Issue 15, 2016

A facile one-pot synthesis of carbon nitride dots–reduced graphene oxide nanocomposites for simultaneous enhanced detecting of dopamine and uric acid

Abstract

In this study, we described the facile synthesis of carbon nitride dots–reduced graphene oxide nanocomposites (CNDs–rGO) and their application for the enhanced electrochemical determination of dopamine (DA) and uric acid (UA). CNDs–rGO were synthesized for the first time through a green and facile one-step approach, carried out by hydrothermal heat-treatment of an aqueous solution containing GO and chitosan without introduction of other reducing agents or surface modifier. Then, the morphology and composition of CNDs–rGO nanocomposites were characterized by transmission electron microscopy (TEM), Raman spectroscopy, X-ray photoelectron spectroscopy and Fourier transform infrared spectroscopy. TEM observations revealed that CNDs with a size of about 5.0 nm were homogeneously and densely distributed on the surface of rGO. Electrochemical investigations indicated that CNDs–rGO nanocomposites exhibited an excellent performance toward DA and UA. The linear range for DA was estimated to be from 80 nM to 227 μM with a sensitivity of 154.3 μA mM−1 cm−2 and a low detection limit of 0.03 μM. Meanwhile, the linear range for UA was estimated to be from 80 nM to 328 μM with a high sensitivity of 178.1 μA mM−1 cm−2 and a low detection limit of 0.05 μM. Therefore, CNDs–rGO nanocomposites showed great application potential for constructing electrochemical sensors for the detection of DA and UA.

Graphical abstract: A facile one-pot synthesis of carbon nitride dots–reduced graphene oxide nanocomposites for simultaneous enhanced detecting of dopamine and uric acid

Article information

Article type
Paper
Submitted
17 Mar 2016
Accepted
30 May 2016
First published
31 May 2016

Analyst, 2016,141, 4757-4765

A facile one-pot synthesis of carbon nitride dots–reduced graphene oxide nanocomposites for simultaneous enhanced detecting of dopamine and uric acid

Z. Yang, X. Zheng, Z. Li and J. Zheng, Analyst, 2016, 141, 4757 DOI: 10.1039/C6AN00640J

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