The ultrafine tin oxide (SnO2) nanorods/reduced graphene oxide (rGO) composites are synthesized by a two-step hydrothermal method. The prepared nanocomposites are characterized by scanning electron microscope, X-ray diffractometer, and Fourier transform infrared spectra. The results of electrochemical performance show that specific capacitance of the composite can reach 262.2 F g−1 at the current density of 100 mA g−1 in the 1 M Na2SO4 electrolyte. An initial capacitance retention of the composite is 96.1% after 6000 cycles, indicating its excellent electrochemical performance.
Composites of ultrathin SnO2 nanorods, ∼20 nm in diameter and ∼100 nm in length, intercalated with reduced graphene oxide nanosheets were synthesized by a simple one-step hydrothermal process. The electrochemical performance of the composites as electrode materials for supercapacitors was studied in 1 M Na2SO4 electrolyte. The experimental results indicated that a maximum specific capacitance of 184.6 F g−1 could be obtained from composites at a current density of 100 mA g−1, which was much higher than that of pure SnO2 (62.4 F g−1). Furthermore, the composite exhibited excellent cycling stability (the specific capacitance still retained 98% after 6000 cycles when the scan rate was 50 mV s−1). The excellent electrochemical performance of the composites was attributed to the synergistic effect of SnO2 nanorods and reduced graphene oxide, which makes up for the shortcomings of the individual components. These results indicated that the prepared composites are excellent candidates as electrode materials for high performance energy storage devices.
With the development of science and technology, intelligent facility has already become an indispensable part of our life. At the same time, shopkeepers want a convenient and inexpensive facility to cut the labor cost. To meet their needs, we decide to design an Intelligent Shelf System. The Intelligent Shelf System should include four parts: shelf management, behavior identification, data upload and image update. After the system has been tested, we find that the system can identify 98.4% of goods on average.
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