Suppression of electron scattering resonances in graphene by quantum dots

Transmission of low-energetic electrons through two-dimensional materials leads to unique scattering resonances. These resonances contribute to photoemission from occupied bands where they appear as strongly dispersive features of suppressed photoelectron intensity. Using angle-resolved photoemission we have systematically studied scattering resonances in epitaxial graphene grown on the chemically differing substrates Ir(111), Bi/Ir, Ni(111) as well as in graphene/Ir(111) nanopatterned with a superlattice of uniform Ir quantum dots. While the strength of the chemical interaction with the substrate has almost no effect on the dispersion of the scattering resonances, their energy can be controlled by the magnitude of charge transfer from/to graphene. At the same time, a superlattice of small quantum dots deposited on graphene eliminates the resonances completely. We ascribe this effect to a nanodot-induced buckling of graphene and its local rehybridization from sp$^{2}$ to sp$^{3}$ towards a three-dimensional structure. Our results suggest nanopatterning as a prospective tool for tuning optoelectronic properties of two-dimensional materials with graphene-like structure.Comment: The following article has been submitted to Applied Physics Letters. If it is published, it will be found online at http://apl.aip.or

Rashba splitting of 100 meV in Au-intercalated graphene on SiC

Intercalation of Au can produce giant Rashba-type spin-orbit splittings in graphene but this has not yet been achieved on a semiconductor substrate. For graphene/SiC(0001), Au intercalation yields two phases with different doping. Here, we report the preparation of an almost pure p-type graphene phase after Au intercalation. We observe a 100 meV Rashba-type spin-orbit splitting at 0.9 eV binding energy. We show that this giant splitting is due to hybridization and much more limited in energy and momentum space than for Au-intercalated graphene on Ni

Proton Stopping Power of Different Density Profile Plasmas

In this work, the stopping power of a partially ionized plasma is analyzed by means of free electron stopping and bound electron stopping. For the first one, the RPA dielectric function is used, and for the latter one, an interpolation of high and low projectile velocity formulas is used. The dynamical energy loss of an ion beam inside a plasma is estimated by using an iterative scheme of calculation. The Abel inversion is also applied when we have a plasma with radial symmetry. Finally, we compare our methods with two kind of plasmas. In the first one, we estimate the energy loss in a plasma created by a laser prepulse, whose density is approximated by a piecewise function. For the latter one, a radial electron density is supposed and the stopping is obtained as function of radius from the calculated lateral points. In both cases, the dependence with the density profile is observed.Comment: 5 pages, 7 figure

Deep-Sea Mining: a Manageable Necessity or a Curse?

The dependence of modern societies upon critical raw materials (nearly all metals) is overwhelming. Some believe that demand is growing faster than offer, not only because of geological availability but also for political and economic reasons. For these reasons it is imperative to consider new sources for raw materials.The seafloor stands as a likely candidate. We must create readiness now to be prepared when the need comes. One of the greatest fears is the environmental cost involved in mining the deep seafloor. However, the mining industry no longer deserves its partially not favorable reputation. We need both the resources and the environment. And nIMBY (not In My Back Yard) will not help.info:eu-repo/semantics/publishedVersio

Neuro-fuzzy techniques to optimize an FPGA embedded controller for robot navigation

This paper describes how low-cost embedded controllers for robot navigation can be obtained by using a small number of if-then rules (exploiting the connection in cascade of rule bases) that apply Takagi-Sugeno fuzzy inference method and employ fuzzy sets represented by normalized triangular functions. The rules comprise heuristic and fuzzy knowledge together with numerical data obtained from a geometric analysis of the control problem that considers the kinematic and dynamic constraints of the robot. Numerical data allow tuning the fuzzy symbols used in the rules to optimize the controller performance. From the implementation point of view, very few computational and memory resources are required: standard logical, addition, and multiplication operations and a few data that can be represented by integer values. This is illustrated with the design of a controller for the safe navigation of an autonomous car-like robot among possible obstacles toward a goal configuration. Implementation results of an FPGA embedded system based on a general-purpose soft processor confirm that percentage reduction in clock cycles is drastic thanks to applying the proposed neuro-fuzzy techniques. Simulation and experimental results obtained with the robot confirm the efficiency of the controller designed. Design methodology has been supported by the CAD tools of the environment Xfuzzy 3 and by the Embedded System Tools from Xilinx. © 2014 Elsevier B.V.Peer Reviewe