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<p>This study presents the development of a plasmonic sensor that integrates graphene, silver, and gold for the sensitive detection of organic compounds in wastewater, addressing the growing need for efficient environmental monitoring. Plasmonic sensors, known for their ability to detect minute changes in the refractive index of their surroundings, hold significant promise for real-time pollutant detection. The sensor's performance was evaluated across several key parameters, including the chemical potential of graphene, incident angles, and resonator dimensions. Operating in the frequency range of 0.1–1 THz, the sensor achieved a sensitivity of 227 GHzRIU<sup>−1</sup>, a figure of merit of 3.852 RIU⁻<sup>1</sup>, and a quality factor of 7.746. In addition, the detection capabilities were significantly enhanced through the use of a stacking ensemble machine learning approach, which demonstrated impressive prediction accuracy with R<sup>2</sup> values ranging from 91 to 100%. With a broad refractive index range of 1.3256–1.542 and a consistent full width at half maximum (FWHM) of 0.059 THz, the sensor shows considerable potential for applications in environmental monitoring, particularly for detecting trace organic contaminants in water systems.</p>

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Design and Optimization of a Graphene-Enhanced Plasmonic Sensor with Machine Learning Integration for Organic Compound Detection in Wastewater

  • 期刊论文

  • 发表日期：

    2025年3月5日

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  • Jacob Wekalao

摘要

This study presents the development of a plasmonic sensor that integrates graphene, silver, and gold for the sensitive detection of organic compounds in wastewater, addressing the growing need for efficient environmental monitoring. Plasmonic sensors, known for their ability to detect minute changes in the refractive index of their surroundings, hold significant promise for real-time pollutant detection. The sensor's performance was evaluated across several key parameters, including the chemical potential of graphene, incident angles, and resonator dimensions. Operating in the frequency range of 0.1–1 THz, the sensor achieved a sensitivity of 227 GHzRIU−1, a figure of merit of 3.852 RIU⁻1, and a quality factor of 7.746. In addition, the detection capabilities were significantly enhanced through the use of a stacking ensemble machine learning approach, which demonstrated impressive prediction accuracy with R2 values ranging from 91 to 100%. With a broad refractive index range of 1.3256–1.542 and a consistent full width at half maximum (FWHM) of 0.059 THz, the sensor shows considerable potential for applications in environmental monitoring, particularly for detecting trace organic contaminants in water systems.