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    This study introduces a machine learning approach using polynomial regression for biosensor design. The optimized double split-ring resonator metasurface achieved high sensitivity for potential hemoglobin detection applications.

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    Area of Science:

    • Metasurface design
    • Biosensor technology
    • Machine learning applications

    Background:

    • Metasurfaces offer tunable electromagnetic properties for sensor applications.
    • Machine learning (ML) models can optimize complex system performance.
    • Predictive modeling is crucial for efficient sensor design and characterization.

    Purpose of the Study:

    • To optimize metasurface absorber/sensor performance using ML.
    • To analyze different metasurface designs for enhanced sensitivity.
    • To develop a polynomial regression model for predicting sensor absorption.

    Main Methods:

    • Analysis of double split-ring resonator, single split ring resonator, and split ring resonator with thin wire metasurfaces.
    • Utilizing polynomial regression (PR) to predict absorption values based on various parameters.
    • Evaluating PR model accuracy using R2 score for test cases R-30 and R-50.

    Main Results:

    • Highest sensitivity achieved with double split-ring and single split ring resonator designs.
    • PR model demonstrated high prediction efficiency with R2 scores close to 1.0 at higher polynomial degrees.
    • Parameter variations significantly affect absorption and sensitivity.

    Conclusions:

    • The proposed PR model effectively predicts metasurface biosensor performance.
    • Optimized metasurface designs show promise for sensitive detection.
    • The developed biosensor is suitable for biomedical applications, specifically hemoglobin detection.