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March 14, 2026Scientific Reports6 citationsOpen Access

Dual-band graphene-assisted metamaterial absorber with machine learning integration for high-sensitivity THz biosensing

SGSachin Kumar GuptaVGVaishnavi Chandrashekar GosiPPParth Pareek

Key Points

  • The goal is to develop a terahertz metamaterial absorber with high sensitivity for biosensing applications, incorporating machine learning for optimization.
  • Designed a dual-band terahertz metamaterial absorber using metallic and graphene-based rings.
  • Utilized a SiO2 substrate backed by a gold ground plane to enhance performance.
  • Conducted parametric analysis to evaluate the effect of structural dimensions on absorption efficiency.
  • Implemented machine learning to predict absorption characteristics and assist in biosensing tasks.
  • Achieved near-unity absorption at 0.9623 THz and 1.4079 THz with maximum sensitivities of 1.7435 THz/RIU and 1.475 THz/RIU.
  • Demonstrated high figures of merit and quality factors suitable for precise biosensing.
  • Validated resonance mechanisms through an equivalent circuit model.

Abstract

In this work, a compact dual-band terahertz (THz) metamaterial absorber integrated with graphene is presented for high-performance biosensing applications. The structure is composed of four concentric octagonal rings, where two are metallic and two are graphene-based, supported on a SiO2 substrate backed by a gold ground plane. The dual-resonant response is achieved through the metallic rings, while the graphene layers enhance absorption and tunability by improving impedance matching with free space. The absorber demonstrates near-unity absorption at 0.9623 THz and 1.4079 THz, with maximum sensitivities of 1.7435 THz/RIU and 1.475 THz/RIU, and corresponding high figures of merit and quality factors, making it highly suitable for precise biosensing. A detailed parametric analysis confirms the influence of structural dimensions on absorption efficiency, while the equivalent circuit model validates the underlying resonance mechanisms. Furthermore, machine learning models are employed to predict absorption characteristics and support biosensing tasks such as virus detection, thereby reducing the need for repeated full-wave simulations. The proposed design effectively combines compact geometry, high sensitivity, excellent selectivity, and ML-assisted optimization, positioning it as a versatile candidate for next-generation THz biosensing and biomedical diagnostics.

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Cite This Study

Gupta et al. (2026) studied this question.

synapsesocial.com/papers/69b4fb1bb39f7826a300bb0fhttps://doi.org/10.1038/s41598-026-41667-0
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