Clinical interviewing is an essential skill in medical training, but often requires significant time and training. Generative artificial intelligence (AI) can be used to help accelerate this training; however, its effectiveness remains unclear. Now, a new study reports how AI-based assessment of medical interview transcripts closely matches human-based assessments. These findings suggest that AI could support more efficient and scalable training in medical education.

Researchers have developed a stable, orally administrable "nanoreactor" using hydrogen-bonded organic frameworks (HOFs) that withstands the gastrointestinal environment to deliver protective enzymes. By targeting and degrading a specific aging-associated metabolite in the gut, this targeted therapy reduced neuroinflammation and alleviated cognitive decline in aged mice. This study offers a potential strategy for senescence intervention and the oral delivery of sensitive biologic drugs.

Ammonia is a vital chemical feedstock and a promising hydrogen carrier. Through the integration of in situ electron microscopy, spectroscopy, and theoretical calculations, the research team identified that during the ammonia synthesis process, oxygen defects on the surface of the catalyst support can store and transfer active nitrogen species, thereby generating surface nitrogen spillover. This effect accelerates nitrogen activation and enhances ammonia yield. This study elucidates a novel mechanism underlying the promotion of ammonia formation by the support in the Haber–Bosch process, providing new perspectives for the design of higher-efficiency ammonia synthesis catalysts.

A team of researchers from the Institute of Microelectronics of the Chinese Academy of Sciences (IMECAS) and Hebei University of Technology, in collaboration with Zhejiang Hikstor Technology, has developed a method to enable deterministic magnetization switching in perpendicular spin-orbit torque magnetic random access memory (SOT-MRAM) devices without the need for external magnetic fields. By controlling the chirality of magnetic domain walls through strain-engineered anisotropy and initial magnetic initialization, this new approach leverages time-reversal symmetry breaking to achieve efficient, reliable, and scalable memory switching. This scheme successfully demonstrates stable field-free switching of sub-100 nm devices on a 300-mm production line, providing a novel technical solution for the large-scale industrialization SOT-MRAM.

Wind and solar power have grown faster than almost anyone predicted but projecting their future expansion remains surprisingly difficult. Researchers at Chalmers University of Technology, Sweden, have developed what they call a computational “time machine” – a model that outperforms existing projection methods by using AI techniques to analyse historical growth patterns across countries. Their central projection shows that onshore wind is likely to supply around 25 per cent of global electricity by 2050, with solar reaching about 20 per cent. This is consistent with the 2°C target, but falls short of what is required for 1.5°C.

Lithium metal batteries promise dramatically higher energy density than today's lithium-ion technology, but their practical use has been limited by unstable interfaces and dangerous lithium dendrite growth. In this study, researchers developed a two-dimensional polymeric metal phthalocyanine layer that actively regulates anion movement and lithium-ion transport at the electrode–electrolyte interface. By guiding electrolyte anions toward the electrode surface and simultaneously accelerating lithium-ion conduction, the engineered layer promotes the formation of dense, lithium fluoride–rich interphases that stabilize lithium deposition. This molecularly designed interface significantly improves cycling stability, Coulombic efficiency, and safety under demanding operating conditions, offering a new strategy to unlock the long-term reliability of lithium metal and anode-free batteries.

Electric motors are getting smaller, lighter and more powerful – and that makes overheating a growing risk. A research team led by Professor Matthias Nienhaus at Saarland University has developed an AI-assisted method of determining the temperature distribution inside a running electric motor in real time – and without the need for additional hardware. The team will be showcasing the technology at the Hannover Messe (20–24 April) and is looking for industry partners to transfer it into practical and commercial applications. (Hall 11, Stand D41)