Browsing by Author "Muheki, Jonas"

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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
Enhancing infrared solar absorption efficiency through plasmonic solar absorber using machine learning-assisted design
(Springer Link- Plasmonics, 2024-10-18) Muheki, Jonas; Patel, Shobhit K.; Ainembabazi, Fortunate; Al-Zahrani, Fahad Ahmed
This research introduces the architecture of an infrared solar energy absorber coupled with absorption prognosis employing machine learning techniques. Our approach involves creating an efficient absorber tailored for infrared wavelengths complemented by a machine learning model for accurately predicting absorption levels. The absorber's design focuses on maximizing absorption within the 0.7 µm to 4.0 µm range. We optimized the absorber's parameters, including resonator thickness, substrate thickness, and angle of incidence. Simulation results demonstrate excellent absorption performance, capturing over 90% of light within the specified range. At angles between 0° and 40°, the average absorptance exceeds 80%, peaking at 97.16%. However, at an 80° angle of incidence, absorptance drops to 23.3%. The study employs a 1D-CNN regression model to estimate absorption at various wavelengths, which greatly decreases the time required for simulations and experiments. The findings demonstrate the promise of combining metamaterial structures with machine learning approaches to boost the efficiency of solar energy harvesting and conversion processes.
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.