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RISS 인기검색어
- 검색키워드
- 저자 : Xiaohuan Wu (검색결과 3 건)
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Wang, Weina,Wu, Qiuhua,Zang, Xiaohuan,Wang, Chun,Wang, Zhi Korean Chemical Society 2012 Bulletin of the Korean Chemical Society Vol.33 No.10
In this paper, a layered-carbon-$Fe_3O_4$ (LC-$Fe_3O_4$) hybrid material was synthesized through a facile one-pot solvothermal method and used as the adsorbent for the preconcentration of some phthalate esters (dimethyl phthalate, diethyl phthalate, diallyl phthalate, diisobutyl phthalate and benzyl butyl phthalate) in water samples. The effects of the adsorbent dosage, extraction time, the solution pH and salinity on the adsorption of the phthalate esters (PAEs) were investigated. The magnetic nanocomposite adsorbent could remove and enrich the PAEs from water samples efficiently. After the adsorption, the analytes were desorbed and then determined by high performance liquid chromatography-ultraviolet detection. Under the optimum conditions, the enrichment factors of the method for the analytes were in the range from 161 to 180. A linear response with peak area as the quantification signal was observed in the concentration range from 0.5 to $100ng\;mL^{-1}$. The limits of detection (S/N = 3) of the method were between 0.08 and $0.1ng\;mL^{-1}$. The method was suitable for the determination of trace phthalate esters in environmental water samples.
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Lin Lin,Xiaohuan Wu,Jiajin Qi,Hongxin Ci 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.1
Power quality (PQ) disturbances recognition is the foundation of power quality analysis and improvement. In order to improve the classification accuracy and efficiency, a new classification approach based on modified Fourier neural networks (FNN) and Hyperbolic S-transform (HST) was designed for PQ disturbances classification. HST has better a time-frequency resolution than S-transform. The features extracted from HST results compose the input vectors of classifier. The DFP emendatory Quasi-Newton method is used to improve the learning ability of FNN and avoid local minimum problem. Three modified FNNs were used to construct a classifier with the structure of decision tree. Six types of disturbances with different noise ratio were simulated to test the classification ability of the new approach. Simulation results show that the new classifier has better classification accuracy than other classifiers based on BP neural networks and Fourier neural networks. The new approach is effective.
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Weina Wang,Qiuhua Wu,Xiaohuan Zang,Chun Wang,Zhi Wang 대한화학회 2012 Bulletin of the Korean Chemical Society Vol.33 No.10
In this paper, a layered-carbon-Fe3O4 (LC-Fe3O4) hybrid material was synthesized through a facile one-pot solvothermal method and used as the adsorbent for the preconcentration of some phthalate esters (dimethyl phthalate, diethyl phthalate, diallyl phthalate, diisobutyl phthalate and benzyl butyl phthalate) in water samples. The effects of the adsorbent dosage, extraction time, the solution pH and salinity on the adsorption of the phthalate esters (PAEs) were investigated. The magnetic nanocomposite adsorbent could remove and enrich the PAEs from water samples efficiently. After the adsorption, the analytes were desorbed and then determined by high performance liquid chromatography-ultraviolet detection. Under the optimum conditions, the enrichment factors of the method for the analytes were in the range from 161 to 180. A linear response with peak area as the quantification signal was observed in the concentration range from 0.5 to 100 ng mL−1. The limits of detection (S/N = 3) of the method were between 0.08 and 0.1 ng mL−1. The method was suitable for the determination of trace phthalate esters in environmental water samples.
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