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Graphene is a very attractive material for nanomechanical devices and membrane applications. Graphene blisters based on silicon oxide micro-cavities are a simple but relevant example of nanoactuators. A drawback of this experimental set up is that gas leakage through the graphene-SiO2 interface contributes significantly to the total leak rate. Here we study the diffusion of air from pressurized graphene drumheads on SiO2 micro-cavities and propose a straightforward method to improve the already strong adhesion between graphene and the underlying SiO2 substrate, resulting in reduced leak rates. This is carried out by applying controlled and localized ultrahigh pressure (> 10 GPa) with an Atomic Force Microscopy diamond tip. With this procedure, we are able to significantly approach the graphene layer to the SiO2 surface around the drumheads, thus enhancing the interaction between them allowing us to better seal the graphene-SiO2 interface, which is reflected in up to ~ 4 times lower leakage rates. Our work opens an easy way to improve the performance of graphene as a gas membrane on a technological relevant substrate such as SiO2., Comment: pages 19, 4 figures + supplementary information

Contrary to most materials, graphene exhibits a negative thermal expansion coefficient (TEC), i.e it contracts when heated. This contraction is due to the thermal excitation of low energy out-of-plane vibration modes. These flexural modes have been reported to govern the electronic transport and the mechanical response of suspended graphene. In this work, we systematically investigate the influence of defects in the TEC of suspended graphene membranes. Controlled introduction of low densities of mono-vacancies reduces the graphene TEC, up to one order of magnitude for a defect density of $5\times10^{12}$~cm$^{-2}$ . Our molecular dynamics simulations reproduce the observed trend and show that TEC reduction is due to the suppression of out-of-plane fluctuations caused by the strain fields created by mono-vacancies in their surrounding areas. These results highlight the key role of defects in the properties of "real-life" graphene, and pave the way for future proposals of electronic and mechanical defect engineering., Comment: 19

The purpose of this research is to study the connection of global properties of eight young stellar clusters projected in the Vista Variables in the Via Lactea (VVV) ESO Large Public Survey disk area and their young stellar object population. The analysis in based on the combination of spectroscopic parallax-based reddening and distance determinations with main sequence and pre-main sequence ishochrone fitting to determine the basic parameters (reddening, age, distance) of the sample clusters. The lower mass limit estimations show that all clusters are low or intermediate mass (between 110 and 1800 Mo), the slope Gamma of the obtained present-day mass functions of the clusters is close to the Kroupa initial mass function. On the other hand, the young stellar objects in the surrounding cluster's fields are classified by low resolution spectra, spectral energy distribution fit with theoretical predictions, and variability, taking advantage of multi-epoch VVV observations. All spectroscopically confirmed young stellar objects (except one) are found to be massive (more than 8 Mo). Using VVV and GLIMPSE color-color cuts we have selected a large number of new young stellar object candidates, which are checked for variability and 57% are found to show at least low-amplitude variations. In few cases it was possible to distinguish between YSO and AGB classification on the basis of the light curves., Comment: 45 pages; 17 figures; published in AJ

We present the first physical characterization of the young open cluster VVV CL041. We spectroscopically observed the cluster main-sequence stellar population and a very-massive star candidate: WR62-2. CMFGEN modeling to our near-infrared spectra indicates that WR62-2 is a very luminous (10$^{6.4\pm0.2} L_{\odot}$) and massive ($\sim80 M_{\odot}$) star., Comment: 1 page, 1 figure, to be published in the "International Workshop on Wolf-Rayet Stars conference proceedings" by Universit\"atsverlag Potsdam (editors: W.-R. Hamann, A. Sander, and H. Todt)

We present an adaptive multi-GPU Exchange Monte Carlo method designed for the simulation of the 3D Random Field Model. The algorithm design is based on a two-level parallelization scheme that allows the method to scale its performance in the presence of faster and GPUs as well as multiple GPUs. The set of temperatures is adapted according to the exchange rate observed from short trial runs, leading to an increased exchange rate at zones where the exchange process is sporadic. Performance results show that parallel tempering is an ideal strategy for being implemented on the GPU, and runs between one to two orders of magnitude with respect to a single-core CPU version, with multi-GPU scaling being approximately $99\%$ efficient. The results obtained extend the possibilities of simulation to sizes of $L = 32, 64$ for a workstation with two GPUs., Comment: 15 pages, 10 figures

Context. The Vista Variables in the V\'ia L\'actea (VVV) ESO Public Survey is a variability survey of the Milky Way bulge and an adjacent section of the disk carried out from 2010 on ESO Visible and Infrared Survey Telescope for Astronomy (VISTA). VVV will eventually deliver a deep near-IR atlas with photometry and positions in five passbands (ZYJHK_S) and a catalogue of 1-10 million variable point sources - mostly unknown - which require classifications. Aims. The main goal of the VVV Templates Project, that we introduce in this work, is to develop and test the machine-learning algorithms for the automated classification of the VVV light-curves. As VVV is the first massive, multi-epoch survey of stellar variability in the near-infrared, the template light-curves that are required for training the classification algorithms are not available. In the first paper of the series we describe the construction of this comprehensive database of infrared stellar variability. Methods. First we performed a systematic search in the literature and public data archives, second, we coordinated a worldwide observational campaign, and third we exploited the VVV variability database itself on (optically) well-known stars to gather high-quality infrared light-curves of several hundreds of variable stars. Results. We have now collected a significant (and still increasing) number of infrared template light-curves. This database will be used as a training-set for the machine-learning algorithms that will automatically classify the light-curves produced by VVV. The results of such an automated classification will be covered in forthcoming papers of the series., Comment: 12 pages, 16 figures, 3 tables, accepted for publication in A&A. Most of the data are now accessible through http://www.vvvtemplates.org/