In vitro diagnosis based on SERS–LFIA: research hotspots, increase sensitivities, combined detection, multimodal detection and related patents

Abstract

In recent years, the SERS–LFIA platform has gained significant traction in in vitro diagnostics. However, a comprehensive review of its advancements and applications is still lacking. This review first employing a bibliometric approach to analyze research trends. It then outlines strategies to enhance sensitivity, focusing on Raman reporter molecules, SERS tags, coupling methods, detection instruments. Additionally, the review explores the use of SERS–LFIA for diagnosing multiple disease biomarkers, highlighting its potential to improve diagnostic accuracy. The review also synthesizes the application of multimodal SERS–LFIA technology, integrating signals such as colorimetric, magnetic, photothermal, fluorescent, and catalytic modalities. This approach enhances detection versatility and broadens diagnostic capabilities. Furthermore, it examines the current patent landscape, providing insights into the technology's commercial and technological progress. Lastly, the review discusses ongoing challenges, including stability and reproducibility and quantitative detection, while suggesting directions for future research. In summary, this review consolidates the latest advancements in SERS–LFIA technology for in vitro diagnostics over the past decade. Anticipated to furnish a robust scientific foundation and theoretical underpinning for the advancement of SERS–LFIA technology, this endeavor aims to enhance its efficacy in clinical diagnostics.

Graphical abstract: In vitro diagnosis based on SERS–LFIA: research hotspots, increase sensitivities, combined detection, multimodal detection and related patents

Supplementary files

Article information

Article type
Review Article
Submitted
07 dek 2024
Accepted
12 mar 2025
First published
28 apr 2025

J. Mater. Chem. B, 2025, Advance Article

In vitro diagnosis based on SERS–LFIA: research hotspots, increase sensitivities, combined detection, multimodal detection and related patents

Y. Zhang, Q. Zhang, Z. Li, X. Zhou, C. Liu, H. Zha, X. Zhang, H. Zhao, G. Lü, J. Li and X. Li, J. Mater. Chem. B, 2025, Advance Article , DOI: 10.1039/D4TB02721C

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