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 (Ti₃T₂C_x) 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.

The optical Vernier effect is a powerful tool for improving the sensitivity of an optical sensor, which relies on the use of two sensor units with slightly detuned frequencies. However, an improper amount of detuning can easily cause the Vernier effect to be unusable. In this work, the effective generation range of the Vernier effect and the corresponding interferometer configuration are suggested and experimentally demonstrated through a tunable cascaded Fabry–Perot interferometer structure. We further demonstrate a practical method to increase the magnification factor of the Vernier effect based on the device bandwidth. Only the optical path length of an interferometer probe and the sensitivity of the measurement parameters are needed to design this practical interferometer based on the Vernier effect. Our results provide potential insights for the sensing applications of the Vernier effect.

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.

This paper proposes a Fabry–Perot pressure sensor based on AB epoxy adhesive with ultra-high sensitivity under low pressure. Fabry–Perot interference, located between single-mode fiber (SMF) and hollow-core fiber (HCF), is an ultra-thin AB epoxy film formed by capillary action. Then the thick HCF was used to fix the HCF and SMF at both ends with AB epoxy adhesive. Experimental results show that when the thickness of AB epoxy film is 8.74 μm, and the cavity length is 30 μm, the sensor has the highest sensitivity. The sensitivity is 257.79 nm/MPa within the pressure range of 0–70 kPa. It also investigated the influence of the curing time of AB epoxy on the interference spectrum. Experiments showed that the interference spectrum peak is blue-shifted with the increase of curing time. Our study also demonstrated the humidity stability of this pressure sensor. These characteristics mean that our sensor has potential applications in the biomedical field and ocean exploration.

Chiral metasurfaces have great influence on the development of holography. Nonetheless, it is still challenging to design chiral metasurface structures on demand. As a machine learning method, deep learning has been applied to design metasurface in recent years. This work uses a deep neural network with a mean absolute error (MAE) of 0.03 to inverse design chiral metasurface. With the help of this approach, a chiral metasurface with circular dichroism (CD) values higher than 0.4 is designed. The static chirality of the metasurface and the hologram with an image distance of 3000 μm are characterized. The imaging results are clearly visible and demonstrate the feasibility of our inverse design approach.