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).

Phosphorus and nitrogen dual-doped few-layered porous graphene: a high-performance anode material for lithium-ion batteries.

Abstract: Few-layered graphene networks composed of phosphorus and nitrogen dual-doped porous graphene (PNG) are synthesized via a MgO-templated chemical vapor deposition (CVD) using (NH4)3PO4 as N and P source. P and N atoms have been substitutionally doped in graphene networks since the doping takes place at the same time with the graphene growth in the CVD process. Raman spectra show that the amount of defects or disorders increases after P and N atoms are incorporated into graphene frameworks. The doping levels of P and N measured by X-ray photoelectron spectroscopy are 0.6 and 2.6 at %, respectively. As anodes for Li ion batteries (LIBs), the PNG electrode exhibits high reversible capacity (2250 mA h g–1 at the current density of 50 mA g–1), excellent rate capability (750 mA h g–1 at 1000 mA g–1), and satisfactory cycling stability (no capacity decay after 1500 cycles), showing much enhanced electrode performance as compared to the undoped few-layered porous graphene. Our results show that the PNG is a promisi...

Layer-by-Layer Self-assembly of Co3O4 Nanorod-Decorated MoS2 Nanosheet-Based Nanocomposite toward High-Performance Ammonia Detection.

Abstract: This article is the first demonstration of a molybdenum disulfide (MoS2)/tricobalt tetraoxide (Co3O4) nanocomposite film sensor toward NH3 detection. The MoS2/Co3O4 film sensor was fabricated on a substrate with interdigital electrodes via layer-by-layer self-assembly route. The surface morphology, nanostructure, and elemental composition of the MoS2/Co3O4 samples were examined by scanning electron microscopy, transmission electron microscopy, X-ray diffraction, energy-dispersive spectrometry, and X-ray photoelectron spectroscopy. The characterization results confirmed its successful preparation and rationality. The NH3 sensing properties of the sensor for ultra-low-concentration detection were investigated at room temperature. The experimental results revealed that high sensitivity, good repeatability, stability, and selectivity and fast response/recovery characteristics were achieved by the sensor toward NH3. Moreover, the MoS2/Co3O4 nanocomposite film sensor exhibited significant enhancement in ammonia...

Text feature extraction based on deep learning: a review

Abstract: Selection of text feature item is a basic and important matter for text mining and information retrieval. Traditional methods of feature extraction require handcrafted features. To hand-design, an effective feature is a lengthy process, but aiming at new applications, deep learning enables to acquire new effective feature representation from training data. As a new feature extraction method, deep learning has made achievements in text mining. The major difference between deep learning and conventional methods is that deep learning automatically learns features from big data, instead of adopting handcrafted features, which mainly depends on priori knowledge of designers and is highly impossible to take the advantage of big data. Deep learning can automatically learn feature representation from big data, including millions of parameters. This thesis outlines the common methods used in text feature extraction first, and then expands frequently used deep learning methods in text feature extraction and its applications, and forecasts the application of deep learning in feature extraction.