Issue 36, 2017, Issue in Progress

Synthesis of nickel(ii) coordination polymers and conversion into porous NiO nanorods with excellent electrocatalytic performance for glucose detection

Abstract

A nanostructured nickel–salicylic acid (SA) coordination polymer, Ni(SA)2(H2O)4 has been successfully synthesized under hydrothermal conditions on a large scale without the assistance of surfactants or template. After heat treatment, the Ni(SA)2(H2O)4 coordination polymers were converted into NiO nanostructures. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) results showed that the length of the as-prepared NiO varies from 2.0 to 3.0 μm with an average diameter of 150 nm. Due to a large number of gases released during thermal decomposition, the obtained NiO is formed of porous nanostructures. The porous NiO was used as an electrocatalyst for the detection of glucose in a nonenzymatic electrochemical sensor. The porous NiO modified glassy carbon (p-NiO/GC) electrode shows a wide linear range (0.01–5 mM), a low detection limit (2 μM), and a high sensitivity (364 μA mM−1 cm−2), as well as good stability and reproducibility. More importantly, the common interfering species, such as ascorbic acid (AA), uric acid (UA), and 3,4-dihydroxyphenylacetic acid (DOPAC) did not cause any interference at the detection potential (0.45 V vs. SCE). Furthermore, the nonenzymatic sensor was successfully applied to the determination of the glucose concentration in human serum samples.

Graphical abstract: Synthesis of nickel(ii) coordination polymers and conversion into porous NiO nanorods with excellent electrocatalytic performance for glucose detection

Supplementary files

Article information

Article type
Paper
Submitted
17 Jan 2017
Accepted
03 Apr 2017
First published
21 Apr 2017
This article is Open Access
Creative Commons BY-NC license

RSC Adv., 2017,7, 22208-22214

Synthesis of nickel(II) coordination polymers and conversion into porous NiO nanorods with excellent electrocatalytic performance for glucose detection

C. Chen, M. Shi, M. Xue and Y. Hu, RSC Adv., 2017, 7, 22208 DOI: 10.1039/C7RA00715A

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