Issue 13, 2018

Facile fabrication of a 3,4,9,10-perylene tetracarboxylic acid functionalized graphene–multiwalled carbon nanotube–gold nanoparticle nanocomposite for highly sensitive and selective electrochemical detection of dopamine

Abstract

A novel non-enzymatic electrochemical sensor for highly sensitive and selective detection of dopamine was developed based on a 3,4,9,10-perylene tetracarboxylic acid functionalized graphene–multiwalled carbon nanotube–gold nanoparticle nanocomposite modified glassy carbon electrode (PTCA-RGO-MWCNTs-Au NPs/GCE). The nanocomposite film was prepared by a facile, eco-friendly and controllable route and its morphology was characterized by transmission electron microscopy (TEM), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopic (EDX) analysis, and X-ray diffraction (XRD) spectroscopy, respectively. Cyclic voltammetry and chronoamperometry were used for evaluating the electrochemical behaviors of the prepared sensor. The DA sensor exhibited excellent electrochemical performance toward DA with a sensitivity as high as 0.124 μA mM−1, a wide linear range of 1–100 μM and a low detection limit of 0.07 μM (S/N = 3). Moreover, it showed good selectivity toward DA without any obvious interference by AA and UA. Furthermore, the prepared DA sensor was applied to detect DA in real samples with satisfactory results.

Graphical abstract: Facile fabrication of a 3,4,9,10-perylene tetracarboxylic acid functionalized graphene–multiwalled carbon nanotube–gold nanoparticle nanocomposite for highly sensitive and selective electrochemical detection of dopamine

Article information

Article type
Paper
Submitted
25 Marts 2018
Accepted
10 Maijs 2018
First published
11 Maijs 2018

Analyst, 2018,143, 3075-3084

Facile fabrication of a 3,4,9,10-perylene tetracarboxylic acid functionalized graphene–multiwalled carbon nanotube–gold nanoparticle nanocomposite for highly sensitive and selective electrochemical detection of dopamine

C. Zhang, J. Ren, J. Zhou, M. Cui, N. Li, B. Han and Q. Chen, Analyst, 2018, 143, 3075 DOI: 10.1039/C8AN00559A

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