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<p>This study introduces a tunable graphene-based terahertz metasurface sensor developed for selective multi-gas detection. The novelty of this work lies in the combination of graphene electrostatic tunability and metasurface field confinement, providing a versatile platform for THz refractive index sensing. Numerical results indicate controlled resonance shifts within the 9.225–9.337 THz range, with the device reaching a sensitivity of 340&#xa0;GHz/RIU, a quality factor of 543.35, and a figure of merit of 14.55 RIU⁻¹. The sensor effectively distinguishes CO<sub>2</sub>, CH<sub>4</sub>, N<sub>2</sub>, C<sub>3</sub>H<sub>8</sub>, and air, supported by strong linear correlations (R² = 0.978) between spectral position and reflection response. To support rapid design exploration, a Random Forest regression model is implemented, providing accurate predictions of resonant behavior and reducing computational cost. These findings underline the relevance of graphene-based metasurfaces as compact and reconfigurable THz sensors for environmental and industrial monitoring applications.</p>

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Graphene Integrated Terahertz Metasurface for Multi Gas Sensing Applications with Machine Learning Optimization

  • 期刊论文

  • 发表日期：

    2026年3月16日

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  • Hamza Wertani,
  • Hamza Ben Krid,
  • Aymen Hlali,
  • Hassen Zairi

摘要

This study introduces a tunable graphene-based terahertz metasurface sensor developed for selective multi-gas detection. The novelty of this work lies in the combination of graphene electrostatic tunability and metasurface field confinement, providing a versatile platform for THz refractive index sensing. Numerical results indicate controlled resonance shifts within the 9.225–9.337 THz range, with the device reaching a sensitivity of 340 GHz/RIU, a quality factor of 543.35, and a figure of merit of 14.55 RIU⁻¹. The sensor effectively distinguishes CO2, CH4, N2, C3H8, and air, supported by strong linear correlations (R² = 0.978) between spectral position and reflection response. To support rapid design exploration, a Random Forest regression model is implemented, providing accurate predictions of resonant behavior and reducing computational cost. These findings underline the relevance of graphene-based metasurfaces as compact and reconfigurable THz sensors for environmental and industrial monitoring applications.