ScholarWorks@중앙대학교

초록

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.

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