We report on an experimental demonstration of graphene-metal ohmic contacts with contact resistance below 100 Ω µm. These have been fabricated on graphene wafers, both with and without hydrogen intercalation, and measured using the transmission line method. Specific contact resistivities of 3 × 10−7 to 1.2 × 10−8 Ω cm2 have been obtained. The ultra-low contact resistance yielded short-channel (source-drain distance of 0.45 µm) HfO2/graphene field effect transistors (FETs) with a low on-resistance (Ron) of 550 Ω µm and a high current density of 1.7 A/mm at a source-drain voltage of 1 V. These values represent state-of-the-art (SOA) performance in graphene-metal contacts and graphene FETs. This ohmic contact resistance is comparable to that of SOA high-speed III–V high electron mobility transistors.

[1] Both the Yangtze River Delta (YRD) and the Pearl River Delta (PRD), the two most rapidly developing areas in eastern China, have suffered from serious air pollution, and thus, numerous investigations were devoted to studying these problems. Other areas in eastern China have received less attention despite similar rapid development in their industries and economy. In this study, we analyzed air-quality data from Kinmen Island (24°27′26″N, 118°19′36″E) located off Fujian Province and between the two above-mentioned deltas. Our results clearly show that the study area is experiencing serious air quality deterioration. Particularly, high levels of suspended particulate matter (PM) were observed during winter, when the northeasterly monsoon prevails. For example, concentrations of wintertime PM 10 (particles ≤ 10 mm in diameter) frequently exceeded 100 mg/m 3 in the last three years. In addition to the air-quality data analysis, aerosol samples were collected between 22 November 2007 and 6 March 2008 and subjected to chemical analyses of various species. Our findings show that the three principal PM components include organic, mineral, and sulfate species with moderate to minor fractions of nitrate, sea salt, elemental carbon, and trace metal oxides. The high PM levels observed over the island may be partly attributed to the transport from a mixed-type industrial area located ∼40 km northeast of Kinmen. Our study could partially fill the air quality data gap between the YRD and PRD regions, and highlight the alarming fact that air pollution has gradually expanded along eastern China's coastal zone.Citation: Hsu, S. C., et al. (2010), High wintertime particulate matter pollution over an offshore island (Kinmen) off

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High-speed railways have been one of the most popular means of transportation all over the world. As an important part of the high-speed railway power supply system, the overhead catenary system (OCS) directly influences the stable operation of the railway, so regular inspection and maintenance are essential. Now manual inspection is too inefficient and high-cost to fit the requirements for high-speed railway operation, and automatic inspection becomes a trend. The 3D information in the point cloud is useful for geometric parameter measurement in the catenary inspection. Thus it is significant to recognize the components of OCS from the point cloud data collected by the inspection equipment, which promotes the automation of parameter measurement. In this paper, we present a novel method based on deep learning to recognize point clouds of OCS components. The method identifies the context of each single frame point cloud by a convolutional neural network (CNN) and combines some single frame data based on classification results, then inputs them into a segmentation network to identify OCS components. To verify the method, we build a point cloud dataset of OCS components that contains eight categories. The experimental results demonstrate that the proposed method can detect OCS components with high accuracy. Our work can be applied to the real OCS components detection and has great practical significance for OCS automatic inspection.