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Stable Europium(III) Metal-Organic Framework Fluorescence Probe for Intelligent Visualization Detection of Gossypol and Nitrofuran Antibiotics in Real Samples
被引:6
|作者:
Su, Yu
[1
]
Guo, Yichen
[1
]
Wu, Qi
[1
]
Wang, Linxia
[1
]
Wang, Yifei
[1
]
Yang, Guoping
[1
]
Zhang, Wenyan
[1
]
Wang, Yaoyu
[1
]
机构:
[1] Northwest Univ, Coll Chem & Mat Sci, Key Lab Synthet & Nat Funct Mol, Shaanxi Key Lab Physico Inorgan Chem,Minist Educ, Xian 710127, Peoples R China
关键词:
SENSITIVE DETECTION;
SENSOR;
D O I:
10.1021/acs.inorgchem.4c02232
中图分类号:
O61 [无机化学];
学科分类号:
070301 ;
081704 ;
摘要:
Gossypol (Gsp) and antibiotics present in water bodies become organic pollutants that are harmful to human health and the ecological environment. Accurate and effective detection of these pollutants has far-reaching significance in many fields. A new three-dimensional metal-organic framework (MOF), {[Eu-3(L)(2)(HCOO-)(H2O)(3)]<middle dot>2H(2)O<middle dot>2DMF}(n) (Eu-MOF), was synthesized from 3,5-bis(2,4-dicarboxylphenyl)nitrobenzene (H4L) ligand and Eu3+ via the solvothermal method in this paper. The Eu-MOF demonstrates strong red fluorescence and can remain stable in different pH solutions. The MOF fluorescence probe could detect organic pollutants through the "shut-off" effect, with a fast response speed and a low detection limit [Gsp, nitrofurantoin (NFT), and nitrofurazone (NFZ) for 0.43, 0.38, and 0.41 mu M, respectively]. During the testing process, Eu-MOF exhibited good selectivity and recoverability. Furthermore, the mechanism of fluorescence quenching was investigated, and the recoveries were also good in real samples. This paper introduced a deep learning model to recognize the fluorescence images, a portable intelligent logic detector designed for real-time detection of Gsp by logic gate strategy, and an anticounterfeiting mark prepared based on inkjet printing. Importantly, this work provides a new way of thinking for the detection of organic pollutants in water with high sensitivity and practicality by combining the fluorescence probe with machine learning and logical judgment.
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页码:15134 / 15143
页数:10
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