Rayleigh Backscattering Based Macrobending Single Mode Fiber for Distributed Refractive Index Sensing

Abstract

A novel and compact distributed refractive index (RI) sensor based on Rayleigh backscattering and macrobending single mode fiber (SMF) is proposed and experimentally investigated. Our proposed sensor is simply fabricated by bending a piece of SMF to a radius of curvature in several millimeters. We detect the refractive index of the external medium surrounding the macrobending fiber, for the first time, by analyzing the Rayleigh backscattering signals recorded from optical frequency domain reflectometry. We measure the range of the RI from 1.3348 to 1.3557 using the proposed method. To verify the capability of the distributed sensing, we also use this sensor to detect multipoint RIs simultaneously. The RI measurement sensitivities are 2319.24 GHz/RIU (18.55 nm/RIU) and 2717.85 GHz/RIU (21.74 nm/RIU) with bending diameters of 12.2 mm and 11.3 mm, respectively. In addition, our macrobending fiber has its original buffer coating remaining intact, allowing the fiber to maintain optimal mechanical property and be suitable for more practical applications.

Department(s)

Electrical and Computer Engineering

Sponsor(s)

University of Missouri Research Board
Missouri University of Science and Technology. Materials Research Center
Missouri University of Science and Technology. Intelligent Systems Center

Comments

The work was supported by University of Missouri Research Board, Materials Research Center at Missouri S & T, and the ISC center Post-doc Matching funds at Missouri S&T

Keywords and Phrases

Backscattering; Fiber optic sensors; Fibers; Frequency domain analysis; Single mode fibers, Distributed sensing; Macro bending; Measurement sensitivity; Optical frequency domain reflectometry; Radius of curvature; Rayleigh backscattering; Refractive index sensing; Refractive index sensor, Refractive index; Macrobending

International Standard Serial Number (ISSN)

0925-4005

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2017 Elsevier, All rights reserved.

Publication Date

01 Apr 2017

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