High Sensitivity Fiber Optic SPR Refractive Index Sensor Based on Multimode-No-Core-Multimode Structure

Hu, Haofeng; Song, Xueyang; Han, Qun; Chang, Pengxiang; Zhang, Jiahang; Liu, Kun; Du, Yong; Wang, Hongyuan; Liu, Tiegen

doi:10.1109/jsen.2019.2956559

Cited by 31 publications

(7 citation statements)

References 24 publications

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“…The sensors reviewed in section 2 are selected to showcase these three approaches, which include unique structures like balloon-like structures [45,68,82], S-tapered structures [50], Kretschmann structures [67], and sawtooth structures [89]; notable specialty fibers such as HCF [66,85], SPF-TCPCF [46], 7-CF [87], and 4-CF [88]; and compensation methods, e.g. using FBG or LPG [41,43,44,47,76] to discriminate the cross-sensitivity and thin-film coating [67,79,[83][84][85]90] to improve the sensing performance. Despite the challenges involved in using unique structures or new specialty fibers, such as the complex fabrication process and the need for an in-depth understanding of fiber properties, further research is still demanded.…”

Section: Discussion and Prospectionmentioning

confidence: 99%

“…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%

“…Hu et al [84] presented an SPR fiber sensor based on a multimode-no-core-multimode (MNM) structure for RI sensing (figure 4(g)). The NCF section was coated with chromium and gold to improve the sensing performance.…”

Section: Refractive Index Sensorsmentioning

confidence: 99%

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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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Section: Discussion and Prospectionmentioning

confidence: 99%

Section: Refractive Index Sensorsmentioning

confidence: 99%

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

Wang,

Mizuno,

Dong

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Optical fiber sensors have been intensively investigated and reviewed in many research due to their high performance and unique capabilities [1], [2]. One of their uses is refractive index detection [3]- [6], which is significantly important in various fields. In recent years, optical fiber surface plasmon resonance (SPR) sensors have achieved a high impact due to their high performance.…”

Section: Introductionmentioning

confidence: 99%

“…A photonic crystal fiber SPR sensor is reported in [14], where a D-shaped sensor with V-groove analyte channel has been created. Hu et al [3] proposed a fiber optic SPR refractive index sensor based on multimode-no-core-multimode fiber structure. A core-shift welding technology has been investigated in [15], where a light is directly injected into the sensing fiber cladding to obtain the evanescent field at the interface between the cladding and the air.…”

Section: Introductionmentioning

confidence: 99%

Few-mode optical fiber surface plasmon resonance sensor with controllable range of measured refractive index

Shehab

Salah²,

Al-Sawalmeh³

et al. 2023

IJECE

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A few-mode optical fiber surface plasmon resonance sensor with graphene layer is investigated, firstly, with the aim of studying the behavior of the guided modes and, secondly, with the aim of determining the range of the measured refractive index for some selected few-mode fibers. The results show that as the number of modes propagated in the fiber increases, the maximum sensitivity of a particular mode decreases while the range of the measured refractive index of that mode increases. Also, it is shown that the range can be easily tuned with sensitivity consideration by only adjusting the operating wavelength without any modification of the sensor, which is desirable from practical point of view. In addition, it is shown that the core diameter of the fiber should be chosen according to sensitivity and range needing, where a compromise between them must be found. The study presented in this paper can significantly help in developing new sensing techniques, such as multi-parameter sensing, by monitoring the various responses of the modes. Also, it can be used to customize the sensor for specific sensing applications in various fields, especially to measure refractive indices in subranges of 1.38 to 1.46.

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Research Progress of Resonance Optical Fiber Sensors Modified by Low‐Dimensional Materials

Wang

Yin

et al. 2023

Laser & Photonics Reviews

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Resonance optical fiber sensors are widely studied for their high sensitivity, small size, and anti-electromagnetic interference. However, traditional resonance optical fiber sensors have encountered bottlenecks in the trace detection due to limited localized field intensity and low molecules affinity. With the development of materials technology, low-dimensional materials such as quantum dots, graphene, and transition metal sulfides have excellent properties such as high carrier mobility, large specific surface area, and flexible structure controllable artificial technologies that can also be used to synthesize new materials with desired characteristics, which can enhance the performance of sensors fundamentally. The introduction of low-dimensional materials can not only achieve the performance optimization of sensors, but also make sensors functional to realize the diversification of detection objects. This paper reviews the research progress of resonance optical fiber sensors based on low-dimensional materials. The detection principle and performance indexes related to resonance optical fiber sensing are elaborated, and resonance optical fiber sensors modified by low-dimensional materials are introduced. Furthermore, the role of low-dimensional materials in resonance fiber sensing is summarized, the direction of performance optimization in the production process of resonance fiber sensors is analyzed, and the future prospect of new resonance optical fiber sensors is given.

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High Sensitivity Fiber Optic SPR Refractive Index Sensor Based on Multimode-No-Core-Multimode Structure

Cited by 31 publications

References 24 publications

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

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

Few-mode optical fiber surface plasmon resonance sensor with controllable range of measured refractive index

Research Progress of Resonance Optical Fiber Sensors Modified by Low‐Dimensional Materials

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