Issue 9, 2016

Convenient and controllable preparation of a novel uniformly nitrogen doped porous graphene/Pt nanoflower material and its highly-efficient electrochemical biosensing

Abstract

By employing dopamine as a nitrogen source and reducing agent, the block copolymer P123 as a pore forming agent, and graphene oxide as a carbon precursor, we present, for the first time, a convenient and controllable approach to the preparation of a novel uniformly nitrogen doped porous graphene (N-PGR) material. Using the prepared N-PGR as the supporting material, a novel nitrogen doped porous graphene/Pt nanoflower material (Pt/N-PGR) was obtained by a green and simple method. The characterization results of scanning electron microscopy (SEM), element mapping, transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS) demonstrate that Pt nanoflowers are uniformly dispersed on nitrogen doped porous graphene. Electrochemical measurements show that Pt/N-PGR-900/GCE exhibits improved electrocatalytic activity towards H2O2 reduction and glucose oxidation. Linear responses are found for H2O2 and glucose in the range of 0.5–40 326 μM and 0.5–133.5 mM with the detection limit (S/N = 3) of 0.2 μM and 0.05 mM, respectively. In addition, Pt/N-PGR-900/GCE exhibits high sensitivity and good anti-interference ability. The superior catalytic activity and selectivity make Pt/N-PGR a promising nanomaterial for application in electrochemical biosensing studies.

Graphical abstract: Convenient and controllable preparation of a novel uniformly nitrogen doped porous graphene/Pt nanoflower material and its highly-efficient electrochemical biosensing

Supplementary files

Article information

Article type
Paper
Submitted
28 十二月 2015
Accepted
10 三月 2016
First published
11 三月 2016

Analyst, 2016,141, 2741-2747

Convenient and controllable preparation of a novel uniformly nitrogen doped porous graphene/Pt nanoflower material and its highly-efficient electrochemical biosensing

S. Ren, H. Wang, Y. Zhang, Y. Sun, L. Li, H. Zhang, Z. Shi, M. Li and M. Li, Analyst, 2016, 141, 2741 DOI: 10.1039/C5AN02654G

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