Label-free diagnosis of ovarian cancer using spoof surface plasmon polariton resonant biosensor

表面等离子共振 生物传感器 表面等离子体激元 等离子体子 光电子学 材料科学 超材料 表面等离子体子 纳米技术 纳米颗粒
作者
Yifei Zhang,Yu Xia,Haotian Ling,Jinhua Zhang,Ke Li,Cunzhong Yuan,Hanlin Ma,Wenjian Huang,Qiangpu Wang,Xuantao Su
出处
期刊:Sensors and Actuators B-chemical [Elsevier]
卷期号:352: 130996-130996 被引量:13
标识
DOI:10.1016/j.snb.2021.130996
摘要

Abstract Artificial periodic grooves on a metal line can mimic optical surface plasmon polaritons (SPPs) with high cut-off response at microwave frequencies, which is known as spoof SPPs and has recently gained great interest in biosensing due to the strong enhancement of the localized electric field. In this paper, a sensitive spoof SPP biosensor with split ring resonators (SRRs) for ovarian cancer diagnosis is reported for the first time. The SRRs are distributed in series on a metal line to replace the conventional periodic grooves, which maintains the same cut-off frequency and generates a sharp resonance simultaneously. This resonance enhances the localized field by a factor of 250 so that the proposed device is highly sensitive for detecting permittivity difference. The sensitivity in terms of frequency shifting was first investigated by electromagnetic simulation, and then demonstrated by distinguishing sucrose solutions with various concentrations. With the benefit of high sensitivity, the proposed biosensor successfully detects the serous ovarian cancer (SOC) and ovarian clear cell carcinoma (OCCC) tissues. The resonant frequency of the biosensor is 53.990 GHz intrinsically, which shifts to 53.814-53.968, 53.698-53.872, and 53.719-53.845 GHz with the normal, SOC, and OCCC tissues, respectively. The average point of all ovarian tissues is at 53.812 GHz, i.e., well below the normal tissues, which is a reasonable reference for cancer detection. Moreover, the average transmittance of all cancerous tissues is lower than the SOC tissues, which may be used to classify the SOC and OCCC tissues. The proposed planar SPP biosensor reveals a fast, sensitive, and label-free detection approach for ovarian cancers.

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