Browsing by Author "Elsayed, Hussein A."

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Advanced graphene–MXene–black phosphorus multilayered metasurface sensor for high-sensitivity terahertz brain tumor detection
(AIP Advances, 2026-03-11) Wekalao, Jacob; Elsayed, Hussein A.; Alqhtani, Haifa A.; Almawgani, Abdulkarem H. M; Gumaih, Hussein S.; Adam, Yousif S.; Mehaney, Ahmed; Ochen, William
In this research, we present a multilayer metasurface sensor design integrating graphene, MXene, black phosphorus, and gold for the ultrasensitive detection of brain tumor biomarkers in liquid biopsy samples. The hierarchical structure consists of a MXene-coated rectangular resonator, a black phosphorus-coated square resonator, a gold-coated circular ring, and a graphene-based circular substrate. This architecture was systematically optimized through comprehensive numerical simulations using COMSOL Multiphysics 6.3, integrated with machine learning frameworks. The proposed sensor demonstrates an outstanding sensitivity of 2308 GHz/RIU across a physiologically relevant refractive index range (1.3333–1.4833), significantly outperforming current state-of-the-art devices. Performance analysis identifies an optimal sensing regime at RI = 1.3425, achieving a figure of merit of 20.79 RIU−1 and a detection limit as low as 0.079 RIU. Detailed investigations of the transmission spectra under varying graphene chemical potentials (0.1–0.9 eV), incident angles (0○ –80○ ), and geometric modifications of the resonators reveal highly tunable sensing behavior. Furthermore, Random Forest Regression models achieve predictive accuracies of 85%–100%, enabling reliable estimation of sensor performance across diverse operating conditions. Collectively, these results establish a solid foundation for employing advanced 2D material–based metasurfaces in minimally invasive and early-stage brain tumor diagnostics, thereby advancing the capabilities of next-generation liquid biopsy technologies.
Design and optimization of a hybrid graphene–gold–silver terahertz metasurface biosensor for high-sensitivity sperm detection with machine learning for behavior prediction
(Journal of Electronic Materials, 2025-11-25) Muheki, Jonas; Elsayed, Hussein A.; Alfassam, Haifa E.; Ochen, William; Rajakannu, Amuthakkannan; Mehaney, Ahmed; Wekalao, Jacob
This study introduces a plasmonic-based sensor for sperm detection, integrating gold, graphene, and black phosphorus within a tailored multilayer structure. The sensor design consists of a silver-coated circular ring resonator (radius: 2–2.5 µm), a black phosphorus-coated square ring (7–8 µm), and four gold-coated circular resonators (each with a 2 µm radius) placed on a graphene-coated square platform. Electromagnetic simulations performed using COMSOL Multiphysics indicate optimal sensing performance within the 0.1–0.6 THz frequency range. The sensor demonstrates remarkable sensitivity of 5000 GHz per refractive index unit (RIU−1), a figure of merit of 90.909 RIU−1, and a detection limit of 0.02 RIU. It is capable of detecting sperm concentrations in a range of 17–197 million/mL, corresponding to refractive index variations from 1.33 to 1.3461. Furthermore, performance optimization through XGBoost machine learning achieved perfect prediction accuracy (R2 = 1.00) across all test cases. This high-efficiency sensor marks a significant step forward in sperm detection technologies, with promising applications in male fertility assessment and reproductive medicine
High-sensitivity terahertz metasurface biosensor for multi-cancer detection: a machine learningenhanced approach using graphene–MXene– silver–copper hybrid architecture
(Materials Technology Advanced Performance Materials, 2025-12-19) Wekalao, Jacob; Elsayed, Hussein A.; Mehaney, Ahmed; Ochen, William; Othman, Sarah I.; Bellucc, Stefano; Amuthakkannan, Rajakannu; Ahmed, Ashour M.; Muheki, Jonas
Early cancer detection requires highly sensitive diagnostic tools beyond the capabilities of conventional imaging and biopsy methods. We present a terahertz (THz) metasurface biosensor that integrates a copper-coated H-shaped resonator with three silver rectangular resonators enclosed within an MXene circular ring. The design incorporates complex electromagnetic interactions, nonlocal effects, and coupled-mode modelling to optimise performance. The biosensor achieves a sensitivity of 1000 GHz/RIU, a quality factor of 3.6–3.747, and a figure of merit up to 13.333 RIU⁻¹. It maintains stable absorption (52.789–53.804%) across 0.27–0.281 THz, with a linear resonance–refractive-index response (R² = 0.95276). Machine-learning optimisation of graphene chemical potential further enhances predictive accuracy (R² = 0.93). By enabling simultaneous detection of multiple cancer biomarkers through frequency-shift analysis, this noninvasive platform offers strong potential for real-time, early-stage cancer screening.
Machine learning–enhanced tunable terahertz metasurface sensor with a hybrid multi-resonator architecture for highsensitivity mino acid detection
(Optical and Quantum Electronics, 2026-02-28) Muheki, Jonas; Ahmed, Ashour M.; Ali, M. K. M.; Elsayed, Hussein A.; Kabarokole, Pelluce; Wekalao, Jacob; Mehaney, Ahmed; Rajakannu, Amuthakkannan
Accurate amino acid detection is essential for biomedical diagnostics, clinical monitoring, and biochemical research; however, conventional analytical techniques are often constrained by complex sample preparation, high operational costs, and limited real-time capability. Terahertz (THz) metasurface sensors provide a promising label-free and nonionizing alternative, yet their performance is frequently hindered by weak light–matter interaction and design trade-offs between sensitivity and fabrication feasibility. In this work, a hybrid THz metasurface sensor is proposed, integrating graphene, gold (Au), silver (Ag), copper (Cu), and tungsten disulfide (WS₂) within a hierarchical multiresonator configuration comprising square, circular ring, and L-shaped resonators fabricated on a SiO₂ substrate. The proposed architecture exploits synergistic plasmonic–dielectric coupling and strong near-field confinement to enhance sensitivity to refractive index perturbations induced by amino acid analytes, while maintaining a geometrically simplified structure to ensure manufacturability. Numerical simulations demonstrate excellent sensing performance, achieving a maximum sensitivity of 1000 GHz/RIU, a peak figure of merit (FOM) of 50 RIU⁻1 , and a strong linear relationship (R2=0.96243) between resonance frequency shift and analyte refractive index. Furthermore, machine learning (ML) models are employed to predict and optimize sensor behavior, yielding near-perfect accuracy (R2>0.9995) for variations in graphene chemical potential (0.1–0.9 eV) and circular resonator dimensions (5.5–7.5 µm). The proposed integration of hybrid materials, multi-resonator metasurface design, and ML-driven optimization effectively addresses key challenges in THz biosensing, enabling rapid, sensitive, and scalable amino acid detection for both point-of-care diagnostics and advanced biochemicalresearch.
Machine-learning-assisted multilayer graphene–silver–ZrN surface plasmon resonance biosensor for high-sensitivity hemoglobin detection
(Materials Technology, 2026-02-09) Ochen, William; Wekalao, Jacob; Muheki, Jonas; Elsayed, Hussein A.; Alqhtani, Haifa A.; Almawgani, Abdulkarem H. M.; Alhawari, Adam R.; Mehaney, Ahmed; Solouma, Emad
This work presents a theoretically optimized multilayer surface plasmon resonance (SPR) biosensor for quantitative hemoglobin detection using the Kretschmann configuration. The sensor integrates a BK-7 prism, silver plasmonic layer, graphene enhancement layer, zirconium nitride (ZrN) protective layer, and aqueous sensing medium. This architecture synergistically combines enhanced electromagnetic confinement with chemical stability, addressing silver's oxidation vulnerability while maintaining superior plasmonic performance. Electromagnetic analysis via transfer matrix method and finite element simulations demonstrates exceptional sensitivity metrics: maximum angular sensitivity of 500°/RIU, figure of merit of 92.25 RIU⁻¹, and detection limit of 0.006 RIU across clinically relevant hemoglobin concentrations (10–40 g/L). Localized electric field enhancement (~10⁶ V/m) at the sensing interface confirms optimal light-matter interaction amplification. Machine learning models predict sensor responses to graphene thickness and refractive index variations with R² > 0.99, enabling rapid optimization. This design advances SPR biosensor technology for sensitive, label-free biochemical detection applications.
Multi-resonator plasmonic metasurface biosensor with graphene enhancement for ultra-sensitive terahertz pregnancy detection using machine learning optimization
(Journal of Electromagnetic Waves and Applications, 2025-11-27) Wekalao, Jacob; Muhek,Jonas; Elsayed, Hussein A.; Mehaney,Ahmed; Othmane, Sarah I.; Abukhadra, Mostafa R.; Bellucci, Stefano; Rajakannu,Amuthakkannan; Ochen, William
This study presents a multi-resonator plasmonic metasurface biosensor operating in the terahertz range for detecting human chorionic gonadotropin (hCG), a primary pregnancy biomarker. The sensor consists of four resonators with different geometries and dimensions made from graphene, copper, aluminum, and gold. Its operation is based on surface plasmon resonance. Finite element simulations showed that transmittance varied from 98.428% to 30.736% as the graphene chemical potential changed from 0.1 to 0.45 eV. The optimized sensor achieved a sensitivity of 1000 GHz per refractive index unit (RIU) and a figure of merit of 13.333 RIU−1 . A Gradient Boosting Regressor model was used to predict sensor behavior. The model produced R 2 values between 0.90 and 1.00 for variations in incident angle, square ring geometry, and graphene chemical potential. Resonance frequency shifted from 0.32 to 0.30 THz with refractive index changes, following a linear relationship (R2 = 0.88947) that allows calibration for hCG detection.