Issue 32, 2018

Paper-based visual detection of silver ions and l-cysteine with a dual-emissive nanosystem of carbon quantum dots and gold nanoclusters

Abstract

Fluorescence has been extensively employed for visual detection; however, it is inconvenient for people to distinguish single-color fluorescence changes. Herein, a dual-signal ratiometric fluorescent probe of a carbon quantum dot/gold nanocluster (CQD/Au NC) nanosystem for sensitive and selective detection of Ag+ and L-cysteine (Cys) was established. The CQDs with blue fluorescence at 472 nm served as a reference signal and Au NCs with orange fluorescence at 605 nm functioned as a reporter. From zeta potential measurements and TEM images, there was no indication of interaction between the two materials due to electrostatic repulsion. Upon adjusting the proportion of the CQDs and Au NCs, the nanosystem emitted a gray-orange emission at a single excitation wavelength. Ag+ enhanced the orange colour of Au NCs, but Cys could quench it completely with the high affinity of the coordination interaction between Cys and Ag+, while the blue fluorescence of CQDs was not affected. Therefore, the fluorescence of the nanosystem showed gray orange to light orange colour for determination of Ag+, and light orange to blue colour for determination of Cys. Moreover, paper-based visual detection was successfully carried out, which showed a more distinguishable color difference than that in aqueous solution. Our method has bright potential for application in the real-time and online visual detection of Ag+ and Cys in the future.

Graphical abstract: Paper-based visual detection of silver ions and l-cysteine with a dual-emissive nanosystem of carbon quantum dots and gold nanoclusters

Supplementary files

Article information

Article type
Paper
Submitted
09 May 2018
Accepted
08 Jul 2018
First published
28 Jul 2018

Anal. Methods, 2018,10, 3945-3950

Paper-based visual detection of silver ions and L-cysteine with a dual-emissive nanosystem of carbon quantum dots and gold nanoclusters

B. Han, Y. Li, X. Hu, Q. Yan, J. Jiang, M. Yu, T. Peng and G. He, Anal. Methods, 2018, 10, 3945 DOI: 10.1039/C8AY01044G

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