German scientists have achieved a breakthrough. They have created a novel material, CSiGeSn. This alloy combines carbon, silicon, germanium, and tin. The new compound is stable. Experts believe it will revolutionize electronics and quantum computing. The team used existing chip manufacturing technology. This ensures compatibility. The discovery paves the way for advanced components. It also allows for scalable production.
Researchers at Forschungszentrum Jülich have successfully created the world’s first experimentally verified two-dimensional half metal—a material that conducts electricity using electrons of just one spin type: either “spin-up” or “spin-down.” Their findings, now published as an Editors’ Suggestion in Physical Review Letters, mark a milestone in the quest for materials enabling energy-efficient spintronic that go beyond conventional electronics.
Half metals are key to spintronics: Unlike traditional conductors, half metals allow only one spin orientation to pass through. This makes them ideal candidates for spintronics, a next-generation information technology that leverages both the charge and the spin of electrons for data storage and processing. In conventional electronics, on the other hand, only the charge is used.
However, all known half metals operate only at ultra-low temperatures and lose their special properties at the surface—limiting their use. This was until now, when the team at Forschungszentrum Jülich engineered a 2D half metal in the form of an ultrathin alloy of iron and palladium, just two atoms thick, on a palladium crystal. Using a state-of-the-art imaging technique called spin-resolved momentum microscopy, they showed that the alloy allows only one spin type to conduct, confirming the long-sought 2D half-metallicity.
A team of scientists has developed a powerful new way to detect subtle magnetic signals in common metals like copper, gold, and aluminum—using nothing more than light and a clever technique. Their research, recently published in Nature Communications, could pave the way for advances in everything from smartphones to quantum computing.
For over a century, scientists have known that electric currents bend in a magnetic field—a phenomenon known as the Hall effect. In magnetic materials like iron, this effect is strong and well understood. But in ordinary, non-magnetic metals like copper or gold, the effect is much weaker.
In theory, a related phenomenon—the optical Hall effect—should help scientists visualize how electrons behave when light and magnetic fields interact. But at visible wavelengths, this effect has remained far too subtle to detect. The scientific world knew it was there, but lacked the tools to measure it.
Dr. Jagid and his team executed the implant on RJ just months ago.
“This device is completely invisible, you know, to anybody else that interacts with somebody who has it implanted. The other thing that makes it very unique is how it’s been miniaturized. It’s a very small device,” Dr. Jagid said.
During Neuralink’s summer update on the trial, they showed the moment one participant was able to move a cursor with his thoughts.
Wimbledon’s new automated line-calling system glitched during a tennis match Sunday, just days after it replaced the tournament’s human line judges for the first time.
The system, called Hawk-Eye, uses a network of cameras equipped with computer vision to track tennis balls in real-time. If the ball lands out, a pre-recorded voice loudly says, “Out.” If the ball is in, there’s no call and play continues.
However, the software temporarily went dark during a women’s singles match between Brit Sonay Kartal and Russian Anastasia Pavlyuchenkova on Centre Court.
View recent discussion. Abstract: Scaling language models unlocks impressive capabilities, but the accompanying computational and memory demands make both training and deployment expensive. Existing efficiency efforts typically target either parameter sharing or adaptive computation, leaving open the question of how to attain both simultaneously. We introduce Mixture-of-Recursions (MoR), a unified framework that combines the two axes of efficiency inside a single Recursive Transformer. MoR reuses a shared stack of layers across recursion steps to achieve parameter efficiency, while lightweight routers enable adaptive token-level thinking by dynamically assigning different recursion depths to individual tokens.