Issue 14, 2022

A label-free amperometric immunosensor with improved electrocatalytic 3D braided AuPtCu-SWCNTs@MoS2-rGO for human growth differentiation factor-15 detection

Abstract

Growth differentiation factor-15 (GDF-15) is a member of the transforming growth factor-β family. GDF-15 is overexpressed in cardiovascular diseases and has become a novel biomarker for these diseases. In this study, we fabricated a label-free electrochemical immunosensor for sensitive detection of GDF-15. Briefly, a three-dimensional braided composite of AuPtCu-SWCNTs@MoS2-rGO (denoted A@M), which served as a label-free immunosensor platform, was obtained by wrapping single-walled carbon nanotubes (SWCNTs) with trimetallic nanoflowers (AuPtCu NFs) woven on a three-dimensional network nanostructure composed of Molybdenum disulfide (MoS2) and reduced graphene oxide (rGO) nanosheets. This optimization improved the ability of the platform to immobilize antibodies, accelerated the reduction of hydrogen peroxide, and promoted the migration rate of electrons on the electrode surface, thereby further amplifying the electrical signal and improving the sensitivity. The constructed sensor exhibited high sensitivity over a wide linear range from 1 pg mL−1 to 50 ng mL−1, with a low detection limit of 0.825 pg mL−1 for GDF-15. The fabricated label-free immunosensor exhibits satisfactory reproducibility, selectivity, and stability. The detection of actual samples was successful, enabling a broad scope of application in the early diagnosis, prognosis, and treatment of cardiovascular diseases.

Graphical abstract: A label-free amperometric immunosensor with improved electrocatalytic 3D braided AuPtCu-SWCNTs@MoS2-rGO for human growth differentiation factor-15 detection

Supplementary files

Article information

Article type
Paper
Submitted
28 Dec 2021
Accepted
09 Mar 2022
First published
10 Mar 2022

Anal. Methods, 2022,14, 1420-1429

A label-free amperometric immunosensor with improved electrocatalytic 3D braided AuPtCu-SWCNTs@MoS2-rGO for human growth differentiation factor-15 detection

Y. Jiao, Z. Huang, M. Chen, X. Zhou, H. Lu, B. Wang and X. Dai, Anal. Methods, 2022, 14, 1420 DOI: 10.1039/D1AY02198B

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