Issue 39, 2024

Ultra-small water-soluble fluorescent copper nanoclusters for p-nitrophenol detection

Abstract

Due to the widespread application of p-nitrophenol (p-NP) across various industries, particularly in the pharmaceutical and chemical sectors, it has emerged as a significant environmental contaminant in both soil and water ecosystems. The development of swift and sensitive detection platforms for p-NP is therefore demanding. Herein, a fluorescence sensor based on ultra-small copper nanoclusters with exterior glutathione ligands determined by electrospray ionization mass spectrometry (ESI-MS) as [Cu14(SG)12]+ (denoted as Cu-SG NCs) has been prepared in high efficiency, and shown high selectivity for p-NP detection. The Cu-SG NCs, synthesized via a facile one-pot chemical reduction technique, exhibit emission maxima at 620 nm. Notably, the introduction of p-NP into the nanocluster system causes a significant quenching of the Cu-SG NCs fluorescence. The quenching phenomenon arises predominantly as a result of the inner filter effect (IFE), which stems from the substantial overlap between the UV-Vis absorption spectrum of p-NP and the excitation wavelength of Cu-SG NCs. The developed fluorescence sensor platform demonstrates a wide determination range for p-NP, ranging from 0.04 to 2000 µM, with a detection limit of 30 nM. Additionally, the sensor efficacy was successfully validated in the analysis of actual water samples. The ease of synthesis, excellent optical properties, and low toxicity of Cu-SG NCs represent significant advantages over the reported noble metal nanomaterials and is highly promising for future practical applications.

Graphical abstract: Ultra-small water-soluble fluorescent copper nanoclusters for p-nitrophenol detection

Supplementary files

Article information

Article type
Paper
Submitted
08 Aug 2024
Accepted
04 Sep 2024
First published
09 Sep 2024

Anal. Methods, 2024,16, 6688-6695

Ultra-small water-soluble fluorescent copper nanoclusters for p-nitrophenol detection

M. Oyebanji, L. Chen, R. Qian, M. Tu, Q. Zhang, X. Yang, H. Yu and M. Zhu, Anal. Methods, 2024, 16, 6688 DOI: 10.1039/D4AY01481B

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