Issue 15, 2020

A 3D porous graphene aerogel@GOx based microfluidic biosensor for electrochemical glucose detection

Abstract

As a chronic disease, diabetes may result in serious complications that endanger the health and life of patients. Accurate and real-time detection of blood sugar levels is of great significance for the prevention and treatment of diabetes. In this paper, an enzymatic electrochemical microfluidic biosensor for glucose detection was developed based on a three-dimensional (3D) porous graphene aerogel and glucose oxidase (GOx). A graphene aerogel was prepared by freeze-drying a graphene hydrogel and has a high electrical conductivity, the 3D porous structure provided a good near-biological condition for GOx and the increased specific surface area allowed more GOx to be immobilized on the graphene aerogel. The microfluidic system greatly reduced the consumption of samples during tests. Amperometric measurements were carried out to test glucose concentrations, and the enzyme biosensor showed a linear range from 1 mM to 18 mM (R2 = 0.991). The limit of detection (LOD) was 0.87 mM (S/N = 3) and the sensor showed excellent selectivity and stability. Finally, monitoring glucose in serum samples was achieved by the proposed sensor and good recoveries were obtained. Due to its excellent performance, the proposed biosensor has a favorable application prospect in the prevention and clinical diagnosis of diabetes. Furthermore, our method, which is used to prepare a graphene aerogel modified electrode in a microfluidic chip, can be widely used in various electrochemical sensors.

Graphical abstract: A 3D porous graphene aerogel@GOx based microfluidic biosensor for electrochemical glucose detection

Supplementary files

Article information

Article type
Paper
Submitted
06 Apr 2020
Accepted
30 May 2020
First published
05 Jun 2020

Analyst, 2020,145, 5141-5147

A 3D porous graphene aerogel@GOx based microfluidic biosensor for electrochemical glucose detection

J. Xu, K. Xu, Y. Han, D. Wang, X. Li, T. Hu, H. Yi and Z. Ni, Analyst, 2020, 145, 5141 DOI: 10.1039/D0AN00681E

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