Due to the weak spin-orbit interaction, the photonic spin Hall effect (SHE) is generally very weak, which is disadvantageous for potential applications of nanophotonic devices. Surface plasmon resonance (SPR) has been proposed to enhance the photonic SHE. In this paper, by covering the heterostructure material composed of graphene and molybdenum sulfide (MoS ₂ ) on the SPR structure, it is demonstrated that the two-dimensional (2-D) material heterostructure can effectively enhance the photonic SHE. The transverse shift of light beam in the heterostructure is larger than those in SPR structure due to the larger refractive index gradient in the heterostructure. The maximum transverse shift can be up to 27.5 μm under the optimized parameters, which is much larger than the previously reported values. This interesting phenomenon is attributed to the larger light absorption and hence the refractive index variation gradient of graphene/MoS ₂ heterostructure. These findings provide us with a new way to modulate the photonic SHE, and also establish foundation for developing nanophotonic devices based on 2-D nanomaterials heterostructure.

Hexagonal nanosheets of Sb2Te3 single crystals with uniform morphology are successfully synthesized through a solvothermal method. We experimentally demonstrate that topological insulator Sb2Te3 as an optical media can be used for the generation of ring-shaped beams based on a giant optical nonlinearity and the effect of graded-index plasma lens. Good propagation properties in free space are exhibited by the generated ring-shaped beam and the dark spot size of ring-shaped beams can be conveniently controlled by adjusting the power of the pump beam. We also find that it is more easily to generate a ring-shaped beam in the high concentration Sb2Te3 dispersion solution and the divergence angle of the ring-shaped beam increases as the concentration of dispersion solution increase.

Two-dimensional Bi₂Se₃ nanosheets with hexagonal shape are synthesized by a solution synthetic route. The Bi₂Se₃ nanosheets are 120 nm in edge width and 7 nm in thickness. The size of the Bi₂Se₃ nanosheets can be controlled by choosing different kinds of reducing agents including hydroxylamine and ethylenediamine. Subsequently, we demonstrate a configuration of two-color all-optical switching based on plasma channels effect using the as-synthesized Bi₂Se₃ nanosheets as an optical media. The signal light can be modulated as two states including dot and ring shape by changing the intensity of control light. The modulated signal light exhibits excellent spatial propagation properties. As a type of interesting optical material, ultrathin two-dimensional Bi₂Se₃ nanosheets might provide an effective option for photoelectric applications.

The graphene oxide (GO) is successfully prepared from a purified natural graphite through a pressurized oxidation method. We experimentally demonstrate that GO as an optical media can be used for spatial light modulation based on plasma channels induced by femtosecond pulses. The modulated beam exhibits good propagation properties in free space. It is easy to realize the spatial modulation on the probe beam at a high concentration of GO dispersion solutions, high power and smaller pulse width of the pump beam. We also find that the spatial modulation on the probe beam can be conveniently adjusted through the power and pulse width of pump lasers, dispersion solution concentration.

Nowadays, IoT technology is developing rapidly and RFID (Radio Frequency Identification) based indoor positioning problems can be performed using deep learning and intelligent optimization algorithms. Deep models can analyze and predict the localization problem as a regression problem to achieve high accuracy positioning. Meanwhile, to ensure the accuracy of the model, we need to find excellent hyperparameters, which requires the support of optimization algorithms, but existing optimization algorithms do not allow flexible adaptation according to the optimization phase and there is room for improvement. In this paper, we propose a deep model, called CTT, and a multi-objective evolutionary algorithm (MOEA) based on a neighborhood adaptive adjustment strategy, called MOEA-NAAS. The experimental results show that CTT optimized by the NAAS algorithm is significantly more accurate and stable in the localization problem, with significant improvements in the three main metrics, proving the usability of the optimization algorithm. At the same time, the localization effect of the CTT also shows obvious advantages. In the future, the optimized algorithm can be combined with other deep models and widely used in various high-precision indoor positioning.