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    Area of Science:

    • Plasmonics and Nanophotonics
    • Materials Science
    • Biosensing

    Background:

    • Graphene-based nanomaterials offer unique optical properties.
    • Optical dimers utilizing localized surface plasmon resonances (LSPRs) are promising for sensing.
    • Multi-frequency sensing enhances detection capabilities.

    Purpose of the Study:

    • To develop and characterize graphene-coated spherical nano-particle optical dimers for multi-frequency refractive index sensing.
    • To investigate the dual-band sensing capabilities arising from LSPRs and plasmonic hybridization.
    • To demonstrate the potential for single bio-molecule recognition and sensor design using machine learning.

    Main Methods:

    • Fabrication of graphene-coated spherical nano-particles to form optical dimers.
    • Analysis of localized surface plasmon resonances (LSPRs) under parallelly polarized light.
    • Characterization of dual operating bands and near-field enhancement.
    • Application of machine learning algorithms (Random Forest, Decision Tree, XGBoost) for sensor analysis and design.

    Main Results:

    • Achieved dual operating bands with high sensitivity (S1 = 2.8143×10^4 nm/RIU, S2 = 1.8070×10^4 nm/RIU) and figures of merit (FOM1 = 213.2860 RIU^-1, FOM2 = 305.1521 RIU^-1).
    • Demonstrated significant near-field enhancement at the dimer gap, crucial for sensitive detection.
    • Showcased suitability for single bio-molecule recognition by tuning graphene properties and spectral response.
    • Successfully implemented machine learning for sensor performance analysis and molecule-specific sensor design.

    Conclusions:

    • Graphene-coated nano-particle optical dimers are effective for high-performance, multi-frequency refractive index sensing.
    • The proposed sensor architecture allows for tunable spectral response and versatile bio-molecule detection.
    • Machine learning provides a powerful framework for optimizing sensor design and data analysis in plasmonic sensing applications.