Issue 32, 2022

A highly selective molecularly imprinted electrochemical sensor with anti-interference based on GO/ZIF-67/AgNPs for the detection of p-cresol in a water environment

Abstract

p-Cresol is a harmful phenolic substance that can cause serious effects on human health even at a low concentration in water. Therefore, the detection of p-cresol in a water environment is particularly important. In this paper, a novel zeolite imidazolate framework-67 (ZIF-67) material with regular morphology was prepared on the surface of graphene oxide doped with silver nanoparticles. The composite was modified on the glassy carbon electrode surface to increase the specific surface area, accelerate the electron transfer rate, enhance the current response and improve the performance of electrochemical sensors. Furthermore, a layer of p-cresol-molecularly imprinted polymer was prepared on the surface of the modified electrode by electropolymerization for the selective, rapid and sensitive detection of p-cresol, which greatly improved the specific recognition of p-cresol. Under optimal conditions, the prepared sensor had a good linear range of 1.0 × 10−10 M to 1.0 × 10−5 M with a detection limit as low as 5.4 × 10−11 M, and it presented excellent reproducibility, stability and selectivity. Moreover, the sensor was successfully applied for the detection of trace p-cresol in a real water environment, providing a reliable assay for sensitive, rapid and selective detection of p-cresol in complex samples.

Graphical abstract: A highly selective molecularly imprinted electrochemical sensor with anti-interference based on GO/ZIF-67/AgNPs for the detection of p-cresol in a water environment

Supplementary files

Article information

Article type
Paper
Submitted
05 Jun 2022
Accepted
19 Jul 2022
First published
19 Jul 2022

Anal. Methods, 2022,14, 3079-3086

A highly selective molecularly imprinted electrochemical sensor with anti-interference based on GO/ZIF-67/AgNPs for the detection of p-cresol in a water environment

S. Han, R. Sun, F. Teng, Y. Wang, H. Chu, W. Zong, Y. Chen and Z. Sun, Anal. Methods, 2022, 14, 3079 DOI: 10.1039/D2AY00911K

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