Issue 22, 2022

A novel three-dimensional molecularly imprinted polypyrrole electrochemical sensor based on MOF derived porous carbon and nitrogen doped graphene for ultrasensitive determination of dopamine

Abstract

Herein, a novel molecular imprinting polypyrrole electrochemical sensor was fabricated based on a zirconia and carbon core–shell structure (ZrO2@C) and a nitrogen-doped graphene (NPG) modified glassy carbon electrode (GCE) for ultrasensitive recognition of dopamine (DA). The NPG was prepared by a sacrificial-template-assisted pyrolysis method and ZrO2@C was synthesized via annealing treatment of a zirconium-based metal–organic framework (UiO-66). A convenient electropolymerization method was used to prepare the pyrrole (Py) conductive molecularly imprinted polymer (MIP) in the presence of DA. The elution process of DA was performed by a simple overoxidation process under alkaline conditions. Differential pulse voltammetry (DPV) was used to assess the electrochemical performance of the sensors. The MIP-based electrochemical sensor with specific binding sites could be used for selective recognition of DA. Under the optimal conditions, the linear range of such a sensor was 5.0 × 10−9–1.0 × 10−4 mol L−1 and the detection limit was 3.3 × 10−10 mol L−1 (S/N = 3). This sensor exhibited suitable selectivity, stability, and reproducibility, which suggested that it could be a promising candidate for rapid diagnostic methods in dopamine investigations.

Graphical abstract: A novel three-dimensional molecularly imprinted polypyrrole electrochemical sensor based on MOF derived porous carbon and nitrogen doped graphene for ultrasensitive determination of dopamine

Supplementary files

Article information

Article type
Paper
Submitted
05 Sep 2022
Accepted
22 Sep 2022
First published
23 Sep 2022

Analyst, 2022,147, 5194-5202

A novel three-dimensional molecularly imprinted polypyrrole electrochemical sensor based on MOF derived porous carbon and nitrogen doped graphene for ultrasensitive determination of dopamine

L. Bu, D. Jiang, Q. Song, X. Shan, W. Wang and Z. Chen, Analyst, 2022, 147, 5194 DOI: 10.1039/D2AN01469F

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