heavy metal ions
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2022 ◽  
Vol 431 ◽  
pp. 133909
Author(s):  
Alaa Shaheen ◽  
Salwa AlBadi ◽  
Botagoz Zhuman ◽  
Hanifa Taher ◽  
Fawzi Banat ◽  
...  

2022 ◽  
Vol 176 ◽  
pp. 114304
Author(s):  
Ya-Hong Zhu ◽  
Qiang Zhang ◽  
Guo-Tao Sun ◽  
Chang-Zhou Chen ◽  
Ming-Qiang Zhu ◽  
...  

2022 ◽  
Vol 176 ◽  
pp. 114387
Author(s):  
Liping Tan ◽  
Yongchao Zhang ◽  
Weihua Zhang ◽  
Rongwen Zhao ◽  
Yue Ru ◽  
...  

Author(s):  
Yu Zhang ◽  
Yun Jia ◽  
Yaoguo Huang ◽  
Linlin Chen ◽  
Ming Wang ◽  
...  

2022 ◽  
Author(s):  
Zijun Xu ◽  
Yuying Liu ◽  
Jiao Chen ◽  
Xiyuan Wang ◽  
Hao Liu ◽  
...  

Abstract As a large amount of heavy metals leaches into water sources from industrial effluents, heavy metal pollution has become an important factor affecting water quality. To enable the detection of multiple heavy metals, we constructed a pH-regulation fluorescence sensor array. Firstly, by adding a metal chelating agent as receptor, metal ions and carbon quantum dots (CDs) were connected to distinguish between Cr6+, Fe3+, Fe2+, and Hg2+ ions. Thus, the lack of affinity between the indicator functional groups and the analyte was solved. Secondly, by adjusting the pH environment of the solution system, an economical and simple array sensing platform is established, which effectively simplified the array construction. In this study, the SX-model was used in the field of fluorescence sensor array detection for metal ion recognition. Based on the strategy of stepwise prediction, combined with the classification and concentration models, the bottleneck of the unified model in previous studies was broken. This sensor array demonstrated sensitive detection of four heavy metal ions within a concentration range from 1 to 50 µM, with an accuracy of 95.45%. Moreover, it displayed the ability to efficiently identify binary mixed samples with an accuracy of 95.45%. Furthermore, metal ions in 15 real samples (lake water) were effectively discriminated with 100% accuracy. A chelating agent was used to improve the sensitivity of heavy metal ion detection and eventually led to high-precision prediction using the SX-model.


2022 ◽  
pp. 81-114
Author(s):  
John Andraos ◽  
Albert S. Matlack

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