Browsing by Author "Rajakannu, Amuthakkannan"

Now showing 1 - 3 of 3

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
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.
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.