Janus-structured colorimetric and fluorescent nanocomposites with highly luminescent retention to enhance point-of-care diagnosis

Lateral flow immunoassay (LFIA) with dual-readout signals offers significant advantages in point-of-care testing due to its adaptability to various analysis scenarios. However, colorimetric and fluorescent nanocomposites (cfNCs) composed of plasmonic and luminescent components often suffer from low luminescent efficiency because of severe optical interference between the two functional components. Herein, we propose a simple self-assembly strategy for fabricating novel cf-NCs with a Janus structure (J-cf-NCs) using oleylamine ligand-coated gold nanoparticles (OA-AuNPs) and aggregation-induced emission luminogens (AIEgens) as building blocks. For comparison, two other cf-NCs with core-shell (CS-cf-NCs) and co-embedding (CE-cf-NCs) structures were synthesized to explore the effects of spatial intersection of OA-AuNPs and AIEgens on their optical properties. Our results demonstrate that J-cf-NCs exhibit the highest fluorescence retention (75 %), as the face-to-face spatial distribution of OA-AuNPs and AIEgens effectively prevents fluorescence resonance energy transfer and minimizes inner-filter effect-induced fluorescence quenching. When integrated with the LFIA platform, the developed J-cfNC-LFIA demonstrated excellent detection performance for both qualitative and quantitative determination of various target analytes in sandwich and competitive formats. Specifically, the visual limit of detection (vLOD) for C-reactive protein using J-cf-NC-LFIA showed approximately an eight-fold improvement compared to the classical colloidal gold test strip, while the quantitative sensitivity was two orders of magnitude greater than that of colorimetric readout. Furthermore, the vLOD for aflatoxin M1 in milk samples was 0.5 ng center dot mL-1, with a quantitative sensitivity 38-fold higher than that of the colorimetric pattern. Overall, the proposed J-cf-NCs exhibit significant potential for enhancing the detection performance of LFIA platforms.