journal article·2020-06

Mechanistic Modeling for Performance Engineering of SPR-Based Fiber-Optic Sensor Employing Ta2O5 and Graphene Multilayers in Phase Interrogation Scheme

Ravi Kant, Rana Tabassum

Plasmonics· Volume 15· Issue 3
Publisher page

Abstract

This work elucidates the numerical simulations carried out for the performance analysis of surface plasmon resonance (SPR)–based fiber-optic sensors using thin layers of tantalum (v) oxide (Ta2O5) and graphene in phase interrogation scheme. For this purpose, fiber-optic sensing probes with three distinct configurations have been used which include consecutive as well as individual layers of Ta2O5 and graphene over silver-coated unclad core of a silica optical fiber. The performance of sensing probes is studied in terms of the phase difference arising between TM and TE polarized components of the incident light as they propagate through the sensing region, besides their SPR responses towards varying refractive index of analyte layer, making use of the transfer matrix theory for stratified isotropic optical media in conjunction with geometrical optics. Theoretical investigations reveal that the probe having multi-layered arrangement of Ta2O5 and graphene possesses maximum sensitivity of propagating phase difference with magnitude 4.852 × 105 Deg/RIU at analyte refractive index of 1.33 in comparison to probes with individual Ta2O5 (3.731 × 105 Deg/RIU) and graphene (0.362 × 105 Deg/RIU) layers. Moreover, the design parameters of the probes have been numerically maneuvered with an aim of achieving an analyte refractive index of 1.33 in transmitted light spectrum to display the feasibility of proposed sensor in biochemical fields. The reported results unfold an innovative perspective to widen the application horizon of fiber-optic SPR sensors in phase interrogation scheme.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Plasmonic and Surface Plasmon Research · Advanced Fiber Optic Sensors · Photonic and Optical Devices

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

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