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.
引用
收藏
页码:15134 / 15143
页数:10
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