Volume 3, 2024

An rGO-doped laser induced graphene electrochemical biosensor for highly sensitive exosome detection

Abstract

In this study, we developed a novel electrochemical sensing chip integrated with reduced graphene oxide (rGO) with laser-induced graphene (LIG) for the detection of exosomes associated with breast cancer biomarkers. Employing laser-induced technology, a three-dimensional porous graphene material is fabricated on the surface of a flexible polyimide film, which is subsequently combined with rGO through π–π stacking. This integration facilitates the doping of two-dimensional and three-dimensional material (2D/3D) structures, significantly enhancing the conductivity of the electrode material. Additionally, this approach markedly improves the surface hydrophobicity and biomolecule affinity of LIG, optimizing the immobilization of specific antibodies for exosomes. Importantly, this experiment marks the first occasion of merging two-dimensional rGO with three-dimensional LIG, resulting in the construction of a high-performance biosensing chip that enables specific capture and highly sensitive detection of exosomes. Under optimized conditions, the quantitative detection range for exosomes is established at 5 × 102 to 5 × 105 particles per μL, with a limit of detection (LOD) of 166 particles per μL. The biosensor is successfully used to analyze exosomes in breast cancer cell lines and patient serum samples, proving its practical application. This electrochemical biosensing chip offers significant practical application value in the early screening and diagnosis of diseases.

Graphical abstract: An rGO-doped laser induced graphene electrochemical biosensor for highly sensitive exosome detection

Article information

Article type
Paper
Submitted
31 May 2024
Accepted
05 Aug 2024
First published
02 Sep 2024
This article is Open Access
Creative Commons BY-NC license

Sens. Diagn., 2024,3, 1724-1732

An rGO-doped laser induced graphene electrochemical biosensor for highly sensitive exosome detection

X. Chen, X. Yan, J. Qiu, X. Zhang, Y. Zhang, H. Zhou, Y. Zhao, L. Han and Y. Zhang, Sens. Diagn., 2024, 3, 1724 DOI: 10.1039/D4SD00181H

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