Rayleigh Backscattering Based Macrobending Single Mode Fiber for Distributed Refractive Index Sensing
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.
Y. Du et al., "Rayleigh Backscattering Based Macrobending Single Mode Fiber for Distributed Refractive Index Sensing," Sensors and Actuators B: Chemical, vol. 248, pp. 346-350, Elsevier, Apr 2017.
The definitive version is available at https://doi.org/10.1016/j.snb.2017.04.014
Electrical and Computer Engineering
University of Missouri Research Board
Missouri University of Science and Technology. Materials Research Center
Missouri University of Science and Technology. Intelligent Systems Center
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)
Article - Journal
© 2017 Elsevier, All rights reserved.
01 Apr 2017