Falling off a horse at high-speed changes the impact to the rider’s head and the parameters for a quality helmet, according to new research from the Virginia Tech Helmet Lab.  Published on April 28 in the Annals of Biomedical Engineering, the findings from researchers Steve Rowson and Lauren Duma indicate that head impacts during falls at high speed generate unique head rotation, which in turn, directly affects helmet behavior. 

Researchers at UCLA have developed a new catalyst design capable of pushing the projected fuel cell catalyst lifespans to 200,000 hours. Published in Nature Nanotechnology, the research marks a significant step toward the widespread adoption of fuel cell technology in heavy-duty vehicles, such as long-haul tractor trailers. While platinum-alloy catalysts have historically delivered superior chemical reactions, the alloying elements leach out over time, diminishing catalytic performance. The degradation is further accelerated by the demanding voltage cycles required to power heavy-duty vehicles. To address this challenge, the UCLA team has engineered a durable catalyst architecture with a novel design that shields platinum from the degradation typically observed in alloy systems.

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.