Issue 5, 2025

Research on a molecularly imprinted electrochemical sensor based on a graphene quantum dot-gold nanoparticle composite for the determination of 17β-estradiol

Abstract

In this study, a molecularly imprinted electrochemical sensor (MIECS) was constructed based on the combination of graphene quantum dots-gold nanoparticles (GQDs-AuNPs), molecular imprinting polymer (MIP), and electrochemical technology for the ultra-sensitive detection of 17β-estradiol (E2). GQDs-AuNPs were synthesized and modified on the surface of glassy carbon electrodes (GCE). Safranine T was used as the functional monomer and E2 was the template molecule for self-assembly and electropolymerization, thus generating an MIP film on the electrode surface. By elution, a large number of recognition sites for E2 were left in the polymer matrix. Before and after the combination with the target, there is an obvious change in the peak current signal, which enables the quantitative detection of E2 to be achieved. Under the optimized conditions, the concentration of E2 showed a good linear relationship with the peak current of the sensor in the range of 1 × 10−5–1 × 10−14 M, and the detection limit was 2.2 fM. The molecularly imprinted electrochemical sensor based on GQDs-AuNPs established in this study offers the features of simplicity of operation, low experimental cost, and high sensitivity. This method successfully detected E2 in milk, urine, and human serum, demonstrating its potential for broad application in clinical practice.

Graphical abstract: Research on a molecularly imprinted electrochemical sensor based on a graphene quantum dot-gold nanoparticle composite for the determination of 17β-estradiol

Article information

Article type
Paper
Submitted
24 Oct 2024
Accepted
16 Dec 2024
First published
03 Jan 2025

Anal. Methods, 2025,17, 999-1009

Research on a molecularly imprinted electrochemical sensor based on a graphene quantum dot-gold nanoparticle composite for the determination of 17β-estradiol

W. Yuan, Y. Wang, M. Jiang, Y. Jin, W. Yan, Q. Wang and L. Niu, Anal. Methods, 2025, 17, 999 DOI: 10.1039/D4AY01943A

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