Photoluminescence Quenching in Single-layer MoS2 via Oxygen Plasma Treatment

By creating defects via oxygen plasma treatment, we demonstrate optical properties variation of single-layer MoS2. We found that, with increasing plasma exposure time, the photoluminescence (PL) evolves from very high intensity to complete quenching, accompanied by gradual reduction and broadening of MoS2 Raman modes, indicative of distortion of the MoS2 lattice after oxygen bombardment. X-ray photoelectron spectroscopy study shows the appearance of Mo6+ peak, suggesting the creation of MoO3 disordered regions in the MoS2 flake. Finally, using band structure calculations, we demonstrate that the creation of MoO3 disordered domains upon exposure to oxygen plasma leads to a direct to indirect bandgap transition in single-layer MoS2, which explains the observed PL quenching.Comment: 12 pages, 7 figure

Towards Syntactic Approximate Matching - A Pre-Processing Experiment

Over the past few years the popularity of approximate matching algorithms (a.k.a. fuzzy hashing) has increased. Especially within the area of bytewise approximate matching, several algorithms were published, tested and improved. It has been shown that these algorithms are powerful, however they are sometimes too precise for real world investigations. That is, even very small commonalities (e.g., in the header of a le) can cause a match. While this is a desired property, it may also lead to unwanted results. In this paper we show that by using simple pre-processing, we signicantly can in uence the outcome. Although our test set is based on text-based le types (cause of an easy processing), this technique can be used for other, well-documented types as well. Our results show, that it can be benecial to focus on the content of les only (depending on the use-case). While for this experiment we utilized text les, Additionally, we present a small, self-created dataset that can be used in the future for approximate matching algorithms since it is labeled (we know which les are similar and how)

Towards Syntactic Approximate Matching-A Pre-Processing Experiment

Over the past few years, the popularity of approximate matching algorithms (a.k.a. fuzzy hashing) has increased. Especially within the area of bytewise approximate matching, several algorithms were published, tested, and improved. It has been shown that these algorithms are powerful, however they are sometimes too precise for real world investigations. That is, even very small commonalities (e.g., in the header of a file) can cause a match. While this is a desired property, it may also lead to unwanted results. In this paper, we show that by using simple pre-processing, we significantly can influence the outcome. Although our test set is based on text-based file types (cause of an easy processing), this technique can be used for other, well-documented types as well. Our results show that it can be beneficial to focus on the content of files only (depending on the use-case). While for this experiment we utilized text files, additionally, we present a small, self-created dataset that can be used in the future for approximate matching algorithms since it is labeled (we know which files are similar and how)

A Heuristic Neural Network Structure Relying on Fuzzy Logic for Images Scoring

Traditional deep learning methods are sub-optimal in classifying ambiguity features, which often arise in noisy and hard to predict categories, especially, to distinguish semantic scoring. Semantic scoring, depending on semantic logic to implement evaluation, inevitably contains fuzzy description and misses some concepts, for example, the ambiguous relationship between normal and probably normal always presents unclear boundaries (normal − more likely normal - probably normal). Thus, human error is common when annotating images. Differing from existing methods that focus on modifying kernel structure of neural networks, this study proposes a dominant fuzzy fully connected layer (FFCL) for Breast Imaging Reporting and Data System (BI-RADS) scoring and validates the universality of this proposed structure. This proposed model aims to develop complementary properties of scoring for semantic paradigms, while constructing fuzzy rules based on analyzing human thought patterns, and to particularly reduce the influence of semantic conglutination. Specifically, this semantic-sensitive defuzzier layer projects features occupied by relative categories into semantic space, and a fuzzy decoder modifies probabilities of the last output layer referring to the global trend. Moreover, the ambiguous semantic space between two relative categories shrinks during the learning phases, as the positive and negative growth trends of one category appearing among its relatives were considered. We first used the Euclidean Distance (ED) to zoom in the distance between the real scores and the predicted scores, and then employed two sample t test method to evidence the advantage of the FFCL architecture. Extensive experimental results performed on the CBIS-DDSM dataset show that our FFCL structure can achieve superior performances for both triple and multiclass classification in BI-RADS scoring, outperforming the state-of-the-art methods

Detection of genetically modified maize (Zea mays L.) in seed samples from Nepal

Maize is the second major cereal in Nepal; its food biosafety and ecological conservation is an important concern. To address this issue, it is necessary to detect genetically modified (GM) maize and establish a monitoring and regulatory system in Nepal. Currently, Nepal does not have legal regulations or labeling directives for GM maize. Therefore, the authors aimed to survey the current status of GM maize seeds in Nepal. First, they performed multiplex polymerase chain reaction (mPCR) to detect 8 GM maize lines in 46 maize seed samples from different locations in Nepal. Suspected samples were then verified by real-time PCR (RT-PCR) and screen-specific PCR. Based on current evidence, they can not identify any GM maize in the seed samples. This first report may formulate and implement a baseline for quality regulation and biodiversity conservation of maize seeds in Nepal.Keywords: Genetically modified crops, maize, seeds, Nepal, multiplex polymerase chain reaction, real-time PCRAfrican Journal of Biotechnology Vol. 9(34), pp. 5581-5589, 23 August, 201

Enhanced self-field critical current density of nano-composite YBa(2)Cu(3)O(7) thin films grown by pulsed-laser deposition

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ EPLA, 2008.Enhanced self-field critical current density Jc of novel, high-temperature superconducting thin films is reported. Layers are deposited on (001) MgO substrates by laser ablation of YBa2Cu3O7−δ(Y-123) ceramics containing Y2Ba4CuMOx (M-2411, M=Ag, Nb, Ru, Zr) nano-particles. The Jc of films depends on the secondary-phase content of the ceramic targets, which was varied between 0 and 15 mol%. Composite layers (2 mol% of Ag-2411 and Nb-2411) exhibit Jc values at 77 K of up to 5.1 MA/cm2, which is 3 to 4 times higher than those observed in films deposited from phase pure Y-123 ceramics. Nb-2411 grows epitaxially in the composite layers and the estimated crystallite size is ~10 nm.The Austrian Science Fund, the Austrian Federal Ministry of Economics and Labour, the European Science Foundation and the Higher Education Commission of Pakistan

Learning a General Model of Single Phase Flow in Complex 3D Porous Media

Modeling effective transport properties of 3D porous media, such as permeability, at multiple scales is challenging as a result of the combined complexity of the pore structures and fluid physics - in particular, confinement effects which vary across the nanoscale to the microscale. While numerical simulation is possible, the computational cost is prohibitive for realistic domains, which are large and complex. Although machine learning models have been proposed to circumvent simulation, none so far has simultaneously accounted for heterogeneous 3D structures, fluid confinement effects, and multiple simulation resolutions. By utilizing numerous computer science techniques to improve the scalability of training, we have for the first time developed a general flow model that accounts for the pore-structure and corresponding physical phenomena at scales from Angstrom to the micrometer. Using synthetic computational domains for training, our machine learning model exhibits strong performance (R$^2$=0.9) when tested on extremely diverse real domains at multiple scales