Few-layers MoS2: Advanced SPR sensing platform for serological diagnosis of dengue
Source abstract
Abstract
Plasmonic biosensing has emerged as a promising next-generation diagnostic platform due to its high sensitivity and potential for point-of-care applications. Among these approaches, Surface Plasmon Resonance (SPR) is widely used to study biomolecular interactions in clinically relevant systems. However, its selectivity may be compromised by nonspecific refractive index (RI) variations occurring at the sensor surface. Two-dimensional (2D) molybdenum disulfide (MoS 2 ) exhibits attractive physicochemical properties, including high surface area, excellent biocompatibility, and strong affinity for biomolecules. These characteristics enhance biomolecule immobilization and create a favorable microenvironment for highly selective detection in SPR biosensors. Herein, we report a simplified SPR biosensor based on the direct immobilization of the recombinant envelope protein domain III of dengue virus serotype 1 (EDIII-DENV-1) onto MoS 2 nanosheets. Protein immobilization occurs predominantly through hydrophobic interactions. The biosensing platform was structurally characterized by atomic force microscopy (AFM), Raman spectroscopy, and scanning electron microscopy (SEM). A thin MoS 2 film (∼8 layers, 13 ± 4 nm) deposited onto the gold surface by spin coating (5000 rpm for 50 s) provided high surface coverage. To demonstrate its clinical relevance, the optimized SPR biosensor was applied to detect dengue antibodies in human serum samples from infected (n = 10) and non-infected (n = 10) individuals, showing a highly significant difference between the groups (p = 7 × 10 −7 ). Using a cut-off value of 232 m° (Δθ SPR ), the biosensor achieved 100% sensitivity and 100% specificity, correctly classifying all analyzed samples.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: 2D Materials and Applications · Mechanical and Optical Resonators · Advanced Fiber Optic Sensors
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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