Browsing by Author "Ahmed, Ashour, M."

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    Multi-resonator MXene–graphene metasurface biosensor for non-invasive glucose detection with 1D-CNN optimization
    (Journal of Materials Science, 2026-10-24) Ochen, William; Ahmed, Ashour, M.; Elsayed, Hussein A.; Abdelkawy, M. A.; Mehaney, Ahmed; Rajakannu, Amuthakkannan; Wekalao, Jacob; Muheki, Jonas
    A high-performance terahertz (THz) metasurface biosensor incorporating an MXene–graphene hybrid nanocoating is presented for non-invasive blood glucose monitoring, targeting the growing clinical demand for painless, real-time diabetes management tools. The proposed sensor architecture features a K-shaped resonator functionalized with Ti3C2Tₓ MXene, embedded within concentrically arranged gold and silver ring antennas on a silicon dioxide (SiO₂) substrate. This geometrically optimized configuration promotes intense electromagnetic near-field confinement and supports spectrally sharp, tunable surface plasmon resonances that are highly sensitive to analyte-induced perturbations in the local dielectric environment. Full-wave electromagnetic simulations performed in COMSOL Multiphysics confirm a refractive index sensitivity of 1000 GHz RIU−1 across a physiologically relevant index range of from 1.335 to 1.347 RIU, accompanied by a quality factor exceeding 14 and a figure of merit of 20 RIU−1—metrics that collectively position this sensor among the leading THz metasurface designs reported to date. Glucose concentration-dependent characterization reveals systematic, monotonic resonance frequency shifts spanning from 0.719 to 0.714 THz, yielding a usable tuning bandwidth of 30 GHz across the clinically significant concentration range. To extend the platform beyond spectral readout, one-dimensional convolutional neural networks (1D-CNNs) trained on the simulated transmission spectra achieved a classification accuracy of 93% for glucose concentration prediction, demonstrating that machine learning integration substantially enhances analytical discrimination. The synergistic combination of MXene–graphene nanomaterial engineering, plasmonic resonance tailoring, and deep-learning-assisted spectral interpretation yields an ultrasensitive, label-free, reagent-free biosensing platform capable of real-time analyte quantification without blood extraction. This work establishes a compelling proof-of-concept for next-generation non-invasive glucose diagnostics and provides a scalable design framework applicable to broader continuous metabolite monitoring applications.