Issue 31, 2024

Novel flexible magnetoelastic biosensor based on PDMS/FeSiB/QD composite film for the detection of African swine fever virus P72 protein

Abstract

African swine fever (ASF) is a highly contagious and severe hemorrhagic disease caused by the African swine fever virus (ASFV). The continuous spread of ASFV affects the safety of the global meat supply; therefore, the establishment of sensitive and specific detection methods for ASFV has become an important hot spot in food safety. Herein, we developed a flexible magnetoelastic (ME) biosensor based on PDMS/FeSiB/QDs composite films for the detection of ASFV P72 protein. Based on the high luminescence performance of CsPbBr3 quantum dots and the excellent magnetoelastic effect of FeSiB, flexible ME biosensors convert stress signals generated by antibody–antigen-specific binding into optical and electromagnetic signals. The nanostructures covalently linked by quantum dots and PDMS provide biomodification sites for ASFV P72 antibodies, simplifying the functionalization modification process compared to the case of conventional biosensors. The deformation of the PDMS film is amplified, and the conversion of surface stress signals to electrical signals is enhanced by exposing the biosensor to a uniform magnetic field. The experimental results proved that the flexible ME biosensor has a wide linear range of 10 ng mL−1–100 μg mL−1, and the detection limit is as low as 0.079 ng mL−1. Moreover, the flexible ME biosensor also shows good stability, sensitivity and specificity, confirming the potential for early disease screening.

Graphical abstract: Novel flexible magnetoelastic biosensor based on PDMS/FeSiB/QD composite film for the detection of African swine fever virus P72 protein

Article information

Article type
Paper
Submitted
06 Jun 2024
Accepted
10 Jul 2024
First published
12 Jul 2024

Anal. Methods, 2024,16, 5441-5449

Novel flexible magnetoelastic biosensor based on PDMS/FeSiB/QD composite film for the detection of African swine fever virus P72 protein

Y. Liu, S. Sang, D. Zhao, Y. Ge, J. Xue, Q. Duan and X. Guo, Anal. Methods, 2024, 16, 5441 DOI: 10.1039/D4AY01057D

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