Controlled PAH-mediated method with enhanced optical properties for simple, stable immunochromatographic assays
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Updated: Sep 30, 2025

Development of a Lateral Flow Immunochromatographic Strip for Rapid and Quantitative Detection of Small Molecule Compounds
Published on: November 13, 2021
Yanna Shao1, Wenxing Xu1, Yin Zheng2
1Institute of Microbiology, Guangdong Academy of Sciences, State Key Laboratory of Applied Microbiology Southern China, Key Laboratory of Agricultural Microbiomics and Precision Application, Ministry of Agriculture and Rural Affairs, Guangdong Provincial Key Laboratory of Microbial Safety and Health, Guangzhou, 510070, China; Department of Food Science and Technology, Institute of Food Safety and Nutrition, College of Science & Engineering, College of Life Science and Technology, Jinan University, Guangzhou, 510632, China.
Abstract:
Despite their potential for signal amplification in immunochromatographic assays (ICAs) with Au nanoparticles (AuNPs) as probes, metal growth methods are of limited practical applicability given their complex non-specificity and lack of robust growth schemes. Here, we propose a novel method of polyallylamine hydrochloride (PAH)-mediated metal growth for the detection of Escherichia coli O157:H7 by AuNP-ICA. The developed method relies on the highly controlled growth of Cu shells on the AuNP core and allows one to achieve highly enhanced colorimetric signals by controlling PAH as the growth framework. The introduction of PAH eliminates the non-specific adsorption of Cu ions on the nitrocellulose membrane and thus provides maximized and effective signal-to-noise ratios to achieve a detection limit of 9.8 CFU/mL for E. coli O157:H7. Moreover, the newly developed detection method exhibits good reproducibility (coefficient of variation <13%), remarkable stability, and practical applicability. The PAH-mediated signal enhancement system paves the way to the realization of stable metal growth methods based on Au, Ag, and other metals and is well suited for the rapid, stable, and sensitive detection of food-borne pathogens using the AuNP-ICA platform.
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