Department of Electrical and Computer Engineering, University of Mohaghegh Ardabili, Ardabil, Iran.
Sci Rep. 2024 Oct 26;14(1):25499. doi: 10.1038/s41598-024-77336-3.
In recent years, following the outbreak of the COVID-19 pandemic, there has been a significant increase in cases of SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) and related deaths worldwide. Despite the pandemic nearing its end due to the introduction of mass-produced vaccines against SARS-CoV-2, early detection and diagnosis of the virus remain crucial in preventing disease progression. This article explores the rapid identification of SARS-CoV-2 by implementing a multimode plasmonic refractive index (MMRI) optical sensor, developed based on the split ring resonator (SRR) design. The Finite Difference Time Domain (FDTD) numerical solution method simulates the sensor. The studied sensor demonstrates three resonance modes within the reflection spectrum ranging from 800 nm to 1400 nm. Its material composition and dimensional parameters are optimized to enhance the sensor's performance. The research indicates that all three resonance modes exhibit strong performance with high sensitivity and figures of merit. Notably, the first mode achieves an exceptional sensitivity of 557 nm/RIU, while the third mode exhibits a commendable sensitivity of 453 nm/RIU and a Figure of Merit (FOM) of 45 RIU. These findings suggest that the developed MMRI optical sensor holds significant potential for the early and accurate detection of SARS-CoV-2, contributing to improved disease management and control efforts.
近年来,随着 COVID-19 大流行的爆发,SARS-CoV-2(严重急性呼吸系统综合症冠状病毒 2)在全球范围内的病例和相关死亡人数显著增加。尽管由于大规模生产的 SARS-CoV-2 疫苗的引入,大流行即将结束,但早期检测和诊断该病毒对于防止疾病进展仍然至关重要。本文探讨了通过实施基于分裂环谐振器 (SRR) 设计的多模等离子体折射率 (MMRI) 光学传感器来快速识别 SARS-CoV-2。有限差分时域 (FDTD) 数值解法模拟了传感器。所研究的传感器在 800nm 至 1400nm 的反射光谱范围内显示出三种共振模式。其材料组成和尺寸参数经过优化,以增强传感器的性能。研究表明,所有三种共振模式都具有高灵敏度和品质因数的优异性能。值得注意的是,第一种模式实现了 557nm/RIU 的异常高灵敏度,而第三种模式表现出 453nm/RIU 的令人瞩目的灵敏度和 45 RIU 的品质因数 (FOM)。这些发现表明,所开发的 MMRI 光学传感器在 SARS-CoV-2 的早期和准确检测方面具有重要潜力,有助于改善疾病管理和控制工作。
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