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| Publications [#382813] of Tuan Vo-Dinh
Papers Published
- Atta, S; Zhao, Y; Sanchez, S; Yampolsky, SV; Vo-Dinh, T, Plasmonics-Enhanced Dual-Modal Colorimetric and Photothermal Lateral Flow Immunoassay Using Gold Nanocages,
Analytical Chemistry, vol. 97 no. 12
(April, 2025),
pp. 6427-6437 [doi]
(last updated on 2026/01/10)
Abstract:
Lateral flow immunoassays (LFIA) are widely recognized as cost-effective point-of-care diagnostic tools (POCT) for infectious disease diagnosis. Despite their widespread use, traditional colorimetric LFIAs, which rely on gold nanospheres (GNP), are constrained by a limited sensitivity. To overcome this challenge, we have engineered gold nanocages (GNCs) with optimized core-to-shell morphologies, achieving significant amplification of both colorimetric and photothermal LFIA readout signals. The distinctive morphology of GNCs, featuring adjustable core-to-shell gap thicknesses, enables fine-tuning of the localized surface plasmon resonance (LSPR) peak across a broad spectral range from 600 to 1200 nm. Among the GNC morphologies evaluated, the optimized GNC (GNC-4), characterized by its larger size and maximal core-to-shell gap thickness, exhibited superior color brightness and enhanced photothermal efficiency compared to other GNC morphologies and traditional GNP. The enhanced performance of GNC-4 enabled the detection of influenza A (H1N1), used as the model analyte, achieving a limit of detection (LOD) of 1.8 ng/mL via colorimetric analysis and 1.51 pg/mL using photothermal LFIA. Compared to traditional GNP-based colorimetric LFIA detection, the colorimetric sensitivity of the GNC-4-based LFIA was enhanced by 7-fold, while the photothermal detection sensitivity showed an improvement of over 8000-fold. By incorporating a portable smartphone-based photothermal LFIA platform, our dual-modal LFIA exhibits high sensitivity, practicality in detecting H1N1 in spiked saliva samples, and long-term stability over five months, making it a promising tool for infectious disease detection and a potential model for diagnosing other pathogens.
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