诺共振
电场
太赫兹辐射
偶极子
激光线宽
材料科学
等离子体子
共振(粒子物理)
法诺平面
光电子学
电偶极矩
光学
物理
原子物理学
激光器
纯数学
量子力学
数学
作者
Ride Wang,Lei Xu,Jiayi Wang,Lang Sun,Yanan Jiao,Yuan Meng,Shuo Chen,Chao Chang,Chunhai Fan
出处
期刊:Nanoscale
[The Royal Society of Chemistry]
日期:2021-01-01
卷期号:13 (44): 18467-18472
被引量:90
摘要
An ultra-sensitive THz metasensor is presented based on quasi-BIC Fano resonance, which can distinguish extremely dilute concentrations (nM) of solutions. It provides a nondestructive sensing approach for disease prevention and diagnosis. However, the main drawback limiting the performance of THz-based bio-chemical sensors is the weak interaction between the optical field and the analyte, the characteristic scale of which is mismatched with the THz wavelength, leading to low sensitivity. Herein, we present an ultra-sensitive THz metasensor based on an electric Fano resonant metasurface which consists of three gold microrods arranged periodically. The designed electric Fano resonance provides a strong near-field enhancement near the surface of the microstructure, significantly boosting the light-analyte interactions and thus the sensitivity. Such an electric Fano resonance is formed by the interference between a leaky electric dipole resonance and a bound toroidal dipole mode which is a symmetry-protected bound state in the continuum supported by the sub-diffractive periodic system here. Owing to the strong electric fields generated near the interface of our microstructure around the toroidal dipole BIC, the proposed structure can distinguish extremely dilute concentrations (nM) of solutions. Importantly, by controlling the degree of geometrical asymmetry, the BIC-inspired mechanism provides an important and simple tool to engineer and tailor the linewidth and Q-factor of our proposed electric Fano resonance, indicating the ability to realize different biosensors for different optical regimes. Our results open new possibilities to realize a non-destructive and non-contact quantitative inspection of low-concentration solutions, providing a useful sensing approach for disease prevention and diagnosis.
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