In recent years, with the rapid development of the new energy vehicle industry, the endurance and energy density of lithium batteries have been significantly improved. However, this advancement has correspondingly increased the risks associated with battery failures. Consequently, implementing safety failure analysis and early warning mechanisms for lithium batteries has become critically important. Conducting dynamic analysis of the entire lifecycle process – including encapsulation, electrolyte filling, charging/discharging, and damage – can effectively guide battery manufacturing and usage practices, thereby advancing next-generation battery development.

Current battery health monitoring methodologies encompass solutions such as radiography, thermal imaging, ultrasonic testing, and internal stress detection. Among these, ultrasound technology stands out as an exceptionally suitable "CT" imaging tool for lithium batteries due to its superior penetration capability, rapid response speed, high spatial resolution, and real-time monitoring capacity, which enable distinct responses to various internal evolution processes. Notably, fiber-optic ultrasound solutions, in particular, offer miniaturization, high sensitivity, resolution, and penetration depth, positioning them as a promising technology for battery diagnostics.

A research team from the Institute of Modern Physics (IMP), Chinese Academy of Sciences (CAS), has developed a machine learning (ML) model enhanced with expert knowledge to classify eight types of faults in superconducting radio-frequency (SRF) cavities at the China Accelerator Facility for superheavy Elements (CAFE2) facility. This model helps operators quickly identify fault patterns after incidents, speeding recovery and supporting long-term stable SRF accelerator operation.

SAN ANTONIO — April 28, 2025 —The NASA New Horizons spacecraft’s extensive observations of Lyman-alpha emissions have resulted in the first-ever map from the galaxy at this important ultraviolet wavelength, providing a new look at the galactic region surrounding our solar system. The findings are described in a new study authored by the SwRI-led New Horizons team.

The popularization and diffusion of compound-eye array camera technology faces formidable challenges. On the one hand, the high-resolution realization of compound-eye array camera systems usually relies on a large-scale number of cameras and high-pixel-density image sensors, with high system complexity and limited imaging real-time. Zoom imaging technology is expected to reduce the number of cameras and the need for sensor pixel density and improve imaging adaptability while taking into account the large field of view and high-resolution imaging capability of the compound eye. However, the traditional mechanical zoom method is slow and lacks dynamic responsiveness, and the introduction of compound-eye array cameras will cause a drastic increase in the size, weight, and power consumption, which makes it difficult to apply to compound-eye array cameras. On the other hand, the compound-eye array camera is susceptible to the interference of the imaging environment during the actual imaging, resulting in the degradation of the imaging quality and difficulty in giving full play to its resolution advantage, and due to the variability of the environmental interference factors and the inherent manufacturing tolerances caused by the variability between the sub-camera units, the traditional image processing algorithms are often difficult to complete the image information demodulation and enhancement of the compound-eye array camera. Therefore, the realization of fast optical zoom and high-fidelity resolution enhancement in compound-eye array cameras remains a key challenge to be solved.