Mass production of graphene from graphite at low cost is essential for its practical application since there is huge storage of natural graphite minerals on earth. However, extracting graphite from the minerals usually involves complex and polluted purification process. Here, natural microcrystalline graphite minerals were directly used to produce high-quality graphene microsheets at high yield of > 70% through a scalable electrochemical & mechanical exfoliation approach. The graphene microsheets present the feature of small sheet sizes of 0.2 -0.6 µm 2 and < 5 atomic layers, low defects and high purity. The graphene microsheets can be highly dispersible in various solvents (the absorption coefficient of graphene microsheets dispersed in isopropanol is around 11.00 cm -1 ) and printable/paintable to make conductive films with a low sheet resistance of ~10 ohm/squ. The graphene products were used for energy-storage electrodes for supercapacitor and lithium ion battery. The supercapacitor reaches high-rate areal performance of 77 mF/cm 2 area capacity at high charge/discharge rate of 20 mA/cm 2 . Notably, graphene anode batteries have high coulombic efficiency of 99.2% and high reversible specific capacity of 390 mAh/g (after 220 cycles) at 40 mA/g and of 200 mAh/g at 595 mA/g for fast charge/discharge time of 17 min. This investigation demonstrates that graphene microsheets can be directly prepared from natural graphite minerals at high yield and low cost and potentially used for high-rate energy storage.Electronic Supplementary Information (ESI) available: Detailed TEM and SEM images, and EDS analysis for graphite minerals and graphene microsheets, and AC impedance spectra for graphene anode battery. See
Power pole detection from images is an important problem in the future electric power industry application. A precise detection is essential to inspect the defects of a power pole. In this paper, we propose a novel approach to detect the power pole object from images. Graph cut for image segmentation is a newly developing graph based image segmentation technique. It is effective but takes huge computation burden. The proposed approach combines prior knowledge with graph cut into detection. Firstly, it locates the rough region of the power pole to obtain two restricted regions based on prior rules. Then, a traditional graph cut framework is used in the restricted regions to improve the precision of segmentation. Experimental results verify its efficiency and accuracy.
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