Naming Zhang

The insulation performance of vacuum arc interrupters greatly affects the reliability of vacuum circuit breakers. Optimizing the electric field distribution of a vacuum arc interrupter can improve its insulation performance. Here, we establish a two-dimensional model of vacuum arc interrupter with a shield system and study the electric field distribution of the vacuum arc interrupter under different structures through finite element simulation. Research has shown that as the electrode gap, contact chamfer radius, and main shield radius increase, the maximum value of the electric field in the vacuum arc interrupter decreases. The conductive rod at the static end of this vacuum arc interrupter is thickened, and the structure of the moving and static ends is asymmetric, resulting in asymmetric distribution of electric fields at the moving and static ends, which is not conducive to uniform electric fields. Optimization can be achieved by adjusting the structure of the main shielding cover. The research results will provide a basis for optimizing the electric field distribution of vacuum arc interrupters.

Pathogenic microbial aerosols pose a significant threat to human health. It is essential to quickly and effectively disinfect the air to prevent the spread of pathogens, enhance public health, and guarantee health and safety. Electromagnetic disinfection is a new type of disinfection technology, which has been proven to inactivate various bacteria, viruses, and other microorganisms, and has the advantages of high efficiency and environmental protection. This study proposed an electromagnetic disinfection-based method for microbial aerosol disinfection. Electromagnetic field theory was used to establish the cavity's starting size, and electromagnetic simulation software was used to model and simulate the cavity. Then, a cavity system was established and disinfection experiments were conducted on indoor air and high-concentration microbial aerosols. The results show that when sterilizing indoor natural air, using an electromagnetic field with a frequency of 2450 MHz and a power of 200 W for 70 seconds can kill 93% of the bacteria in the air. In high-concentration aerosol disinfection experiments, the disinfection effect can be significantly improved as the power and action time increases. Most microorganisms can be removed when the electromagnetic field power is 200 W and the action time is 2 minutes. These results indicate that electromagnetic disinfection devices can effectively eliminate microorganisms in aerosols and provide a new method for controlling disease epidemics.

Our study focused on investigating the bioeffects of extremely low‐frequency electromagnetic fields (ELF‐EMFs) on the immune function of the spleen. We designed an electromagnetic instrument that can locally target on spleen, the spleens of mice were locally exposed to the ELF‐EMF (50 Hz, 30 mT) for 14 days (4 h/day). Parallelly, the isolated splenic T cells were exposed to ELF‐EMF (50 Hz, 15 mT) for 2 h. After the exposure, the splenocyte showed a reduced apoptosis rate. Among the splenocytes, the CD4⁺ T cells and natural killer cells accumulated, the percentage of B cells decreased. In vitro study demonstrated that ELF‐EMF induced the alteration of T cell subsets, showing an increased percentage of CD4⁺ T cells and a decreased percentage of CD8⁺ T cells. Within CD4⁺ T cells, the population of T helper (Th) 17 cells increased, and the population of regulatory T cells (Treg) cells decreased. The enrichment of the nuclear factor (NF)‐κB pathway in the splenic T cells was found to be reduced after exposure to ELF‐EMF. Our findings suggest that ELF‐EMF regulated the immune function of the spleen by changing the proportion of immune cells in the spleen. Specifically, the differentiation of spleen T cells was induced by ELF‐EMF toward Th17 cells and inhibited by ELF‐EMF into Treg cells. The NF‐κB signaling pathway probably accounts for the effects of ELF‐EMF on the spleen T cells.

The accurate monitoring of the internal hotspot temperature in transformers is crucial for ensuring the stability of power grid operations. Traditional methods typically measure only the surface temperature of transformers, whereas this study proposes a non-invasive thermal inversion algorithm based on a wireless mesh network that effectively predicts the internal hotspot temperature. An electromagnetic-thermal-fluid coupling simulation model was developed to simulate the temperature distribution in transformers under various operating conditions. Subsequently, this study employed a Support Vector Regression (SVR) algorithm to train the sample dataset, optimizing the SVR model using a grid search and cross-validation to enhance the predictive accuracy. After training, the model estimates the hotspot temperature based on surface measurements obtained through a non-contact infrared sensor network. The wireless mesh network, based on the Wi-Fi protocol, provides robust and real-time monitoring even in harsh environments, with data transmitted to a central root node via multiple sensor nodes. The experimental results demonstrate that this method is highly accurate, with predicted temperatures closely matching the results from traditional measurement techniques. This method enhances transformer condition monitoring, helping to extend the transformer lifespan and improve power grid stability.