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<p>This paper proposes a novel square-core optical fiber with an internally gold-coated structure for RI sensing. This novel design offers a wide area for the analyte channel and a flat surface for gold film deposition. Additionally, the unique structure facilitates enhanced field interaction with the gold film, as the metal film is positioned along the core surface. The proposed sensor demonstrates outstanding performance not only in RI sensing but also in cancer cell and pathogen identification. The recorded sensitivities are 25,000&#xa0;nm/RIU for RI sensing in the range of 1.33–1.41, 8571.43&#xa0;nm/RIU for cancer cell detection, and 6382.98&#xa0;nm/RIU for pathogen identification in water. Other performance parameters such as the figure of merit (FOM), sensor resolution, and detection accuracy (DA) also highlight the potential of this sensor in the respective domain. Another contribution of this work is the incorporation of a machine learning approach, called Gaussian Process Regression (GPR), to predict the resonance wavelength for any intermediate RI value, which could broaden the scope of this sensor’s applications.</p>

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Highly sensitive square core fiber plasmonic biosensor and Gaussian process regression for RI prediction

  • 期刊论文

  • 发表日期：

    2025年11月21日

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  • Abdulkarem H. M. Almawgani,
  • Afiquer Rahman,
  • Aslam Mollah,
  • Monir Abdullah,
  • Yahya Ali Abdelrahman Ali

摘要

This paper proposes a novel square-core optical fiber with an internally gold-coated structure for RI sensing. This novel design offers a wide area for the analyte channel and a flat surface for gold film deposition. Additionally, the unique structure facilitates enhanced field interaction with the gold film, as the metal film is positioned along the core surface. The proposed sensor demonstrates outstanding performance not only in RI sensing but also in cancer cell and pathogen identification. The recorded sensitivities are 25,000 nm/RIU for RI sensing in the range of 1.33–1.41, 8571.43 nm/RIU for cancer cell detection, and 6382.98 nm/RIU for pathogen identification in water. Other performance parameters such as the figure of merit (FOM), sensor resolution, and detection accuracy (DA) also highlight the potential of this sensor in the respective domain. Another contribution of this work is the incorporation of a machine learning approach, called Gaussian Process Regression (GPR), to predict the resonance wavelength for any intermediate RI value, which could broaden the scope of this sensor’s applications.