The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in late 2019 triggered a global pandemic, revealing weaknesses in surveillance and diagnostic systems.
While antigen tests and reverse transcription polymerase chain reaction (RT-PCR) were crucial, they were limited in two key areas. RT-PCR required labs and time, while antigen tests lacked sensitivity. At a time of urgency, these faults highlighted the demand for innovative, accessible diagnostic solutions globally.
Surface plasmon resonance (SPR) became the solution, offering label-free, highly sensitive, and real-time biomolecular interaction detection. SPR biosensors are fast, require limited sample preparation, and can be miniaturized for portable testing.
Studies have demonstrated that SARS-CoV-2 antigens can be targeted using aptamers, molecularly imprinted polymers, and antibodies. Improvements in thermal control, advanced signal processing, and microfluidics have reduced noise and improved sensitivity.
The integration of artificial intelligence and machine learning also minimized false positives and boosted diagnostic accuracy. Incorporating materials like graphene and perovskites with architectures like nanorings has achieved sensitivities up to 2000 GHz/RIU.
These systems show potential across environmental, industrial, and biomedical domains. Yet, translating prototypes into clinical tools presents challenges, particularly in ensuring scalability, stability, reproducibility, and cost-effective manufacturing for real-world deployment.
The Study
In this work, the researchers proposed a high-performance graphene-silver hybrid metasurface plasmonic biosensor that integrates optimized resonator geometries with machine learning algorithms and advanced nanomaterials. The sensor was designed for scalability, with potential for industrial-scale production exceeding 10,000 units per wafer and an estimated per-sensor cost of between $2 and $5.
Design For Enhanced Plasmonic Resonance
To enhance plasmonic resonance, the metasurface featured a silver-coated central circular ring resonator (3.8 μm inner, 4.3 μm outer diameters). This ring was flanked by two larger rectangular resonators (10 μm × 1.3 μm), and two rectangular components (3 μm × 1.3 μm) were placed methodically on each side of the central circular ring.
These smaller rectangles flank larger ones to form plus-shaped resonators. All rectangular components were silver-coated for resonance enhancement and consistent optical conductivity. These resonators were fixed to a square base covered with a 0.34 nm thick graphene monolayer, enhancing the electromagnetic field tunability and confinement.
Finally, the full structure was mounted on a 21 μm × 21 μm × 1.5 μm silicon dioxide substrate, ensuring mechanical support and terahertz optical transparency. Fabrication tolerance was assessed using 1000 Monte Carlo simulations, accounting for deviations in silver thickness (±8 nm), rectangle dimensions, ring radius (±50 nm), and −5 % graphene coverage.
The Sensor
The graphene-silver metasurface sensor was fabricated using a carefully controlled multi-step nanofabrication process. First, a high-purity silicon dioxide substrate was prepared, cleaned using a standard semiconductor approach, and dried using nitrogen gas.
A graphene monolayer, grown separately by CVD on copper foil, was transferred on the substrate using a poly(methyl methacrylate) (PMMA) support layer, copper etching with iron chloride, and subsequent removal of PMMA by acetone. The sample was then annealed in a hydrogen/argon atmosphere to enhance adhesion and remove residues.
The metasurface pattern, including circular rings, plus-shaped resonators, and rectangular components, was defined using high-resolution electron beam lithography on a PMMA-coated substrate.
After pattern development, silver was deposited through electron beam evaporation to form 50-100 nm thick resonators. A lift-off process in acetone removed excess metal and resist, yielding precisely defined silver-coated structures. Optional annealing improved metal conductivity and crystallinity.
The sensor was characterized using scanning electron microscopy (SEM) and atomic force microscopy (AFM) to assess its surface structure and geometry. Raman spectroscopy confirmed graphene quality, and terahertz time-domain spectroscopy (THz-TDS) assessed its optical performance.
The sensor could also be integrated with microfluidics via soft lithography. Its electromagnetic behavior was modeled using plasmonic resonance, Kubo formalism, Maxwell’s equations, and coupled-mode theories.
Significance of the Study
Through parametric optimization using COMSOL Multiphysics, the sensor achieved 400 GHz/RIU sensitivity, a 5.000 RIU-1 figure of merit (FOM), and a 12.7 Q factor within a 1.334-1.355 RIU refractive index range.
The study’s simulations also demonstrated strong electric field confinement at the resonance frequency of 1.08 THz, emphasizing its detection accuracy.
A machine learning algorithm was used to improve predictive reliability, with a 0.90 coefficient of determination across different refractive indices. This result was achieved using a degree-3 polynomial regression model validated across multiple refractive index scenarios. The algorithm was supported by residual and correlation analysis, ensuring high performance and minimal overfitting.
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However, it is important to note that these results are based on simulations; experimental validation and multi-site clinical testing are proposed in the next phase of the study.
With its integrated fabrication strategy, the combination of the graphene–silver metasurface with ML-based modeling enabled fast, label-free, and highly accurate detection. This sensor outperformed conventional RT-PCR and antigen tests by offering superior sensitivity, accuracy, and potential cost-effectiveness compared to existing optical and terahertz biosensors.
The authors evaluated the sensor’s performance under environmental conditions, including temperature (15-40 °C), humidity (30-90 %), and electromagnetic interference. The results showed stable sensitivity and minimal drift, confirming its robustness for real-world settings.
Conclusion
To summarize, this study demonstrated that the proposed device could be effective as a portable, cost-effective, and novel diagnostic tool for next-generation pandemic preparedness. Future development will focus on multiplexed detection arrays, expanded respiratory pathogen panels, and regulatory readiness (FDA, CE) to move toward clinical use.
Journal Reference
Natraj, N. A., Mubarakali, A., Alagarsamy, M., H., M. Y., Dhivya, R. (2025). Next-generation COVID-19 detection using a metasurface biosensor with machine learning-enhanced refractive index sensing. Scientific Reports, 15(1), 1-20. DOI: 10.1038/s41598-025-18753-w, https://www.nature.com/articles/s41598-025-18753-w
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