In this work, a surface plasmon resonance (SPR) sensor based on a D-shaped germanium-doped photonic crystal fiber (PCF) is proposed. The finite element method (FEM) is introduced to analyze the structure parameters, such as germanium-doped concentration, lattice pitch, and air hole size. In addition, the coupling properties and birefringence properties of PCF are also studied. The computer simulation results indicate that two different surface plasmon polariton (SPP) coupling modes are produced on the polished surface, covered with metal film, when the analyte refractive index (RI) is $1.34$. Then, with the increase of the RI, the incompleteness of one of the coupling modes will be transformed into the complete coupling. The effect of germanium concentration on the birefringence is also analyzed. It has an optimal wavelength sensitivity of 5600 nm/RIU when the RI is 1.37. This sensor exhibits a maximum birefringence of 1.06 × 10⁻² and a resolution of 1.78 × 10⁻⁵ RIU with high linearity.

The virtual synchronous generator (VSG) exhibits high-dimensional complexity, necessitating a tradeoff between accuracy and complexity when constructing small-signal models. To address this issue, this article proposes a data-driven modeling approach based on long short-term memory (LSTM) neural networks. The focus is on mapping relationships between electrical quantities, considering the influence of irrational factors on model accuracy. A detailed data-driven modeling approach for VSG is proposed and verified in this article. Due to the time-series correlation in the electrical data generated during VSG operation, the LSTM algorithm, known for its excellent time-series prediction capabilities, is chosen to construct the VSG data-driven model. Several complex VSG application scenarios are used to validate the effectiveness and accuracy of the proposed modeling approach. In conclusion, the LSTM-based VSG data-driven modeling outperforms small-signal models and recurrent neural network data-driven models in terms of accuracy and stability.

Circular dichroism (CD) effects have broad applications in fields including biophysical analysis, analytical chemistry, nanoscale imaging, and nanosensor design. Herein, a novel design of a tilted F-type chiral metal nanostructure composed of circular nanoholes with varying radii has been proposed to achieve remarkable CD effects, and the results demonstrate the generation of a significant current oscillation at the sharp edges where the nanoholes overlap under circularly polarized light, resulting in a strong CD effect. The CD effect can reach up to 7.5%. Furthermore, spectral modulation of the resonant wavelength can be achieved by adjusting the structural parameters, which enhances the tunability of the structure. Overall, these results provide theoretical or practical guidance for enhancing the circular dichroism signal strength of chiral metal nanostructures and designing new types of two-dimensional chiral structures.

Aiming at the problems of promoting new energy consumption, reducing carbon emissions, load fluctuations, and multi-agent conflict of interests in the networked microgrid system, this article proposes a microgrid optimization operation strategy based on demand response and reward-penalty ladder-type carbon trading mechanism. First, in order to determine the electricity sales price of the system, an optimal scheduling model for microgrid operators is established, including gas cost, electricity profit for users, and surplus power supply network profit. Second, a demand response strategy on electricity price and low-carbon compensation incentives is proposed on the user side. The transaction model is embedded between microgrid operators and users into the master–slave game framework, and a multi-slave game collaborative optimization model is established with microgrid operators as leaders and users as followers. The existence and uniqueness of Stackelberg game are proved, and the differential evolution algorithm and CPLEX solver are used to solve the proposed model. Finally, an example of a microgrid system including three community users is provided to show the effectiveness of the proposed model and strategy.

A prismatic excitation-based affinity biosensor consisting of the prism (BK7), WS₂/graphene hybrid nanosheets, and silver (Ag) as the active metal for the surface plasmon resonance is proposed in this present research. The introduction of the transition metal WS₂/graphene layer protected the silver substrate and enhanced the adsorption of biomolecules, which facilitated the quality and performance of detection. Here, we improved the detection structure by focusing on the metallic materials, graphene and WS₂ film layers, and the thickness of the measured medium on the sensing effect. The results show that the silver film had a more desirable resonance effect, and the design of the symmetric detection structure produced a double resonance peak, and it provides a reference for distributed sensing. Changing the thickness of the detection medium can dynamically adjust the wave vector matching conditions, which gives the sensor a certain tunability. In the bilayer WS₂ and monolayer graphene (W = 2, G = 1) configuration, the sensitivity was up to 224 deg/RIU with a quality factor of 96.97 RIU⁻¹, which has potential for clinical analytic and biochemical detecting applications.