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Chongqing, China

Chongqing University of Technology ,formerly Chongqing Institute of Technology , is a public university located in Chongqing, China. Wikipedia.

This paper reported a Hg(II) sensing nanocomposite using a rhodamine derivitive as the sensing probe and an up-conversion NaYF4 lattice as the excitation core. The nanocomposite was covered by silica shell to improve the dispersibility in aqueous solutions. The obtained sample was fully characterized using electron microscopy, XRD analysis, IR spectra, thermogravimetry, UV-Vis absorption and emission spectra. Experimental results suggested that the Hg(II) sensing nanocomposite was successfully constructed, with the efficient energy transfer from the excitation core to the sensing probe confirmed. The emission of the nanocomposite increased with the increasing Hg(II) concentration, showing emission "ff-on" effect. A linear response was obtained. In addition, it was found that the emission of the nanocomposite owned good photostability under continuous radiation and high selectivity towards Hg(II) ion. © 2014 Elsevier B.V.

Hu H.-J.,Chongqing University of Technology
Journal of Manufacturing Processes | Year: 2012

It is important to research the effect of die structures parameters for equal channel angular extrusion (ECAE) on the deformation behavior, strain distribution and loads requirement. ECAE is an extrusion process widely researched for its potential to produce ultra-fine grained microstructures in magnesium alloys. In this paper some three-dimensional (3D) geometric models with different corner angles 90° and 135° and with fillets or not in the bottom die were designed by UG software. Some important isothermal process parameters were regarded as basal conditions used in DEFORM™-3D software such as temperatures, the friction coefficient, and extrusion speed. The deformation heterogeneity of ECAE was analyzed from the simulation and experimental results. The deformation homogeneity caused by the EACE die with fillets was improved comparing with the die without fillets. But the cumulative maximum strains decreased. The requirement extrusion force decreased with the fillets and channel angle increase in ECAE die. From the simulation and experimental results the smaller channel angle can obtain the higher cumulative strains and produce tinier subgrains. The loads of top die decrease mainly with fillets. The ECAE die with the channel angle 90° and fillets is good to improve the plasticity and deformation homogeneity of the billets if the extrusion force is enough. It was demonstrated that the simulation results were in good agreement with experimental results and the theoretical calculation. © 2011 The Society of Manufacturing Engineers.

Wang Z.-Y.,Chongqing University of Technology
Physical Review E - Statistical, Nonlinear, and Soft Matter Physics | Year: 2016

Despite the ubiquitous character and relevance of the electric double layer in the entire realm of interface and colloid science, very little is known of the effect that surface heterogeneity exerts on the underlying mechanisms of ion adsorption. Herein, computer simulations offer a perspective that, in sharp contrast to the homogeneously charged surface, discrete groups promote multivalent counterion binding, leading to charge reversal but possibly having not a sign change of the electrophoretic mobility. Counterintuitively, the introduction of dielectric images yields a significantly greater accumulation of counterions, which further facilitates the magnitude of charge reversal. The reported results are very sensitive to both the degree of ion hydration and the representation of surface charges. Our findings shed light on the mechanism for charge reversal over a broad range of coupling regimes operating the adsorption of counterions through surface group bridging attraction with their own images and provide opportunities for experimental studies and theoretical development. © 2016 American Physical Society.

Zhang C.,Chongqing University of Technology
Organic and Biomolecular Chemistry | Year: 2014

The incorporation of fluorine-containing moieties into organic compounds is of great importance in pharmaceutical, agricultural, and materials science. Within these organofluorides, the trifluoromethyl group is one of the most important motifs. In recent years, the trifluoromethyl group has attracted more and more attention, and many trifluoromethylated compounds have been found to possess special activities. However, until now, only a few methods have been developed to achieve this efficiently using Umemoto's reagents. This review highlights recent developments in the direct introduction of a trifluoromethyl group into organic compounds with Umemoto's reagents. Seven approaches to the trifluoromethylation of organic compounds are summarized: (i) trifluoromethylation of arenes, (ii) trifluoromethylation of alkenes, (iii) trifluoromethylation of terminal alkynes, (iv) deoxygenative trifluoromethylation of benzylic xanthates, (v) trifluoromethylation of ketoesters, (vi) trifluoromethylation of aryl boronic acids and aromatic amines (synthesis of ArCF3) and (vii) trifluoromethylation of biphenyl isocyanide derivatives. This journal is © the Partner Organisations 2014.

Su L.-y.,Chongqing University of Technology
Computers and Mathematics with Applications | Year: 2010

To improve the prediction accuracy of complex multivariate chaotic time series, a novel scheme formed on the basis of multivariate local polynomial fitting with the optimal kernel function is proposed. According to Takens Theorem, a chaotic time series is reconstructed into vector data, multivariate local polynomial regression is used to fit the predicted complex chaotic system, then the regression model parameters with the least squares method based on embedding dimensions are estimated,and the prediction value is calculated. To evaluate the results, the proposed multivariate chaotic time series predictor based on multivariate local polynomial model is compared with a univariate predictor with the same numerical data. The simulation results obtained by the Lorenz system show that the prediction mean squares error of the multivariate predictor is much smaller than the univariate one, and is much better than the existing three methods. Even if the last half of the training data are used in the multivariate predictor, the prediction mean squares error is smaller than that of the univariate predictor. © 2009 Elsevier Ltd. All rights reserved.

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