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  11. Temperature And Refractive Index Sensing By Brillouin Scattering In Liquid-core Pcf Assisted By Cnn For High-precision Prediction

Temperature and refractive index sensing by Brillouin scattering in liquid-core PCF assisted by CNN for high-precision prediction

Zhongyao Zhang, Shengsheng Huo, Donghe Sheng

    Optics Express
    |June 14, 2025

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    View abstract on PubMed

    Summary
    This summary is machine-generated.

    We developed a liquid-core photonic crystal fiber sensor for precise temperature and refractive index detection. A convolutional neural network accurately predicts these parameters, even in noisy conditions, showing great potential for advanced monitoring systems.

    Area of Science:

    • Photonics
    • Sensing Technology
    • Artificial Intelligence

    Background:

    • Liquid-core photonic crystal fibers (LC-PCFs) offer enhanced light-matter interaction for sensing.
    • Brillouin scattering provides a basis for measuring physical parameters.
    • Conventional methods struggle with real-time variations in sensor sensitivity.

    Purpose of the Study:

    • To propose an LC-PCF sensor for simultaneous temperature and refractive index sensing.
    • To develop a convolutional neural network (CNN) for accurate parameter prediction.
    • To overcome limitations of conventional methods in dynamic sensing environments.

    Main Methods:

    • Designing an LC-PCF with high optical and acoustic energy confinement in the liquid core.
    • Utilizing Brillouin scattering principles for sensing.

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  • Training and testing an optimized CNN model on Brillouin gain spectra under various signal-to-noise ratios (SNRs).

    • Main Results:

    • The LC-PCF demonstrated high sensitivities: 6.78 MHz/°C for temperature and 15.00 GHz/RIU for refractive index.
    • The CNN achieved high prediction accuracy, with R² values up to 99.99% for temperature and 99.98% for refractive index at 25 dB SNR.
    • The CNN maintained excellent performance even at 10 dB SNR, with R² values of 99.69% for both parameters.

    Conclusions:

    • The proposed LC-PCF and CNN approach enables high-precision, real-time sensing of temperature and refractive index.
    • This method overcomes the limitations of conventional equation-solving techniques.
    • The technology shows significant promise for advanced environmental monitoring and other sensing applications.