Simultaneous Measurement of the Refractive Index and Temperature Based on a Hybrid Fiber Interferometer

This paper proposed a fiber sensor based on a Silicon core fiber incorporated with a fused silica grating design. The proposed structure is a nice sensitive in refractive index and temperature variation through theoretical calculation and finite element method simulation. The sensitivities of the refractive index in different polarization directions are 1838.7 and 1949.8 nm/RIU, respectively, within the index range from 1.28 to 1.38 RIU. The temperature range within 15 to 40 o C has the sensitivity up to 1.6 nm/ o C by using ethanol analyte. The proposed fiber sensor can provide a method or a solution for intelligent sensing systems.

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“…For relating to the issue of TOC compensation, Y. Zhang had already discussed and mentioned in [40].…”

Section: Resultsmentioning

confidence: 99%

Simultaneous measurement of temperature and refractive index based on an SPR Silicon core fiber sensor with a fused silica grating design

Kishikawa

Liaw

et al. 2022

Opt Quant Electron

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“…Ergo, they are legitimized to be used only when temperature is known or controlled. Refractive index sensors based on different interferometers have also been demonstrated (Zhao et al, 2019;Hu et al, 2020;Zhang et al, 2021a), but such interferometers cannot discriminate refractive index and temperature as in many cases refractive index induces a shift of the interference pattern.…”

Section: Resultsmentioning

confidence: 99%

Dual-Parameter Sensing With a Single Supermode Interferometer

Flores-Bravo

Villatoro

2022

Front. Sens.

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Optical fiber interferometers have intrinsic sensitivity to temperature, thus, in sensing applications; they need a reference temperature sensor or a mechanism to control the temperature. Here, we demonstrate that a single multicore fiber interferometer can monitor two parameters simultaneously; more particularly, refractive index, and temperature. The interferometer is easy to manufacture; a short segment of an optical fiber with seven coupled cores is fusion spliced at the distal end of a conventional single mode optical fiber. In the coupled-core fiber, two supermodes beat; this makes the reflection spectrum of the device to exhibit a well-defined series of maxima and minima. The refractive index of a sample in contact with multicore fiber alters the amplitude of the interference pattern and temperature induces a shift to such a pattern. The changes of the interference pattern are easy to monitor and decode with a low-resolution spectrometer. As an application of our dual-parameter sensor, the thermo-optic coefficient of a sample was measured.

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“…Coincidently, another balloon-like hybrid fiber sensor was proposed by Hu et al [82] for simultaneous RI and temperature measurement. Compared to [45,68], where the MMF was bent as balloon-like, this structure cascades an SMS and a bending SMF, i.e.…”

Section: Refractive Index Sensorsmentioning

confidence: 99%

“…The polished part was a circular truncated cone shape coated with 50 nm-thickness gold film. Due to the core diameter mismatch, when the light enters the SMF, most of the light wave leaks into the cladding of the [80]), (d) SMF-FTMN-SMF [81], (e) SMS cascaded with a balloon-like SMF [82], (f) experimental setup to detect fiber speckle patterns with D-shaped MMF (© 2020 IEEE. Reprinted, with permission, from [83]), (g) MNM with coated NCF [84], (h) MMF-tapered HCF-MMF [85].…”

Section: Refractive Index Sensorsmentioning

confidence: 99%

Multimode optical fiber sensors: from conventional to machine learning-assisted

Wang,

Mizuno,

Dong

et al. 2023

Meas. Sci. Technol.

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Multimode fiber (MMF) sensors have been extensively developed and utilized in various sensing applications for decades. Traditionally, the performance of MMF sensors was improved by conventional methods that focused on structural design and specialty fibers. However, in recent years, the blossom of machine learning techniques has opened up new avenues for enhancing the performance of MMF sensors. Unlike conventional methods, machine learning techniques do not require complex structures or rare specialty fibers, which reduces fabrication difficulties and lowers costs. In this review, we provide an overview of the latest developments in MMF sensors, ranging from conventional methods to those assisted by machine learning. This article begins by categorizing MMF sensors based on their sensing applications, including temperature and strain sensors, displacement sensors, refractive index sensors, curvature sensors, bio/chemical sensors, and other sensors. Their distinct sensor structures and sensing properties are thoroughly reviewed. Subsequently, the machine learning-assisted MMF sensors that have been recently reported are analyzed and categorized into two groups: learning the specklegrams and learning the spectra. The review provides a comprehensive discussion and outlook on MMF sensors, concluding that they are expected to be utilized in a wide range of applications.

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Simultaneous Measurement of the Refractive Index and Temperature Based on a Hybrid Fiber Interferometer

Cited by 25 publications

References 37 publications

Simultaneous measurement of temperature and refractive index based on an SPR Silicon core fiber sensor with a fused silica grating design

Simultaneous measurement of temperature and refractive index based on an SPR Silicon core fiber sensor with a fused silica grating design

Dual-Parameter Sensing With a Single Supermode Interferometer

Multimode optical fiber sensors: from conventional to machine learning-assisted

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