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The research aims to develop a highly sensitive gas sensor by integrating machine learning with plasmonic metasurface technology.

Next-Generation Terahertz Gas Sensor Based on a Machine Learning–Optimized Hybrid Plasmonic Metasurface

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The research aims to develop a highly sensitive gas sensor by integrating machine learning with plasmonic metasurface technology.
Developed a multi-resonator architecture with gold, MXene, copper, and graphene on a silicon dioxide substrate.
Utilized concentric circular and square ring resonator geometries for operation across the 0.1–1 THz range.
Performed finite element analysis using COMSOL Multiphysics for structural optimization and sensitivity analysis.
Integrated machine learning algorithms to improve performance prediction of the sensor.
Achieved a refractive index sensitivity of 300 GHzRIU⁻¹, indicating high responsiveness to gas concentrations.
Obtained a quality factor of 13.143 and a figure of merit of 4.762 RIU⁻¹, reflecting effectiveness in detection.
Demonstrated resilience to environmental changes, enhancing reliability for portable applications.

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Demonstrates a gas sensor enhancing detection precision in industrial settings, suggesting promising applications.

Wekalao et al. (2026) studied this question.

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