ArticleScientific reports2025
Improvement of the optical cavity-based biosensor's limit of detection using optimal 3-aminopropyltriethoxysilane process.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Genipin-Crosslinked, Silane-Anchored 3D Tumor-Stroma Microtissues for High-Content On-Chip Drug Testing.Advanced healthcare materials · 2026Article
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7 authors.
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Abstract
Optical resonator-based biosensors are important for advancing medical diagnostics and environmental monitoring due to their high sensitivity and label-free detection capabilities. In this study, we present a systematic comparison of three 3-aminopropyltriethoxysilane (APTES) functionalization methods - ethanol-based, methanol-based, and vapor-phase - on an Optical Cavity-based Biosensor (OCB) designed to detect streptavidin. The APTES process is an important first step for surface functionalization to form a linker to immobilize receptor molecules on the sensor surface. Our aim was to identify the deposition conditions that yield a uniform APTES layer, with an enhanced bioreceptor immobilization and improved sensor performance. By using a differential detection approach using two laser diodes at 808 nm and 880 nm, we achieved real-time intensity measurements in the OCB that enabled sensitive detection of target analyte. Among the three APTES methods tested, the methanol-based protocol (0.095% APTES) led to a significantly improved limit of detection (LOD) of 27 ng/mL, a threefold improvement over our previous results. Detailed atomic force microscopy (AFM), contact angle, and dose-response analyses confirmed the high quality of the monolayer formed under optimal conditions, emphasizing the importance of solvent choice and controlled deposition parameters for obtaining stable functional layers. These findings emphasize how the improved APTES functionalization directly enhances the sensitivity and reliability of our OCB system, offering a robust and adaptable approach for real-time, label-free detection in diverse biosensing applications.
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