Issue 3, 2025

Self-service aptamer-free molecularly imprinted paper-based sensor for high-sensitivity visual detection of influenza H5N1

Abstract

Developing low-cost self-service portable sensors to detect viruses is an important step in combating the spread of viral outbreaks. Here, we describe the development of an aptamer-free paper-based molecularly imprinted sensor for the instrument-free detection of influenza virus A (H5N1). In this sensor, Whatman paper loaded with Fe3O4 nanoparticles (WP@Fe3O4) was prepared as a substrate upon which silicon imprinting occurred in the presence of the template virus H5N1. After removal of the template virus, Fe3O4 which exposed in the imprinted cavities was able to catalyze the oxidation of colorless 3,3′,5,5′-tetramethylbenzidine (TMB) by hydrogen peroxide to form blue TMB + ions. Therefore, the concentration of virus can be semi-quantified by the color change of the solution after the catalytic reaction in the absence of any instruments. The color reaction can be clearly observed within 20 min. In addition, the remaining TMB could be quantified fluorometrically, with a limit of detection of 1.16 fM and an imprinting factor of 4.7. As far as we know, this sensor detects the target with the highest sensitivity that has yet been achieved in aptamer-free molecular imprinting sensors. Importantly, the cost of materials used for each sensor was as low as 4 cents (0.23 Yuan) per sensor. This sensitive self-service sensor, which is relatively easy and inexpensive to produce, will provide an effective new avenue for the rapid detection of viruses.

Graphical abstract: Self-service aptamer-free molecularly imprinted paper-based sensor for high-sensitivity visual detection of influenza H5N1

Supplementary files

Article information

Article type
Paper
Submitted
19 Sep 2024
Accepted
17 Dec 2024
First published
03 Jan 2025

Analyst, 2025,150, 552-558

Self-service aptamer-free molecularly imprinted paper-based sensor for high-sensitivity visual detection of influenza H5N1

H. Gong, L. Xu, Y. Li, T. Pang, C. Chen, F. Chen and C. Cai, Analyst, 2025, 150, 552 DOI: 10.1039/D4AN01233J

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