Label-Free Early Cancer Detection Using Encodable Dielectric–Graphene Terahertz Surface Plasmon Resonance Sensor with Artificial Intelligence for Behaviour Prediction
Priti Rishi, Abdulrahman Saad Alqahtani, Pankaj Pathak, Azath Mubarakali, Gopinath S
Abstract
We introduce a cutting-edge terahertz (THz) metasurface biosensor leveraging synergistic integration of graphene, magnesium oxide (MgO), barium titanate (BaTiO 3 ), and calcium fluoride (CaF 2 ) within a multilayered micro-nanostructure for ultra-sensitive, label-free early cancer detection. The sensor architecture is centered around a precisely engineered square ring resonator surrounded by hybrid satellite elements, designed to harness strong plasmonic and dielectric interactions in the 0.1–1 THz range. Finite element simulations conducted in COMSOL Multiphysics reveal an exceptional refractive index sensitivity of up to 1000 GHz RIU −1 , a narrow full width at half maximum of 0.095 THz, and a maximum figure of merit of 10.526 RIU −1 . Tunability via graphene’s chemical potential modulation enables dynamic resonance control, enhancing real-time adaptability for diverse biomarker detection. The device exhibits consistent and predictable redshifts in resonance frequency in response to minute changes in the analyte’s refractive index, supporting detection of cancer-specific. An XGboost Regression model further validates the sensor’s angular resilience, achieving coefficient of determination (R 2 ) scores up to 87%, with optimized configurations reaching 100%. The proposed platform also exemplifies encoding ability and thus demonstrates transformative potential for next-generation point-of-care diagnostics, combining sub-wavelength field confinement, high-throughput ML validation, and scalable fabrication into a single, powerful biosensing solution for early-stage cancer diagnostics.