Colorimetry technique for scalable characterization of suspended graphene

Previous statistical studies on the mechanical properties of chemical-vapor-deposited (CVD) suspended graphene membranes have been performed by means of measuring individual devices or with techniques that affect the material. Here, we present a colorimetry technique as a parallel, non-invasive, and affordable way of characterizing suspended graphene devices. We exploit Newton rings interference patterns to study the deformation of a double-layer graphene drum 13.2 micrometer in diameter when a pressure step is applied. By studying the time evolution of the deformation, we find that filling the drum cavity with air is 2-5 times slower than when it is purged

Knock-on damage in bilayer graphene: Indications for a catalytic pathway

We study by high-resolution transmission electron microscopy the structural response of bilayer graphene to electron irradiation with energies below the knock-on damage threshold of graphene. We observe that one type of divacancy, which we refer to as the butterfly defect, is formed for radiation energies and doses for which no vacancies are formed in clean monolayer graphene. By using first principles calculations based on density-functional theory, we analyze two possible causes related with the presence of a second layer that could explain the observed phenomenon: an increase of the defect stability or a catalytic effect during its creation. For the former, the obtained formation energies of the defect in monolayer and bilayer systems show that the change in stability is negligible. For the latter, ab initio molecular dynamics simulations indicate that the threshold energy for direct expulsion does not decrease in bilayer graphene as compared with monolayer graphene, and we demonstrate the possibility of creating divacancies through catalyzed intermediate states below this threshold energy. The estimated cross section agrees with what is observed experimentally. Therefore, we show the possibility of a catalytic pathway for creating vacancies under electron radiation below the expulsion threshold energy. © 2013 American Physical Society

SDRS: a new lossless dimensionality reduction for text corpora

In recent years, most content-based spam filters have been implemented using Machine Learning (ML) approaches by means of token-based representations of textual contents. After introducing multiple performance enhancements, the impact has been virtually irrelevant. Recent studies have introduced synset-based content representations as a reliable way to improve classification, as well as different forms to take advantage of semantic information to address problems, such as dimensionality reduction. These preliminary solutions present some limitations and enforce simplifications that must be gradually redefined in order to obtain significant improvements in spam content filtering. This study addresses the problem of feature reduction by introducing a new semantic-based proposal (SDRS) that avoids losing knowledge (lossless). Synset-features can be semantically grouped by taking advantage of taxonomic relations (mainly hypernyms) provided by BabelNet ontological dictionary (e.g. “Viagra” and “Cialis” can be summarized into the single features “anti-impotence drug”, “drug” or “chemical substance” depending on the generalization of 1, 2 or 3 levels). In order to decide how many levels should be used to generalize each synset of a dataset, our proposal takes advantage of Multi-Objective Evolutionary Algorithms (MOEA) and particularly, of the Non-dominated Sorting Genetic Algorithm (NSGA-II). We have compared the performance achieved by a Naïve Bayes classifier, using both token-based and synset-based dataset representations, with and without executing dimensional reductions. As a result, our lossless semantic reduction strategy was able to find optimal semantic-based feature grouping strategies for the input texts, leading to a better performance of Naïve Bayes classifiers.info:eu-repo/semantics/acceptedVersio

Multi-objective evolutionary optimization for dimensionality reduction of texts represented by synsets

Despite new developments in machine learning classification techniques, improving the accuracy of spam filtering is a difficult task due to linguistic phenomena that limit its effectiveness. In particular, we highlight polysemy, synonymy, the usage of hypernyms/hyponyms, and the presence of irrelevant/confusing words. These problems should be solved at the pre-processing stage to avoid using inconsistent information in the building of classification models. Previous studies have suggested that the use of synset-based representation strategies could be successfully used to solve synonymy and polysemy problems. Complementarily, it is possible to take advantage of hyponymy/hypernymy-based to implement dimensionality reduction strategies. These strategies could unify textual terms to model the intentions of the document without losing any information (e.g., bringing together the synsets “viagra”, “ciallis”, “levitra” and other representing similar drugs by using “virility drug” which is a hyponym for all of them). These feature reduction schemes are known as lossless strategies as the information is not removed but only generalised. However, in some types of text classification problems (such as spam filtering) it may not be worthwhile to keep all the information and let dimensionality reduction algorithms discard information that may be irrelevant or confusing. In this work, we are introducing the feature reduction as a multi-objective optimisation problem to be solved using a Multi-Objective Evolutionary Algorithm (MOEA). Our algorithm allows, with minor modifications, to implement lossless (using only semantic-based synset grouping), low-loss (discarding irrelevant information and using semantic-based synset grouping) or lossy (discarding only irrelevant information) strategies. The contribution of this study is two-fold: (i) to introduce different dimensionality reduction methods (lossless, low-loss and lossy) as an optimization problem that can be solved using MOEA and (ii) to provide an experimental comparison of lossless and low-loss schemes for text representation. The results obtained support the usefulness of the low-loss method to improve the efficiency of classifiers.info:eu-repo/semantics/publishedVersio

Stability of graphene doping with MoO<inf>3</inf>and I<inf>2</inf>

We dope graphene by evaporation of MoO_3 or by solution-deposition of I_2 and assess the doping stability for its use as transparent electrodes. Electrical measurements show that both dopants increase the graphene sheet conductivity and find that MoO_3-doped graphene is significantly more stable during thermal cycling. Raman spectroscopy finds that neither dopant creates defects in the graphene lattice. In-situ photoemission determines the minimum necessary thickness of MoO_3 for full graphene doping.This is the author's accepted manuscript. Copyright (2014) American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics. The following article appeared in Applied Physics Letters (volume 105) and may be found at http://scitation.aip.org/content/aip/journal/apl/105/10/10.1063/1.489502

Graphene mechanical pixels for Interferometric MOdulator Displays (GIMOD)

Graphene, the carbon monolayer and 2D allotrope of graphite, has the potential to impact technology with a wide range of applications such as optical modulators for high-speed communications. In contrast to modulation devices that rely on plasmonic or electronic effects, MEMS-based modulators can have wider tuning ranges albeit at a lower operating frequency. These properties make electro-optic mechanical modulators ideal for reflective-type display technologies as has been demonstrated previously with SiN membranes in Interferometric MOdulator Displays (IMODs). Despite their low-power consumption and performance in bright environments, IMODs suffer from low frame rates and limited color gamut. Double-layer graphene (DLG) membranes grown by chemical vapor deposition (CVD) can also recreate the interference effect like in IMODs as proven with drumheads displaying Newton's rings. Here, we report on the electro-optical response of CVD DLG mechanical pixels by measuring the change in wavelength-dependent reflectance of a suspended graphene drumhead as a function of electrical gating. We use a spectrometer to measure the wavelength spectrum at different voltages, and find a good agreement with a model based on light interference. Moreover, to verify that gas compression effects do not play an important role, we use a stroboscopic illumination technique to study the electro-optic response of these graphene pixels at frequencies up to 400 Hz. Based on these findings, we demonstrate a continuous full-spectrum reflective-type pixel technology with a Graphene Interferometric MOdulator Display (GIMOD) prototype of 2500 pixels per inch (ppi) equivalent to more than 12K resolution.Comment: 13 pages, 4 figure

Correction to: The Role of Adsorbed and Subsurface Carbon Species for the Selective Alkyne Hydrogenation Over a Pd-Black Catalyst: An Operando Study of Bulk and Surface

The selective hydrogenation of propyne over a Pd-black model catalyst was investigated under operando conditions at 1 bar making use of advanced X-ray diffraction (bulk sensitive) and photo-electron spectroscopy (surface sensitive) techniques. It was found that the population of subsurface species controls the selective catalytic semi-hydrogenation of propyne to pro-pylene due to the formation of surface and near-surface PdCx that inhibits the participation of more reactive bulk hydrogen in the hydrogenation reaction. However, increasing the partial pressure of hydrogen reduces the population of PdCx with the concomitant formation of a β-PdHx phase up to the surface, which is accompanied by a lattice expansion, allowing the participation of more active bulk hydrogen which is responsible for the unselective total alkyne hydrogenation. Therefore, controlling the surface and subsurface catalyst chemistry is crucial to control the selective alkyne semi-hydrogenation