A research team has developed an innovative manufacturing process for permanent magnets that overcomes the limitations of conventional techniques. The team’s breakthrough significantly advances the diffusion technology, which is essential for improving magnetic performance, and creates new possibilities for applying high-efficiency magnets in eco-friendly industries such as electric vehicles, wind turbines, and robotics.

The findings are published in the Journal of Alloys and Compounds.

The joint research team from the Nano Technology Research Division at DGIST was led by Dr. Donghwan Kim and Dr. Jungmin Kim.

With the rapid growth of the electric vehicle and wind power sectors, the demand for powerful permanent magnets capable of stable operation at high temperatures has soared. A major example is the neodymium (Nd-Fe-B) permanent magnet, widely used in electric vehicle motors. However, these magnets experience a decline in magnetic performance under extreme heat, requiring the addition of heavy rare-earth elements such as terbium (Tb) and dysprosium (Dy) to maintain their strength. The challenge is that these elements are both rare and expensive.

To address this issue, the grain boundary diffusion process has been widely adopted. This technique enhances magnetic performance by infiltrating a small amount of heavy rare-earth material into the magnet’s surface. However, diffusion in this process is limited to the surface layer and does not penetrate into the magnet’s interior, making it difficult to apply to thick magnets.

To overcome this limitation, the research team combined spark plasma sintering, an advanced manufacturing technique, with the grain boundary diffusion process. By pre-mixing the diffusion material during the powder-based magnet fabrication stage, uniform diffusion was achieved throughout the magnet. Consequently, the diffusion depth increased markedly compared with that achieved by existing methods, allowing for the creation of a core–shell structure in which the magnet exhibits uniform and enhanced magnetic performance.

Remarkably, even with the same amount of rare-earth material, the new process achieved higher diffusion efficiency and significantly improved overall performance. This advancement makes it possible to produce magnets that are smaller and lighter while maintaining strong magnetic strength. It is expected to contribute to the miniaturization, weight reduction, and improved energy efficiency of electric vehicle motors. Additionally, the process shows great potential for application to large-scale magnets.

Principal Researcher Dr. Donghwan Kim stated, “This study presents a method that overcomes the limitations of the conventional grain boundary diffusion technology, enabling uniform performance throughout the magnet. It will make a significant contribution to the development of high-performance permanent magnets required in eco-friendly energy industries such as electric vehicles and wind power generation.”

The National Transportation Safety Board confirmed Sunday that it is investigating an airliner that was struck by an object in its windscreen, mid-flight, over Utah.

“NTSB gathering radar, weather, flight recorder data,” the federal agency said on the social media site X. “Windscreen being sent to NTSB laboratories for examination.”

The strike occurred Thursday, during a United Airlines flight from Denver to Los Angeles. Images shared on social media showed that one of the two large windows at the front of a 737 MAX aircraft was significantly cracked. Related images also reveal a pilot’s arm that has been cut multiple times by what appear to be small shards of glass.

Object’s Origin Not Confirmed

The captain of the flight reportedly described the object that hit the plane as “space debris.” This has not been confirmed, however.

After the impact, the aircraft safely landed at Salt Lake City International Airport after being diverted.

Images of the strike showed that an object made a forceful impact near the upper-right part of the window, showing damage to the metal frame. Because aircraft windows are multiple layers thick, with laminate in between, the window pane did not shatter completely. The aircraft was flying above 30,000 feet—likely around 36,000 feet—and the cockpit apparently maintained its cabin pressure.

So was it space debris? It is impossible to know without more data. A very few species of birds can fly above 30,000 feet. However, the world’s highest flying bird, Rüppell’s vulture, is found mainly in Africa. An unregulated weather balloon is also a possibility, although it’s not clear whether the velocity would have been high enough to cause the kind of damage observed. Hail is also a potential culprit.

Assuming this was not a Shohei Ohtani home run ball, the only other potential cause of the damage is an object from space.

That was the initial conclusion of the pilot, but a meteor is more likely than space debris. Estimates vary, but a recent study in the journal Geology found that about 17,000 meteorites strike Earth in a given year. That is at least an order of magnitude greater than the amount of human-made space debris that survives reentry through Earth’s atmosphere.

A careful analysis of the glass and metal impacted by the object should be able to reveal its origin.

This story originally appeared on Ars Technica.

Stanford researchers have developed an innovative computer vision model that recognizes the real-world functions of objects, potentially allowing autonomous robots to select and use tools more effectively.

In the field of AI known as computer vision, researchers have successfully trained models that can identify objects in two-dimensional images. It is a skill critical to a future of robots able to navigate the world autonomously. But object recognition is only a first step. AI also must understand the function of the parts of an object—to know a spout from a handle, or the blade of a bread knife from that of a butter knife.

Computer vision experts call such utility overlaps “functional correspondence.” It is one of the most difficult challenges in computer vision. But now, in a paper that will be presented at the International Conference on Computer Vision (ICCV 2025), Stanford scholars will debut a new AI model that can not only recognize various parts of an object and discern their real-world purposes but also map those at pixel-by-pixel granularity between objects.

A future robot might be able to distinguish, say, a meat cleaver from a bread knife or a trowel from a shovel and select the right tool for the job. Potentially, the researchers suggest, a robot might one day transfer the skills of using a trowel to a shovel—or of a bottle to a kettle—to complete a job with different tools.

“Our model can look at images of a glass bottle and a tea kettle and recognize the spout on each, but also it comprehends that the spout is used to pour,” explains co-first author Stefan Stojanov, a Stanford postdoctoral researcher advised by senior authors Jiajun Wu and Daniel Yamins. “We want to build a vision system that will support that kind of generalization—to analogize, to transfer a skill from one object to another to achieve the same function.”

Establishing correspondence is the art of figuring out which pixels in two images refer to the same point in the world, even if the photographs are from different angles or of different objects. This is hard enough if the image is of the same object but, as the bottle versus tea kettle example shows, the real world is rarely so cut-and-dried. Autonomous robots will need to generalize across object categories and to decide which object to use for a given task.

One day, the researchers hope, a robot in a kitchen will be able to select a tea kettle to make a cup of tea, know to pick it up by the handle, and to use the kettle to pour hot water from its spout.

Autonomy rules

True functional correspondence would make robots far more adaptable than they are currently. A household robot would not need training on every tool at its disposal but could reason by analogy to understand that while a bread knife and a butter knife may both cut, they each serve a specific purpose.

In their work, the researchers say, they have achieved “dense” functional correspondence, where earlier efforts were able to achieve only sparse correspondence to define only a few key points on each object. The challenge so far has been a paucity of data, which typically had to be amassed through human annotation.

“Unlike traditional supervised learning where you have input images and corresponding labels written by humans, it’s not feasible to humanly annotate thousands of pixels individually aligning across two different objects,” says co-first author Linan “Frank” Zhao, who recently earned his master’s in computer science at Stanford. “So, we asked AI to help.”

The team was able to achieve a solution with what is known as weak supervision—using vision-language models to generate labels to identify functional parts and using human experts only to quality-control the data pipeline. It is a far more efficient and cost-effective approach to training.

“Something that would have been very hard to learn through supervised learning a few years ago now can be done with much less human effort,” Zhao adds.

In the kettle and bottle example, for instance, each pixel in the spout of the kettle is aligned with a pixel in the mouth of the bottle, providing dense functional mapping between the two objects. The new vision system can spot function in structure across disparate objects—a valuable fusion of functional definition and spatial consistency.

Seeing the future

For now, the system has been tested only on images and not in real-world experiments with robots, but the team believes the model is a promising advance for robotics and computer vision. Dense functional correspondence is part of a larger trend in AI in which models are shifting from mere pattern recognition toward reasoning about objects. Where earlier models saw only patterns of pixels, newer systems can infer intent.

“This is a lesson in form following function,” says Yunzhi Zhang, a Stanford doctoral student in computer science. “Object parts that fulfill a specific function tend to remain consistent across objects, even if other parts vary greatly.”

Looking ahead, the researchers want to integrate their model into embodied agents and build richer datasets.

“If we can come up with a way to get more precise functional correspondences, then this should prove to be an important step forward,” Stojanov says. “Ultimately, teaching machines to see the world through the lens of function could change the trajectory of computer vision—making it less about patterns and more about utility.”