journal article·2019-02

Design and numerical analysis of a graphene-coated fiber-optic SPR biosensor using tungsten disulfide

M. Saifur Rahman, Shaikh Sumit Noor, M.S. Anower, Lway Faisal Abdulrazak, Md. Maksudur Rahman, K.A. Rikta

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Abstract

Abstract This article provides a simple hybrid design and rigorous numerical analysis of a fiber optic–based surface plasmon resonance (SPR) biosensor for DNA hybridization. The sensor core and both sides of the cladding are constructed with optical fiber, whereas the middle portion of the cladding is filled with the proposed hybrid of silver, tungsten disulfide (WS2), graphene, and a sensing medium. To the best of our knowledge, this is the first demonstration of such a highly sensitive SPR biosensor using WS2 for sensing DNA hybridization. The sensitivity, detection accuracy, and figure of merit are considered as the performance parameters and are analyzed in detail. The analyses reveal an impressive enhancement of the overall performance of the proposed sensor. Insertion of a WS2 layer between silver and graphene provides a sensor with sensitivity higher than that of other sensors reported to date. Additionally, concurrent improvement of all performance parameters is shown by this hybrid sensor, which is not the case for sensors based only on graphene. An increased number of graphene-only layers increases the sensitivity and decreases the detection accuracy and figure of merit. Numerical analysis shows that the variation of the SPR angle for mismatched DNA strands is quite negligible, whereas that for complementary DNA strands is considerable, which is essential for proper detection of DNA hybridization. Therefore, the proposed biosensor opens a new window toward detection of biomolecular interactions.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Plasmonic and Surface Plasmon Research · Advanced Fiber Optic Sensors · Advanced biosensing and bioanalysis techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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