4.7 Article

Single gold nanocluster probe-based fluorescent sensor array for heavy metal ion discrimination

Journal

JOURNAL OF HAZARDOUS MATERIALS
Volume 405, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.jhazmat.2020.124259

Keywords

Gold Nanoclusters; Fluorescence; Heavy metal ions; Sensor array

Funding

  1. National Natural Science Foundation of China [21675024, 21804021]
  2. Program for Innovative Leading Talents in Fujian Province [2016B016]
  3. Science and Technology Project of Fujian Province [2018L3008]

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A fluorescent sensor array based on a gold nanocluster probe was developed for the rapid discrimination of heavy metal ions by changing the pH value, successfully identifying seven HMIs with 100% accuracy. This study provides a simple yet powerful sensing platform for multiple HMIs and shows broad application prospects in environmental monitoring.
There is a continuing high demand to design effective sensors for the determination of heavy metal ions (HMIs) since they are hazardous to both human health and the environment. In this study, we reported a facile fluorescent sensor array for rapid discrimination of HMIs based on a single gold nanocluster (AuNC) probe. This AuNC probe was prepared by using 2-mercapto-l-methylimidazole (MMI) as a ligand and polyvinypyrrolidone (PVP) as a dispersing agent. The fluorescence emission of PVP/MMI-AuNC was observed to be closely related to the pH value of the aqueous solution, which displays yellow (lambda(max) = 512 nm) and red (lambda(max) = 700 nm) fluorescence at pH 12.0 and 6.0, respectively. Further experiments indicated that different HMIs can produce differential effects on the photoluminescence of PVP/MMI-AuNC and thus generate distinct fluorescent responses at 512 and 700 nm. On the basis of this phenomenon, a fluorescent sensor array based on the PVP/MMI-AuNC was then built by simply changing pH value in the sensor element. A total of seven HMIs had their unique response patterns and were successfully distinguished by hierarchical cluster analysis and linear discriminant analysis both in buffer solution and spiked water samples, achieving 100% identification accuracy. This study provides a simple and powerful fingerprinting sensing platform for multiple HMIs, showing broad application prospects in the field of environmental monitoring.

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