Advances in intelligent multi-mode lateral flow assays: from multi-metallic nanomaterials to smart analytical integration

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Chem. Soc. Rev., 2026, Advance ArticleDOI: 10.1039/D5CS00963D, Review Article Open Access &nbsp This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.Xinyi Liang, Hayoung Kim, Thanh Mien Nguyen, Kun Wang, Chengcheng …

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Advances in intelligent multi-mode lateral flow assays: from multi-metallic nanomaterials to smart analytical integration

* Corresponding authors

a State Key Laboratory of Chemical Safety, College of Chemistry and Chemical Engineering, China University of Petroleum (East China), Qingdao 266580, China
E-mail: [email protected]

b Department of Energy and Bio Sciences, Hanyang University ERICA, Ansan 15588, South Korea
E-mail: [email protected]

c Department of Applied Chemistry, Hanyang University ERICA, Ansan 15588, South Korea

d Center for Bionano Intelligence Education and Research, Hanyang University ERICA, Ansan 15588, South Korea

e Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea
E-mail: [email protected]

Abstract

Lateral flow assays (LFAs) have evolved from simple qualitative tools into intelligent, multi-modal analytical platforms that integrate rationally engineered multi-metallic nanoparticles (MMNPs) with artificial intelligence (AI)-assisted data analysis to redefine the frontier of point-of-care diagnostics. This transformation has been driven by the advent of MMNPs, which couple plasmonic, catalytic, and magnetic properties within a single nano-system to achieve the tuneable synergistic enhancement of sensitivity, specificity, and dynamic range. The rational design of alloy, core–shell, hetero-structured, and hollow MMNP architectures allows simultaneous multi-signal readouts (e.g. colourimetric, fluorescence, chemiluminescence, surface-enhanced Raman scattering, photothermal, and electrochemical), thereby enabling intrinsic cross-verification and expanding diagnostic reliability. Parallel advances in AI, smartphone integration, and the Internet of Things connectivity have further elevated LFAs into digitally networked biosensors where embedded algorithms perform automated signal interpretation, error correction, and multi-mode data fusion, while cloud-linked infrastructures enable remote monitoring and epidemiological intelligence. These developments collectively reframe LFAs as integral components of data-driven, personalised, and preventive healthcare systems. Herein, we provide a unified framework that links design-on-demand MMNP synthesis, fully automated microfluidic LFA devices, AI-enhanced clinical decision support, and regulatory standardisation, and outline strategies for translating next-generation intelligent LFAs from laboratory innovation to global medical deployment.