A facile method for the selective decoration of graphene defects based on a galvanic displacement reaction

Inherent defects, such as grain boundaries (GBs), wrinkles and structural cracks, present on chemical vapor deposition (CVD)-grown graphene are inevitable because of the mechanism used for its synthesis. Because graphene defects are detrimental to electrical transport properties and degrade the performance of graphene-based devices, a defect-healing process is required. We report a simple and effective approach for enhancing the electrical properties of graphene by selective graphene-defect decoration with Pd nanoparticles (Pd NPs) using a wet-chemistry-based galvanic displacement reaction. According to the selective nucleation and growth behaviors of Pd NPs on graphene, several types of defects, such as GBs, wrinkles, graphene regions on Cu fatigue cracks and external edges of multiple graphene layers, were precisely confirmed via spherical aberration correction scanning transmission electron microscopy, field-emission scanning electron microscopy and atomic force microscopy imaging. The resultant Pd-NP-decorated graphene films showed improved sheet resistance. A transparent heater was fabricated using Pd-decorated graphene films and exhibited better heating performance than a heater fabricated using pristine graphene. This simple and novel approach promises the selective decoration of defects in CVD-grown graphene and further exploits the visualization of diverse defects on a graphene surface, which can be a versatile method for improving the properties of graphene.1881sciescopu

Search for jet extinction in the inclusive jet-pT spectrum from proton-proton collisions at s=8 TeV

Published by the American Physical Society under the terms of the Creative Commons Attribution 3.0 License. Further distribution of this work must maintain attribution to the author(s) and the published articles title, journal citation, and DOI.The first search at the LHC for the extinction of QCD jet production is presented, using data collected with the CMS detector corresponding to an integrated luminosity of 10.7  fb−1 of proton-proton collisions at a center-of-mass energy of 8 TeV. The extinction model studied in this analysis is motivated by the search for signatures of strong gravity at the TeV scale (terascale gravity) and assumes the existence of string couplings in the strong-coupling limit. In this limit, the string model predicts the suppression of all high-transverse-momentum standard model processes, including jet production, beyond a certain energy scale. To test this prediction, the measured transverse-momentum spectrum is compared to the theoretical prediction of the standard model. No significant deficit of events is found at high transverse momentum. A 95% confidence level lower limit of 3.3 TeV is set on the extinction mass scale

Searches for electroweak neutralino and chargino production in channels with Higgs, Z, and W bosons in pp collisions at 8 TeV

Searches for supersymmetry (SUSY) are presented based on the electroweak pair production of neutralinos and charginos, leading to decay channels with Higgs, Z, and W bosons and undetected lightest SUSY particles (LSPs). The data sample corresponds to an integrated luminosity of about 19.5 fb(-1) of proton-proton collisions at a center-of-mass energy of 8 TeV collected in 2012 with the CMS detector at the LHC. The main emphasis is neutralino pair production in which each neutralino decays either to a Higgs boson (h) and an LSP or to a Z boson and an LSP, leading to hh, hZ, and ZZ states with missing transverse energy (E-T(miss)). A second aspect is chargino-neutralino pair production, leading to hW states with E-T(miss). The decays of a Higgs boson to a bottom-quark pair, to a photon pair, and to final states with leptons are considered in conjunction with hadronic and leptonic decay modes of the Z and W bosons. No evidence is found for supersymmetric particles, and 95% confidence level upper limits are evaluated for the respective pair production cross sections and for neutralino and chargino mass values

An efficient top-down search algorithm for learning Boolean networks of gene expression

Boolean networks provide a simple and intuitive model for gene regulatory networks, but a critical defect is the time required to learn the networks. In recent years, efficient network search algorithms have been developed for a noise-free case and for a limited function class. In general, the conventional algorithm has the high time complexity of O(22k mn k+1) where m is the number of measurements, n is the number of nodes (genes), and k is the number of input parents. Here, we suggest a simple and new approach to Boolean networks, and provide a randomized network search algorithm with average time complexity O (mn k+1/ (log m)(k-1)). We show the efficiency of our algorithm via computational experiments, and present optimal parameters. Additionally, we provide tests for yeast expression data.close111