Advanced terahertz gas sensor based on multi-material metasurface integration with machine learning optimization
Source abstract
Abstract
This paper presents a gas sensor based on an integrated metasurface design incorporating borophene, germanene, phosphorene, and graphene. The sensor leverages the unique properties of these two-dimensional materials arranged in a precisely engineered geometric configuration of circular and square ring resonators. Comprehensive simulation and optimization studies demonstrate that the sensor achieves a maximum sensitivity of 700 GHz/RIU, a figure of merit of 9.459 RIU−1, and a detection accuracy of 13.514. Meanwhile, the extensive optimization procedure regarding the sensor’s performance was analyzed across varying parameters, including graphene chemical potential (0.1–0.9 eV), angle of incidence (0–80°), and resonator dimensions. In addition, a machine learning approach using locally weighted linear regression was implemented to optimize the sensor response, achieving prediction accuracies up to 100% R2. The proposed design offers a promising platform for real-time, highly sensitive toxic gas detection with potential applications in industrial safety and environmental monitoring.
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Research context
OpenAlex topics: Terahertz technology and applications · Metamaterials and Metasurfaces Applications · Gas Sensing Nanomaterials and Sensors
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Optics and Optical Engineering, University of Science and Technology of China 1 , 96 Jinzhai Road, Hefei 230026,
Photonic and Phononic Crystals Lab., Physics Department, Faculty of Science, Beni-Suef University 2 , Beni Suef 62512,
3 Department of Biology, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Geosciences Department, College of Science, United Arab Emirates University 4 , 15551 Al Ain,
Applied Science Research Center, Applied Science Private University 5 , Al-Jandaweel, Amman 11931,
INFN-Laboratori Nazionali di Frascati 6 , Via E. Fermi 54, 00044 Frascati,
Department of Mechanical and Industrial Engineering, College of Engineering, National University of Science and Technology 7 , Muscat,
Funding
Deanship of Scientific Research, Princess Nourah Bint Abdulrahman University
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