China University of Petroleum

About: China University of Petroleum is a education organization based out in Beijing, China. It is known for research contribution in the topics: Catalysis & Oil shale. The organization has 39802 authors who have published 39151 publications receiving 483760 citations. The organization is also known as: Zhōngguó Shíyóu Dàxué & China University of Petroleum (Beijing).

Characterization of a hybrid composite of SnO2 nanocrystal-decorated reduced graphene oxide for ppm-level ethanol gas sensing application

Abstract: This paper demonstrated a hybrid composite of tin oxide–reduced graphene oxide (rGO) for ppm-level detection of ethanol vapour. A facile and low-cost method of hydrothermal synthesis was used to construct the SnO2–rGO hybrid film on a PCB substrate with coil-like interdigital microelectrodes. The presence of small-sized SnO2 nanocrystals on rGO sheets was characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), an X-ray diffractometer (XRD) and Brunauer–Emmett–Teller (BET) measurements. The sensing properties of the SnO2–rGO hybrid film sensor, including the sensitivity, response–recovery times and repeatability, were investigated by exposing it to various concentrations of ethanol gas varying from 1 ppm to 100 ppm at room temperature. As a result, the presented sensor exhibited a low detection limit of 1 ppm at room temperature, as well as a fast response–recovery time and good repeatability, which outstripped that of pure rGO film sensors. Finally, the possible mechanism of ethanol gas-sensing for the above presented sensor was discussed in detail.

Efficient photocatalysts of TiO2 nanocrystals-supported PtRu alloy nanoparticles for CO2 reduction with H2O: Synergistic effect of Pt-Ru

Abstract: The photocatalytic reduction of CO2 with H2O to chemical energy-rich molecules (CH4 and CO) is significant to solve energy crisis. Herein, we have successfully fabricated the novel photocatalyst of bimetallic Pt-Ru alloy nanoparticles (NPs) selectively deposited on the {101} facet of TiO2 nanocrystal via the photon-assisted gas bubbling-membrane reduction (P-GBMR) method. The photogenerated electrons and holes are enriched on the coexposed {101}-39% and {001}-61% facets of anatase TiO2 nanocrystal, respectively. Noble metal (Pt, Ru and PtRu) NPs deposited on the TiO2-{101} facet can further improve the separation efficiency of photogenerated electron-hole pairs via the vectorial electron transfer of TiO2→PtRu. Pt/TiO2 catalytst with enriched surface photogenerated electrons shows the relative high formation rate of CH4 (22.9 μmol g−1 h−1) and H2 (52.8 μmol g−1 h−1), while Ru/TiO2 catalyst with strong adsorption/activation capability for CO2 prefers to improve the selectivity to CO product (65.1%). The possible mechanism for CO2 reduction is proposed and discussed: The formation of H-containing intermediate and the adsorption/activation property for CO2 are two important determining steps for improving photocatalytic CO2 reduction with H2O to CH4 product. PtRu/TiO2 catalyst with the synergic effect of Pt and Ru components exhibits the best catalytic performance for CO2 reduction with H2O to CH4 product under simulated solar irradiation, i.e., its formation rate of CH4 (38.7 μmol g−1 h−1) is about 29-fold of commercial P25, its selectivity to CH4 product is 93.7% and its apparent quantum efficiency for CO2 conversion is 0.98%. It expected to be a new heuristic on the development of high efficient photocatalysts for application to light-chemical energy conversion.

Influence of Sintering Temperature on Grain Growth and Phase Structure of Compositionally Optimized High‐Performance Li/Ta‐Modified (Na,K)NbO3 Ceramics

Abstract: Microstructure characteristics, phase transition, and electrical properties of (Na 0 . 535 K 0 . 485 ) 0 . 926 Li 0 . 074 (Nb 0 . 942 Ta 0 . 058 )O 3 (NKN-LT) lead-free piezoelectric ceramics prepared by normal sintering are investigated with an emphasis on the influence of sintering temperature. Some abnormal coarse grains of 20-30 μm in diameter are formed in a matrix consisting of about 2 μm fine grains when the sintering temperature was relatively low (980°C). However, only normally grown grains were observed when the sintering temperature was increased to 1020°C. On the other hand, orthorhombic and tetragonal phases coexisted in the ceramics sintered at 980°-1000°C, whereas the tetragonal phase becomes dominant when sintered above 1020°C. For the ceramics sintered at 1000°C, the piezoelectric constant d 33 is enhanced to 276 pC/N, which is a high value for the Li-and Ta-modified (Na,K)Nb0 3 ceramics system. The other piezoelectric and ferroelectric properties are as follows: planar electromechanical coupling factor k p = 46.2%, thickness electromechanical coupling factor k t = 36%, mechanical quality factor Q m = 18, remnant polarization P r = 21.1 μC/cm 2 , and coercive field E c = 1.85 kV/mm.

Combining Convolutional and Recurrent Neural Networks for Human Skin Detection

Abstract: Skin detection from images, typically used as a preprocessing step, has a wide range of applications such as dermatology diagnostics, human computer interaction designs, and etc. It is a challenging problem due to many factors such as variation in pigment melanin, uneven illumination, and differences in ethnicity geographics. Besides, age and gender introduce additional difficulties to the detection process. It is hard to determine whether a single pixel is skin or nonskin without considering the context. An efficient traditional hand-engineered skin color detection algorithm requires extensive work by domain experts. Recently, deep learning algorithms, especially convolutional neural networks (CNNs), have achieved great success in pixel-wise labeling tasks. However, CNN-based architectures are not sufficient for modeling the relationship between pixels and their neighbors. In this letter, we integrate recurrent neural networks (RNNs) layers into the fully convolutional neural networks (FCNs), and develop an end-to-end network for human skin detection. In particular, FCN layers capture generic local features, while RNN layers model the semantic contextual dependencies in images. Experimental results on the COMPAQ and ECU skin datasets validate the effectiveness of the proposed approach, where RNN layers enhance the discriminative power of skin detection in complex background situations.