In this paper, a bimetallic sensor based on graphene-hexagonal boron nitride (hBN) heterostructure is theoretically studied. The sensitivity of the sensor can be improved by enhancing the Goos–Hänchen (GH) shift in the infrared band. The theoretical results show that adjusting the Fermi level, the number of graphene layers and the thickness of hBN, a GH shift of 182.09 λ can be obtained. Moreover, sensitivity of 2.02 × 10⁵ λ/RIU can be achieved with monolayer graphene, the thickness of gold layer is 20 nm, silver layer is 15 nm, and the hBN thickness of 492 nm. This heterogeneous infrared sensor has the advantages of high sensitivity and strong stability. The research results will provide a theoretical basis for the design of a new high-sensitivity infrared band sensor.

In this paper, we propose a high-sensitivity optical sensor at terahertz frequencies based on a composite structure containing a one-dimensional photonic crystal (1D PC) coated with a layer of monolayer graphene. Between the 1D PC and the graphene there is a sensing medium. This high-sensitivity phenomenon originates from the excitation of optical resonance between the graphene and the 1D PC. The proposed sensor is highly sensitive to the Fermi energy of graphene, the thickness and refractive index of the sensing medium, and the number of graphene layers. By selecting appropriate parameters, the maximum sensitivity ( ${407.36}^{\circ }/\mathrm{RIU}$ ) is obtained. We believe the proposed configuration is promising for fabricating graphene-based biosensor- or gas-sensor devices and other related applications in the terahertz band.

In recent years, rolling bearing fault diagnosis technology based on deep learning has provided a more intelligent and reliable method for the safe operation of mechanical systems due to its powerful feature learning ability. However, in real industrial scenarios, the acquisition of fault samples is limited and knotty, which makes it difficult for deep learning methods that require a large number of fault samples to be successful. To overcome the above problems, in this paper, a novel meta-learning network with adaptive input and attention mechanism is proposed for rolling bearing fault diagnosis with small samples under different working conditions. Firstly, inspired by the envelope demodulation signal processing method, an adaptive input (AI) length selection strategy considering the different working conditions is proposed, which improves the disadvantages of Gram angle field (GAF) two-dimensional coding method with the traditional fixed input. Secondly, the residual structure and attention mechanism are introduced to make the network have stronger feature extraction and generalization performance, and further improve the classification accuracy. Finally, the effectiveness of the method is verified on the Case Western Reserve University (CWRU) bearing fault datasets and the high-speed train axle box bearing fault datasets conducted by us. The results show that the proposed improved MAML network is superior to the other four mainstream meta-learning methods under the same conditions, and satisfactory fault diagnosis results can be obtained on the bearing fault datasets under different working conditions.

In this paper, a long-range surface plasmon resonance (LRSPR) structure with antimonene is proposed to enhance the photonic spin Hall effect (PSHE). By exploiting the LRSPR with antimonene, the maximum transverse displacement of the incident beam with a wavelength of 633 nm can reach 121.4λ (76.9 µm), which is much larger than the previously reported value in the insulator-metal LRSPR structure. Given the high sensitivity of spin splitting to alterations in the refractive index of the sensing layer, PSHE finds application in sensors. Remarkably, the LRSPR structure featuring antimonene demonstrates a sensitivity of 7 × 10⁴µm/RIU, an order of magnitude higher than that of conventional SPR structures. These results present a promising avenue for integrating PSHE into biosensing applications.

Time-reversal symmetry-breaking Weyl semimetals (WSMs) exhibit an interesting optical response in the mid-infrared band, offering what we believe to be a novel approach to study the manipulation of the photonic spin Hall effect (PSHE). This article theoretically investigates the PSHE phenomenon in a prism-coupled structure composed of the WSMs. It is found that the excitation of surface plasmon polaritons (SPPs) and the nonreciprocity inherent in WSMs have a positive effect on enhancing the PSHE phenomenon of the reflected light. Especially, the PSHE can also be manipulated by varying the separation distance and twist angle between the Weyl nodes. Through parameter optimization, we obtained a lateral displacement of 12.14 μm. The effects of the separation distance between Weyl nodes, the twist angle, and the incident light angle on the PSHE were further elucidated. We believe the tunable PSHE with WSMs can provide promising avenues for developing spintronic devices, as well as other photonic applications.