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    A novel graphene-gold-silver hybrid biosensor demonstrates high sensitivity for hemoglobin detection. This advanced design offers potential for real-time health monitoring systems.

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    Area of Science:

    • Nanomaterials Science
    • Biomedical Engineering
    • Analytical Chemistry

    Background:

    • Biosensors are crucial for health monitoring, requiring sensitive detection of biomolecules.
    • Graphene's unique properties make it suitable for biosensor design.
    • Hemoglobin detection is vital for diagnosing various health conditions.

    Purpose of the Study:

    • To propose and analyze a novel Graphene-Gold-Silver hybrid structure based on Zinc Oxide for enhanced biomolecule detection.
    • To achieve highly sensitive detection of hemoglobin.
    • To investigate the optical and electrical properties of the proposed hybrid structure.

    Main Methods:

    • Design and simulation of a Graphene-Gold-Silver hybrid structure on a Zinc Oxide base.
    • Analysis of reflectance and electric field (E-field) properties.
    • Optimization of physical parameters for enhanced sensitivity.
    • Application of a machine learning algorithm for reflectance prediction.

    Main Results:

    • The proposed hybrid biosensor achieved a maximum sensitivity of 1000 nm/RIU for hemoglobin detection.
    • The electric field results correlated well with the reflectance results, validating the design.
    • Optimization of physical parameters significantly improved sensor sensitivity.
    • Machine learning model demonstrated high prediction accuracy, reducing simulation resources.

    Conclusions:

    • The Graphene-Gold-Silver hybrid structure offers a highly sensitive platform for biosensing applications.
    • The developed biosensor shows significant potential for real-time hemoglobin monitoring.
    • The integration of machine learning aids in efficient design optimization and simulation.