Wanjun Long
42 Papers
Wanjun Long is an academic researcher. The author has contributed to research in topics: Chemistry & Medicine. The author has an hindex of 4, co-authored 21 publications.
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Papers
Detection of tetracycline antibiotics using fluorescent "Turn-off" sensor based on S, N-doped carbon quantum dots.
Yao Fan,Wenjun Qiao,Wanjun Long,Hengye Chen,Haiyan Fu,Chunsong Zhou,Yuanbin She +6 more
TL;DR: In this article , a fluorescent sensor based on S, N-doped carbon quantum dots (S, CQDs) was established for rapid detection of tetracycline antibiotics (TCs).
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Untargeted metabolomic analysis of Chinese red wines for geographical origin traceability by UPLC-QTOF-MS coupled with chemometrics.
TL;DR: In this paper , an untargeted metabolomic approach based on UPLC-QTOF-MS was established to discriminate geographical origins of Chinese red wines, which showed significant differences between wine samples from three famous geographical origins in China.
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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+.
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A sensitized ratiometric fluorescence probe based on N/S doped carbon dots and mercaptoacetic acid capped CdTe quantum dots for the highly selective detection of multiple tetracycline antibiotics in food.
TL;DR: In this article , a ratiometric fluorescent probe based on N/S doped carbon dots (N/S-CQDs) and mercaptoacetic acid capped CdTe quantum dots with sensitized and self-calibration functions was constructed to sensitively detect multiple tetracycline antibiotics (TCs).
21
Fast and non-destructive discriminating the geographical origin of Hangbaiju by hyperspectral imaging combined with chemometrics.
Wanjun Long,Qi Zhang,Siyu Wang,Yixin Suo,Hengye Chen,Xiuyun Bai,Xiaolong Yang,Yan Zhou,Jian Yang,Haiyan Fu +9 more
TL;DR: Zhang et al. as discussed by the authors used a bagging classification tree-radial basis function (BAGCT-RBFN) compared with classification tree (CT), radial basis function network (RBFN), was applied to discriminate Hangbaiju samples from different origins.
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