Integrated microfluidic chip with nanobiosensor for rapid and label-free detection of a specific gene

Congxiao Zhang*, Xuefei Lv, Saeed Yasmeen, Hong Qing, Yulin Deng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Biomolecular detection techniques are tending to develop in terms of miniaturization, automation, rapidity, sensitivity and low cost, and these techniques are urgently needed as "point of care tests" or "rapid tests" in clinical diagnosis, environmental monitoring and food safety. The rapid developments of microfluidics and nanomaterials, especially their interdisciplinary investigations, offer great opportunities to satisfy these immediate requirements. In this study, graphene oxide (GO) based nanobiosensors were integrated into microfluidic devices for gene-specific detection, combining their unique advantages in specificity, sensitivity, rapidity or miniaturization. The functional structure and fabrication technology of the microfluidic chip was investigated to meet the requirements for performing rapid and label-free detection of a specific gene using the GO based nanobiosensor, aiming at miniaturization, automation, low consumption of reagents and high throughout. In this paper a novel method is proposed for simple and rapid fabrication of microfluidic chips. Furthermore, the assay conditions of gene detection using the nanobiosensor were optimized in the microfluidic device, aiming at efficient specificity, sensitivity and rapidity for gene detection. Finally, the fabricated integrated microfluidic device was used to perform qualitative and quantitative analysis for detection of the target genes. Thus, this study demonstrates that the integrated microfluidic device can offer unique abilities such as rapid detection, portability, simplicity and low-cost detection of biomolecules in clinical diagnosis, environmental monitoring and industrial applications.

Original languageEnglish
Pages (from-to)3619-3625
Number of pages7
JournalAnalytical Methods
Volume9
Issue number24
DOIs
Publication statusPublished - 28 Jun 2017

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