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Effects of Sulfur Introduction on the UV and the Visible Emission Properties of ZnO
Hongying Guo,Feihong Jiang,Run Yuan,Jun Zhang,Yuanping Sun,Yifan Liu,Yongxin Qiu,Taofei Zhou,Xionghui Zeng,Baoshun Zhang,Ke Xu,Hui Yang 한국물리학회 2015 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.66 No.4
Hexagonal ZnO particles have been synthesized by using the hydrothermal method with differentsulfur concentrations in the reaction solutions. Structural and optical characterizations have beenconducted to study the effects of the sulfur in the reaction solutions on the properties of thesynthesized ZnO in the ultraviolet (UV) and the visible (VIS) bands. The existance of sulfur inthe solutions can help to introduce compression strain along the a axis and an opposite trend forthe parameter c with strain inside the formed ZnO particles, which means the total strain in thesamples is presented along the c axis. The average size of the ZnO particles, as calculated fromSEM images, shows the same trend as the strain in the samples. The increasing incorporation ofsulfur causes an increase in the VIS luminescence band, which can be attributed to an increase inthe number of sulfur-induced defects.
Zhaoping Zhong,Heng Wang,Xiaoyi Wang,Feihong Guo 한국화학공학회 2016 Korean Journal of Chemical Engineering Vol.33 No.5
Flow regime identification is important in the application of fluidized beds. This paper provides a method for deciding flow regime number by objective criterion. The optimized fuzzy c-means clustering algorithm was used to cluster the flow regime classification of two-component particles in a fluidized bed. The genetic algorithm was applied to optimize the initial center clusters of fuzzy c-means clustering. Hilbert-Huang transform was applied to analyze pressure fluctuation signals and extract the characteristic parameters. Three clusters were found and respectively ascribed to three flow regimes: bubbling bed, slugging bed, and turbulent bed. A multilayer neural network was used to train and test the identification system of the flow regimes. The identification accuracies of bubbling bed, slugging bed, and turbulent bed can reach 91.67%, 92.85%, and 91.30%, respectively.