Dual-core silver-coated plasmonic sensor modeling with machine learning

Plasmonic sensors utilizing surface plasmon resonance (SPR) have emerged as powerful tools for sensitive and label-free detection across a wide range of applications. This study introduces a new dual-core silver-coated plasmonic sensor designed to significantly enhance sensitivity and resolution, making it particularly effective for precise analyte detection in complex environments. A key innovation of this sensor lies in its dual-core architecture, which achieves the highest wavelength sensitivity reported at 30,000 nm/RIU and resolution of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>3.33</mml:mn> <mml:mo>×</mml:mo> <mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow> <mml:mrow><mml:mo>-</mml:mo> <mml:mn>6</mml:mn></mml:mrow> </mml:msup> </mml:math> RIU, covering a broad refractive index (RI) range from 1.34 to 1.41. Furthermore, the integration of machine learning (ML) algorithms, including multiple-variable linear regression (MLR), support vector regression (SVR), and random forest regression (RFR), marks a significant advancement in sensor design. These algorithms enable dynamic adaptation and the extraction of data-driven insights, enhancing the sensor's performance in predicting confinement loss and wavelength across various analytes. The innovative combination of a dual-core design and ML integration positions this plasmonic sensor as a highly sensitive and versatile tool well-suited for advanced bio-sensing applications.