Photoinduced non-reciprocal interactions in magnetic metals
Peer-Reviewed Publication
This month, we’re focusing on artificial intelligence (AI), a topic that continues to capture attention everywhere. Here, you’ll find the latest research news, insights, and discoveries shaping how AI is being developed and used across the world.
Updates every hour. Last Updated: 18-Nov-2025 09:11 ET (18-Nov-2025 14:11 GMT/UTC)
A theoretical framework predicts the emergence of non-reciprocal interactions that effectively violate Newton’s third law in solids using light, report researchers from Japan. They demonstrate that by irradiating light of a carefully tuned frequency onto a magnetic metal, one can induce a torque that drives two magnetic layers into a spontaneous, persistent “chase-and-run” rotation. This work opens a new frontier in non-equilibrium materials science and suggests novel applications in light-controlled quantum materials.
With heatwaves among Europe's deadliest climate hazards, a team of scientists led by CMCC has developed a prediction system capable of providing helpful information 4 to 7 weeks before summer, which gives valuable time to improve preparedness.
Trained on data from centuries of climate analysis up to recent years, the machine learning system has demonstrated an increase in forecast efficiency by drastically reducing the computational resources required, making these techniques accessible to a broader number of researchers and institutions.
Geiger-mode avalanche photodiodes (APDs) are capable of detecting single photons by harnessing a process called avalanche multiplication. 4H-SiC APDs have demonstrated high sensitivity in the deep ultraviolet range. However, at higher wavelengths of light, APDs require advanced architectures to improve their unity-gain quantum efficiency to maintain single-photon sensitivity. Optimizing avalanche photodiodes for high wavelength operation brings several design challenges. Researchers have now created a numerical model with a calibrated 4H-SiC material library for designing avalanche photodiodes for near-ultraviolet photodetection.
A research team has developed a causal deep learning model to personalize corticosteroid therapy for intensive care unit patients with sepsis. Using data from patients across two major databases, the model accurately identified which patients would benefit, not benefit, or be harmed by treatment. Patients with severe metabolic acidosis and circulatory dysfunction showed the greatest survival benefit. This breakthrough could potentially optimize critical care decisions, reduce treatment risks, and improve survival rates in sepsis.
In the study, researchers identified top-performing covalent organic frameworks (COFs) for both adsorption and membrane separation, showing that 3D COFs with small pores excel in adsorption, while 2D COFs with large pores are ideal for membrane separation. The team also uncovered key features governing COFs' separation performance, pointing to more efficient ways to extract helium from natural gas.