Yuting Guan
China University of Petroleum
17 Papers
Yuting Guan is an academic researcher from China University of Petroleum. The author has contributed to research in topics: Chemistry & Medicine. The author has an hindex of 1, co-authored 1 publications.
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Papers
Synthesis of g-C3N4 composite co-doped with CeO2 and sugar cane bagasse charcoal for the degradation of methylene blue under visible light
TL;DR: In this article , an effective solid-state pyrolysis method was employed to synthesize CeO2(Ce)/biomass carbon(C)@g-C3N4 photocatalyst, which was then used to degrade methylene blue (MB) with high efficiency under visible light irradiation.
21
A sensitive fluorescence sensor based on a glutathione modified quantum dot for visual detection of copper ions in real samples.
Zikang Hu,Wanjun Long,Ting-Kai Liu,Yuting Guan,Guanghua Lei,Yixin Suo,Mengguo Jia,Jieling He,Hengye Chen,Yuanbin She,Haiyan Fu +10 more
TL;DR: In this paper , a glutathione modified quantum dot (GSH-CdTe QDs) was synthesized and applied in a "turn-off" fluorescence probe to detect Cu2+.
21
A mathematical model for drainage and desorption area analysis during shale gas production
TL;DR: In this paper, a semi-analytical solution with dynamic gas compressibility was proposed to predict the depth of drainage and desorption area for long-term shale gas production.
14
Geographical origin and species identification of lilii bulbus using C/N/H/O stable isotopes and multi-elemental combined chemometrics
Chengying Hai,Hengye Chen,Yixin Suo,Yuting Guan,Siyu Wang,Wei Lan,Wanjun Long,Xiaolong Yang,Jian Yang,Haiyan Fu +9 more
TL;DR: In this article , Li et al. showed that stable isotopes and multi-elemental combined chemometrics provide an effective method for lilii bulbus origin and species identification, while indicating that K, Ca, Na and Mg elements could be used as effective elements for species classification.
13
Classification of Chinese traditional cereal vinegars and antioxidant property predication by fluorescence spectroscopy.
TL;DR: In this paper , a rapid and accurate strategy for classification of Chinese traditional cereal vinegars (CTCV) and antioxidant property predication was proposed by using the combination fluorescence spectroscopy and machine learning.
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