Issue 44, 2019, Issue in Progress

Nitrogen-containing three-dimensional biomass porous carbon materials as an efficient enzymatic biosensing platform for glucose sensing

Abstract

A novel glucose biosensor was developed by immobilizing glucose oxidase (GOD) on a three-dimensional (3D) porous cane vine (wisteria) stem-derived carbon (3D-CVS), which was firstly proposed as novel support material for electrochemical biosensors using loaded biomolecules. Here, an integrated 3D-CVS electrode was fabricated by loading GOD molecule onto a whole piece of 3D-CVS electrode for a glucose biosensor. The morphologies of integrated 3D-CVS and 3D-CVS/GOD electrode were characterized by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). SEM results show the 3D macroporous structure of the integrated 3D-CVS electrode. TEM results show that there are some micro-holes and defects in the 3D-CVS electrode. Electrochemical behaviors and electrocatalytic performance of integrated 3D-CVS/GOD electrode were evaluated by cyclic voltammetry and electrochemical impedance spectroscopy. The effects of pH and scanning rate on the electrochemical response of biosensors have been studied in detail. The glucose biosensor showed a wide linear range from 0.58 μM to 16 mM, with a high sensitivity of 86.17 μA mM−1 and a low detection limit of 0.19 μM. Furthermore, the glucose biosensor exhibited high selectivity, good repeatability and nice stability.

Graphical abstract: Nitrogen-containing three-dimensional biomass porous carbon materials as an efficient enzymatic biosensing platform for glucose sensing

Supplementary files

Article information

Article type
Paper
Submitted
27 May 2019
Accepted
02 Aug 2019
First published
15 Aug 2019
This article is Open Access
Creative Commons BY-NC license

RSC Adv., 2019,9, 25647-25654

Nitrogen-containing three-dimensional biomass porous carbon materials as an efficient enzymatic biosensing platform for glucose sensing

B. Shan, Y. Ji, Y. Zhong, L. Chen, S. Li, J. Zhang, L. Chen, X. Liu, Y. Chen, N. Yan and Y. Song, RSC Adv., 2019, 9, 25647 DOI: 10.1039/C9RA04008K

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