Plasmon-Enhanced Charge Transport in Graphene-Au-SiO₂ Metasurfaces for Terahertz Biosensor Applications.
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| Title: | Plasmon-Enhanced Charge Transport in Graphene-Au-SiO₂ Metasurfaces for Terahertz Biosensor Applications. |
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| Authors: | Wekalao, Jacob1 (AUTHOR), Kouki, Marouan2 (AUTHOR) marouan.kouki@nbu.edu.sa, Ben Khalifa, Sana3 (AUTHOR), U, Arun Kumar4 (AUTHOR), Chebaane, Saleh5 (AUTHOR), Patel, Shobhit K.6 (AUTHOR) shobhitkumar.patel@marwadieducation.edu.in |
| Source: | Plasmonics. Jan2026, Vol. 21 Issue 1, p471-481. 11p. |
| Subjects: | Graphene, Blood sugar monitoring, Machine learning, Surface coatings, Charge transfer, Terahertz technology |
| Abstract: | This research introduces a terahertz sensor that combines graphene and gold-based metasurfaces to detect glucose. The sensor's structure features two circular rings coated with gold, encircled by a square ring coated with MXene, all positioned on a SiO₂ substrate. The design achieves impressive sensitivity at 1000 GHzRIU−1 when measuring refractive indices between 1.335–1.347 RIU, while 0.155 THz and 13.27 denotes the attained FWHM and Q factor. A stacked ensemble machine learning approach was implemented to optimize sensor performance, achieving an R2 score of 94% for chemical potential values between 0.1–0.9 eV and refractive indices ranging from 1.335 to 1.347. The sensor demonstrates better encoding capabilities and maintains stable performance across various operating conditions, making it particularly suitable for glucose monitoring applications. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | This research introduces a terahertz sensor that combines graphene and gold-based metasurfaces to detect glucose. The sensor's structure features two circular rings coated with gold, encircled by a square ring coated with MXene, all positioned on a SiO₂ substrate. The design achieves impressive sensitivity at 1000 GHzRIU−1 when measuring refractive indices between 1.335–1.347 RIU, while 0.155 THz and 13.27 denotes the attained FWHM and Q factor. A stacked ensemble machine learning approach was implemented to optimize sensor performance, achieving an R2 score of 94% for chemical potential values between 0.1–0.9 eV and refractive indices ranging from 1.335 to 1.347. The sensor demonstrates better encoding capabilities and maintains stable performance across various operating conditions, making it particularly suitable for glucose monitoring applications. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 15571955 |
| DOI: | 10.1007/s11468-025-02945-1 |
