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Viscous electron flow exhibits exotic signatures such as superballistic conduction. Bending the geometry of the device is a must to observe hydrodynamic effects. To this end, we build three antidot graphene superlattices with different hole diameters. We measure their electrical properties at various temperatures and under the effect of a perpendicular magnetic field. We find an enhanced superballistic effect, suggesting the effectiveness of the geometry at bending the electron flow. In addition, superballistic conduction behaves non-monotonically with the magnetic field, which is related with the ballistic-hydrodynamic transition. We also analyze the device resistance as a function of the size of the antidot superlattice to find characteristic scaling laws describing the different transport regimes. We prove that the antidot superlattice is a convenient geometry for realizing hydrodynamic flow, and the experiment provides valuable explanations for the technologically relevant effects of superballistic conduction and scaling laws.

The successful application of titanium oxide-graphene hybrids in the fields of photocatalysis, photovoltaics and photodetection strongly depends on the interfacial contact between both materials. The need to provide a good coupling between the enabling conductor and the photoactive phase prompted us to directly grow conducting graphenic structures on TiO2 crystals. We here report on the direct synthesis of tailored graphenic structures by using Plasma Assisted Chemical Vapour Deposition that present a clean junction with the prototypical titanium oxide (110) surface. Chemical analysis of the interface indicates chemical bonding between both materials. Photocurrent measurements under UV light illumination manifest that the charge transfer across the interface is efficient. Moreover, the influence of the synthesis atmosphere, gas precursor (C2H2) and diluents (Ar, O2), on the interface and on the structure of the as-grown graphenic material is assessed. The inclusion of O2 promotes vertical growth of partially oxidized carbon nanodots/rods with controllable height and density. The deposition with Ar results in continuous graphenic films with low resistivity (6.8x10-6 ohm x m). The synthesis protocols developed here are suitable to produce tailored carbon-semiconductor structures on a variety of practical substrates as thin films, pillars or nanoparticles., Comment: 24 pages, 8 figures, original research paper

The activation and subsequent covalent coupling of polycyclic aromatic hydrocarbons (PAHs) are of great interest in fields like chemistry, energy, biology, or health, among others. However, this is not a trivial process. So far, it is based on the use of catalysts that drive and increase the efficiency of the reaction. Here, we report on an unprecedented method in which the dehydrogenation and covalent coupling is thermally activated in the presence of atomic hydrogen and a surface. This mechanism, which requires of the superhydrogenation of the PAHs, has been characterized by high-resolution scanning tunnelling microscopy (STM) and rationalized by density functional theory (DFT) calculations. This work opens a door toward the formation of covalent, PAH-based, macromolecular nanostructures on low-reactive surfaces, thus facilitating its applicability., Comment: This manuscript version is made available under the CC-BY-NC-ND 4.0 license

Technologically useful and robust graphene-based interfaces for devices require the introduction of highly selective, stable, and covalently bonded functionalities on the graphene surface, whilst essentially retaining the electronic properties of the pristine layer. This work demonstrates that highly controlled, ultrahigh vacuum covalent chemical functionalization of graphene sheets with a thiol-terminated molecule provides a robust and tunable platform for the development of hybrid nanostructures in different environments. We employ this facile strategy to covalently couple two representative systems of broad interest: metal nanoparticles, via S-metal bonds, and thiol-modified DNA aptamers, via disulfide bridges. Both systems, which have been characterized by a multi-technique approach, remain firmly anchored to the graphene surface even after several washing cycles. Atomic force microscopy images demonstrate that the conjugated aptamer retains the functionality required to recognize a target protein. This methodology opens a new route to the integration of high-quality graphene layers into diverse technological platforms, including plasmonics, optoelectronics, or biosensing. With respect to the latter, the viability of a thiol-functionalized chemical vapor deposition graphene-based solution-gated field-effect transistor array was assessed.

Recent research on deep learning, a set of machine learning techniques able to learn deep architectures, has shown how robotic perception and action greatly benefits from these techniques. In terms of spacecraft navigation and control system, this suggests that deep architectures may be considered now to drive all or part of the on-board decision making system. In this paper this claim is investigated in more detail training deep artificial neural networks to represent the optimal control action during a pinpoint landing, assuming perfect state information. It is found to be possible to train deep networks for this purpose and that the resulting landings, driven by the trained networks, are close to simulated optimal ones. These results allow for the design of an on-board real time optimal control system able to cope with large sets of possible initial states while still producing an optimal response.

Finding the optimal ordering of k-subsets with respect to an objective function is known to be an extremely challenging problem. In this paper we introduce a new objective for this task, rooted in the problem of star identification on spacecrafts: subsets of detected spikes are to be generated in an ordering that minimizes time to detection of a valid star constellation. We carry out an extensive analysis of the combinatorial optimization problem, and propose multiple algorithmic solutions, offering different quality-complexity trade-offs. Three main approaches are investigated: exhaustive search (branch and prune), goal-driven (greedy scene elimination, minimally intersecting subsets), and stateless algorithms which implicitly seek to satisfy the problem's goals (pattern shifting, base unrank). In practical terms, these last algorithms are found to provide satisfactory approximations to the ideal performance levels, at small computational costs.