Altering the properties of graphene on Cu(111) by intercalation of potassium bromide

The catalytic growth on transition metal surfaces provides a clean and controllable route to obtain defect-free, monocrystalline graphene. However, graphene's optical and electronic properties are diminished by the interaction with the metal substrate. One way to overcome this obstacle is the intercalation of atoms and molecules decoupling the graphene and restoring its electronic structure. We applied noncontact atomic force microscopy to study the structural and electric properties of graphene on clean Cu(111) and after the adsorption of KBr or NaCl. By means of Kelvin probe force microscopy, a change in graphene's work function has been observed after the deposition of KBr, indicating a changed graphene-substrate interaction. Further measurements of single-electron charging events as well as X-ray photoelectron spectroscopy confirmed an electronic decoupling of the graphene islands by KBr intercalation. The results have been compared with density functional theory calculations, supporting our experimental findings

On the importance of sluggish state memory for learning long term dependency

The vanishing gradients problem inherent in Simple Recurrent Networks (SRN) trained with back-propagation, has led to a significant shift towards the use of Long Short-term Memory (LSTM) and Echo State Networks (ESN), which overcome this problem through either second order error-carousel schemes or different learning algorithms respectively. This paper re-opens the case for SRN-based approaches, by considering a variant, the Multi-recurrent Network (MRN). We show that memory units embedded within its architecture can ameliorate against the vanishing gradient problem, by providing variable sensitivity to recent and more historic information through layer- and self-recurrent links with varied weights, to form a so-called sluggish state-based memory. We demonstrate that an MRN, optimised with noise injection, is able to learn the long term dependency within a complex grammar induction task, significantly outperforming the SRN, NARX and ESN. Analysis of the internal representations of the networks, reveals that sluggish state-based representations of the MRN are best able to latch on to critical temporal dependencies spanning variable time delays, to maintain distinct and stable representations of all underlying grammar states. Surprisingly, the ESN was unable to fully learn the dependency problem, suggesting the major shift towards this class of models may be premature

Using metallic noncontact atomic force microscope tips for imaging insulators and polar molecules: tip characterization and imaging mechanisms

We demonstrate that using metallic tips for noncontact atomic force microscopy (NC-AFM) imaging at relatively large (>0.5 nm) tip-surface separations provides a reliable method for studying molecules on insulating surfaces with chemical resolution and greatly reduces the complexity of interpreting experimental data. The experimental NC-AFM imaging and theoretical simulations were carried out for the NiO(001) surface as well as adsorbed CO and Co-Salen molecules using Cr-coated Si tips. The experimental results and density functional theory calculations confirm that metallic tips possess a permanent electric dipole moment with its positive end oriented toward the sample. By analyzing the experimental data, we could directly determine the dipole moment of the Cr-coated tip. A model representing the metallic tip as a point dipole is described and shown to produce NC-AFM images of individual CO molecules adsorbed onto NiO(001) in good quantitative agreement with experimental results. Finally, we discuss methods for characterizing the structure of metal-coated tips and the application of these tips to imaging dipoles of large adsorbed molecules. © 2014 American Chemical Society

Sensorimotor input as a language generalisation tool: a neurorobotics model for generation and generalisation of noun-verb combinations with sensorimotor inputs

The paper presents a neurorobotics cognitive model explaining the understanding and generalisation of nouns and verbs combinations when a vocal command consisting of a verb-noun sentence is provided to a humanoid robot. The dataset used for training was obtained from object manipulation tasks with a humanoid robot platform; it includes 9 motor actions and 9 objects placing placed in 6 different locations), which enables the robot to learn to handle real-world objects and actions. Based on the multiple time-scale recurrent neural networks, this study demonstrates its generalisation capability using a large data-set, with which the robot was able to generalise semantic representation of novel combinations of noun-verb sentences, and therefore produce the corresponding motor behaviours. This generalisation process is done via the grounding process: different objects are being interacted, and associated, with different motor behaviours, following a learning approach inspired by developmental language acquisition in infants. Further analyses of the learned network dynamics and representations also demonstrate how the generalisation is possible via the exploitation of this functional hierarchical recurrent network

Initial stage of para-hexaphenyl thin-film growth controlled by the step structure of the ion-beam-modified TiO2TiO_{2} (110) surface

Organic electronics require a precise control over properties of a moleculeâEuro"substrate interface as well as film growth morphology, from both fundamental points of view, when a clean vacuum environment is needed and also under ambient air conditions. In this paper, we present submonolayer molecular films of para-hexaphenyl (6P) formation on the rutile TiO2(110) substrates and ways of affecting the growth and morphology via ion-beam nanopatterning. Ultrahigh vacuum deposition and measurements are followed by the film evolution study upon air exposure. Strongly anisotropic TiO2(110) surfaces, in the form of terraced ripples with a preserved (1 Ã- 1) structure, were controllably fabricated utilizing ion-beam bombardment and characterized by means of high-resolution scanning tunneling microscopy and low-energy electron diffraction. 6P thin films were prepared using organic molecular beam epitaxy and characterized in situ by noncontact atomic force microscopy. Ex situ characterization was performed by tapping-mode atomic force microscopy, scanning electron microscopy, and noncontact atomic force microscopy with molecular resolution. We have demonstrated that by changing the size of locally preserved (1 Ã- 1) surface areas, determined by the ripple parameters, different 6P assemblies can be promoted. With theStage of para-Hexaphenyl Thin-Film Growth Controlled by the Step Structure of the Ion-Beam-Modified TiO2(110) Surfaceaccompanied by a reorientation of the molecules from flat-lying to upright-standing. The resulting morphology depends on the structure of a two-dimensional phase of lying molecules formed at the initial stage of deposition, which can be either a well-ordered wetting layer or a two-dimensional mobile lattice gas. The postgrowth remainders of these two-dimensional phases participate in additional nucleation processes forming small islands or clusters

Altering the Properties of Graphene on Cu(111) by Intercalation of Potassium Bromide

The catalytic growth on transition metal surfaces provides a clean and controllable route to obtain defect-free, monocrystalline graphene. However, graphene's optical and electronic properties are diminished by the interaction with the metal substrate. One way to overcome this obstacle is the intercalation of atoms and molecules decoupling the graphene and restoring its electronic structure. We applied noncontact atomic force microscopy to study the structural and electric properties of graphene on clean Cu(111) and after the adsorption of KBr or NaCl. By means of Kelvin probe force microscopy, a change in graphene's work function has been observed after the deposition of KBr, indicating a changed graphene-substrate interaction. Further measurements of single-electron charging events as well as X-ray photoelectron spectroscopy confirmed an electronic decoupling of the graphene islands by KBr intercalation. The results have been compared with density functional theory calculations, supporting our experimental findings

Electrospray deposition of structurally complex molecules revealed by atomic force microscopy

Advances in organic chemistry allow the synthesis of large, complex and highly functionalized organic molecules having potential applications in optoelectronics, molecular electronics and organic solar cells. Their integration into devices as individual components or highly ordered thin-films is of paramount importance to address these future prospects. However, conventional sublimation techniques in vacuum are usually not applicable since large organic compounds are often non-volatile and decompose upon heating. Here, we prove by atomic force microscopy and scanning tunneling microscopy, the structural integrity of complex organic molecules deposited onto an Au(111) surface using electrospray ionisation deposition. High resolution AFM measurements with CO-terminated tips unambiguously reveal their successful transfer from solution to the gold surface in ultra-high vacuum without degradation of their chemical structures. Furthermore, the formation of molecular structures from small islands to large and highly-ordered self-assemblies of those fragile molecules is demonstrated, confirming the use of electrospray ionisation to promote also on-surface polymerization reactions of highly functionalized organic compounds, biological molecules or molecular magnets