The primate visual system, by contrast, contains abundant recurrent connections. Recurrent signal flow enables recycling of limited computational resources over time, and so might boost the performance of a physically finite brain or model. Here we show: (1) Recurrent convolutional neural network models outperform feedforward convolutional models matched in their number of parameters in large-scale visual recognition tasks on natural images. (2) Setting a confidence threshold, at which recurrent computations terminate and a decision is made, enables flexible trading of speed for accuracy. At a given confidence threshold, the model expends more time and energy on images that are harder to recognise, without requiring additional parameters for deeper computations. (3) The recurrent model's reaction time for an image predicts the human reaction time for the same image better than several parameter-matched and state-of-the-art feedforward models. (4) Across confidence thresholds, the recurrent model emulates the behaviour of feedforward control models in that it achieves the same accuracy at approximately the same computational cost (mean number of floating-point operations). However, the recurrent model can be run longer (higher confidence threshold) and then outperforms parameter-matched feedforward comparison models. These results suggest that recurrent connectivity, a hallmark of biological visual systems, may be essential for understanding the accuracy, flexibility, and dynamics of human visual recognition. Author summary: Deep neural networks provide the best current models of biological vision and achieve the highest performance in computer vision. Inspired by the primate brain, these models transform the image signals through a sequence of stages, leading to recognition. Unlike brains in which outputs of a given computation are fed back into the same computation, these models do not process signals recurrently. The ability to recycle limited neural resources by processing information recurrently could explain the accuracy and flexibility of biological visual systems, which computer vision systems cannot yet match. Here we report that recurrent processing can improve recognition performance compared to similarly complex feedforward networks. Recurrent processing also enabled models to behave more flexibly and trade off speed for accuracy. Like humans, the recurrent network models can compute longer when an object is hard to recognise, which boosts their accuracy. The model's recognition times predicted human recognition times for the same images. The performance and flexibility of recurrent neural network models illustrates that modeling biological vision can help us improve computer vision.27 p

Si self-diffusion in cubic B20-structured FeSi

Self-diffusion of implanted 31Si in the ε-phase FeSi (cubic B20-structure) has been determined in the temperature range 660$\hbox{--}$ 810 $^{\circ }$C using the modified radiotracer technique. With an activation enthalpy of 2.30 eV and a pre-exponential factor of 15$\times$10-8  m2 s-1 the silicon diffusivity was found to be slightly slower than Ge impurity diffusion in FeSi. This difference is proposed to originate from attractive elastic interactions prevailing between the slightly oversized Ge atoms and the Si sublattice vacancies. The results confirm the argument that 71Ge radioisotopes may be used to substitute the short-lived 31Si radiotracers when estimating self-diffusion in silicides