Intercalation of graphene on SiC(0001) via ion-implantation

Electronic devices based on graphene technology are catching on rapidly and the ability to engineer graphene properties at the nanoscale is becoming, more than ever, indispensable. Here, we present a new procedure of graphene functionalization on SiC(0001) that paves the way towards the fabrication of complex graphene electronic chips. The procedure resides on the well-known ion-implantation technique. The efficiency of the working principle is demonstrated by the intercalation of the epitaxial graphene layer on SiC(0001) with Bi atoms, which was not possible following standard procedures. Our results put forward the ion-beam lithography to nanostructure and functionalize desired graphene chips

Quasi-selective ultrafilters and asymptotic numerosities

We isolate a new class of ultrafilters on N, called “quasi-selective” because they are intermediate between selective ultrafilters and P-points. (Under the Continuum Hypothesis these three classes are distinct.) The existence of quasi-selective ultrafilters is equivalent to the existence of “asymptotic numerosities” for all sets of tuples A ⊆ N^k. Such numerosities are hypernatural numbers that generalize finite cardinalities to countable point sets. Most notably, they maintain the structure of ordered semiring, and, in a precise sense, they allow for a natural extension of asymptotic density to all sets of tuples of natural numbers

Ab-Initio Studies on Carburization of Fe3Al Based Alloys

AbstractFe-Al based alloys exhibit excellent properties but suffer metal dusting in carburizing atmospheres. Surface composition can be a determinant factor in the solution of this problem. We calculate in this work the C adsorption energies in the L21 Fe2AlX (X=Ti,V,Nb) structures and we study the influence of surface covering. Our results show the beneficial effect of Ti, suggesting that there could exist an activation energy to promote the incorporation of C in the subsurface layers when the surface is covered enoug

Direct evidence for efficient ultrafast charge separation in epitaxial WS2_2/graphene heterostructure

We use time- and angle-resolved photoemission spectroscopy (tr-ARPES) to investigate ultrafast charge transfer in an epitaxial heterostructure made of monolayer WS$_2$ and graphene. This heterostructure combines the benefits of a direct gap semiconductor with strong spin-orbit coupling and strong light-matter interaction with those of a semimetal hosting massless carriers with extremely high mobility and long spin lifetimes. We find that, after photoexcitation at resonance to the A-exciton in WS$_2$, the photoexcited holes rapidly transfer into the graphene layer while the photoexcited electrons remain in the WS$_2$ layer. The resulting charge transfer state is found to have a lifetime of $\sim1$\,ps. We attribute our findings to differences in scattering phase space caused by the relative alignment of WS$_2$ and graphene bands as revealed by high resolution ARPES. In combination with spin-selective excitation using circularly polarized light the investigated WS$_2$/graphene heterostructure might provide a new platform for efficient optical spin injection into graphene.Comment: 28 pages, 14 figure

Asymptotic stability of the Cauchy and Jensen functional equations

The aim of this note is to investigate the asymptotic stability behaviour of the Cauchy and Jensen functional equations. Our main results show that if these equations hold for large arguments with small error, then they are also valid everywhere with a new error term which is a constant multiple of the original error term. As consequences, we also obtain results of hyperstability character for these two functional equations

Direct evidence for efficient ultrafast charge separation in epitaxial WS₂/graphene heterostructures

We use time- and angle-resolved photoemission spectroscopy (tr-ARPES) to investigate ultrafast charge transfer in an epitaxial heterostructure made of monolayer WS2 and graphene. This heterostructure combines the benefits of a direct-gap semiconductor with strong spin-orbit coupling and strong light-matter interaction with those of a semimetal hosting massless carriers with extremely high mobility and long spin lifetimes. We find that, after photoexcitation at resonance to the A-exciton in WS2, the photoexcited holes rapidly transfer into the graphene layer while the photoexcited electrons remain in the WS2 layer. The resulting charge-separated transient state is found to have a lifetime of ∼1 ps. We attribute our findings to differences in scattering phase space caused by the relative alignment of WS2 and graphene bands as revealed by high-resolution ARPES. In combination with spin-selective optical excitation, the investigated WS2/graphene heterostructure might provide a platform for efficient optical spin injection into graphene

Grid services for the MAGIC experiment

Exploring signals from the outer space has become an observational science under fast expansion. On the basis of its advanced technology the MAGIC telescope is the natural building block for the first large scale ground based high energy gamma-ray observatory. The low energy threshold for gamma-rays together with different background sources leads to a considerable amount of data. The analysis will be done in different institutes spread over Europe. Therefore MAGIC offers the opportunity to use the Grid technology to setup a distributed computational and data intensive analysis system with the nowadays available technology. Benefits of Grid computing for the MAGIC telescope are presented.Comment: 5 pages, 1 figures, to be published in the Proceedings of the 6th International Symposium ''Frontiers of Fundamental and Computational Physics'' (FFP6), Udine (Italy), Sep. 26-29, 200

Convergence of Discrete-Time Cellular Neural Networks with Application to Image Processing

The paper considers a class of discrete-time cellular neural networks (DT-CNNs) obtained by applying Euler's discretization scheme to standard CNNs. Let T be the DT-CNN interconnection matrix which is defined by the feedback cloning template. The paper shows that a DT-CNN is convergent, i.e. each solution tends to an equilibrium point, when T is symmetric and, in the case where T + En is not positive-semidefinite, the step size of Euler's discretization scheme does not exceed a given bound (En is the n × n unit matrix). It is shown that two relevant properties hold as a consequence of the local and space-invariant interconnecting structure of a DT-CNN, namely: (1) the bound on the step size can be easily estimated via the elements of the DT-CNN feedback cloning template only; (2) the bound is independent of the DT-CNN dimension. These two properties make DT-CNNs very effective in view of computer simulations and for the practical applications to high-dimensional processing tasks. The obtained results are proved via Lyapunov approach and LaSalle's Invariance Principle in combination with some fundamental inequalities enjoyed by the projection operator on a convex set. The results are compared with previous ones in the literature on the convergence of DT-CNNs and also with those obtained for different neural network models as the Brain-State-in-a-Box model. Finally, the results on convergence are illustrated via the application to some relevant 2D and 1D DT-CNNs for image processing tasks