Based on the phase transition of vanadium dioxide(VO2), an ultra-broadband tunable terahertz metamaterial absorber is proposed. The absorber consists of bilayer VO2 square ring arrays with different sizes, which are completely wrapped in Topas and placed on gold substrate. The simulation results show that the absorption greater than 90% has frequencies ranging from 1.63 THz to 12.39 THz, which provides an absorption frequency bandwidth of 10.76 THz, and a relative bandwidth of 153.5%. By changing the electrical conductivity of VO2, the absorption intensity can be dynamically adjusted between 4.4% and 99.9%. The physical mechanism of complete absorption is elucidated by the impedance matching theory and field distribution. The proposed absorber has demonstrated its properties of polarization insensitivity and wide-angle absorption, and therefore has a variety of application prospects in the terahertz range, such as stealth, modulation, and sensing.
In this paper, we reported a theoretical study of a novel Surface plasmon resonance (SPR) biosensor composed of BK7 prism, gold (Au)/silver (Ag) bimetallic layer, silicon and two-dimensional (2D) materials. The bimetallic layer combines the advantages of Au and Ag and the high refractive index silicon layer enhances the electric field on the surface of the sensor, so that the sensor has a better overall performance in terms of sensitivity and figure of merit (FOM). Compared with ordinary dielectrics, 2D materials have excellent photoelectric properties, such as larger specific surface area, higher carrier density and stronger adsorption capacity, which improve the detection ability of the sensor. The sensitivity of the optimized sensor achieves 297.2°/RIU, 274°/RIU and 246°/RIU when the silicon layer is covered with graphene, MXene (Ti3T2Cx) and MoS2, respectively. Compared with the traditional SPR sensor, the sensitivity of the structure has been significantly improved, and its excellent performance has broad application prospects in biosensing and other fields.
Metamaterials and their related research have had a profound impact on many fields, including optics, but designing metamaterial structures on demand is still a challenging task. In recent years, deep learning has been widely used to guide the design of metamaterials, and has achieved outstanding performance. In this work, a metamaterial structure reverse multiple prediction method based on semisupervised learning was proposed, named the partially Conditional Generative Adversarial Network (pCGAN). It could reversely predict multiple sets of metamaterial structures that can meet the needs by inputting the required target spectrum. This model could reach a mean average error (MAE) of 0.03 and showed good generality. Compared with the previous metamaterial design methods, this method could realize reverse design and multiple design at the same time, which opens up a new method for the design of new metamaterials.
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