https://doi.org/10.1140/epjb/s10051-025-00933-2
Regular Article - Computational Methods
Design and optimization of a hybrid graphene-metallic metasurfaces terahertz biosensor for high-precision detection of reproductive hormones, integrating locally weighted linear regression analysis and 2-bit encoding capabilities
1
Department of Optics and Optical Engineering, University of Science and Technology of China, 230026, Hefei, China
2
Physics Department, Faculty of Science, Beni-Suef University, 62512, Beni-Suef, Egypt
3
Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, 11421, Riyadh, Saudi Arabia
4
Materials Technologies and Their Applications Lab, Faculty of Science, Beni-Suef University, Beni Suef City, Egypt
Received:
27
January
2025
Accepted:
22
April
2025
Published online:
8
May
2025
The detection and monitoring of reproductive hormones play a crucial role in understanding reproductive health, fertility treatments, and endocrine disorders. Traditional hormone detection methods, such as immunoassays and chromatography, while accurate, often require complex sample preparation, specialized laboratory settings, and considerable time for analysis. This has created a pressing need for rapid, sensitive, and cost-effective detection methods that can be implemented in point-of-care settings. Meanwhile, we have introduced in the present communication a novel terahertz (THz) biosensor design that integrates graphene, copper, and silver in engineered metasurfaces resonators for high-precision reproductive hormone detection. The proposed structure leverages graphene's tunable properties alongside plasmonic enhancement from copper and silver, achieving a remarkable sensitivity of 1000 GHz/RIU in the 1.335–1.343 refractive index range. Moreover, the sensor demonstrates excellent performance metrics, including a quality factor of 11.315 and a figure of merit of 5.618 RIU–1. In addition, the sensor's capabilities were validated through electromagnetic simulations and locally weighted linear regression analysis, achieving a perfect prediction accuracy with an R2 value of 100% across multiple parametric variations. Furthermore, the design functions as a 2-bit encoder, producing distinct transmittance patterns for different binary states. Finally, the sensor's remarkable performance, combined with its practical fabrication feasibility using conventional techniques, presents a promising solution for point-of-care reproductive hormone detection and monitoring.
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
