With the rapid spread of generative AI, the demand for more energy-efficient methods of powering the hardware is becoming apparent. Now, researchers have succeeded in applying on-axis magnetron sputtering on thulium iron garnet (TmIG)—a promising material that can enable high-speed, low-power information rewriting at room temperature—to build more energy-efficient magnetic random-access memory.

Levitation has long been pursued by stage magicians and physicists alike. For audiences, the sight of objects floating midair is wondrous. For scientists, it’s a powerful way of isolating objects from external disturbances. This is particularly useful in case of rotors, as their torque and angular momentum, used to measure gravity, gas pressure, momentum, among other phenomena in both classical and quantum physics, can be strongly influenced by friction. Freely suspending the rotor could drastically reduce these disturbances – and now, researchers from the Okinawa Institute of Science and Technology (OIST) have designed, created, and analyzed such a macroscopic device, bringing the magic of near-frictionless levitation down to earth through precision engineering. 

Researchers have created a polymer “Chinese lantern” that can snap into more than a dozen curved, three-dimensional shapes by compressing or twisting the original structure. This rapid shape-shifting behavior can be controlled remotely using a magnetic field, allowing the structure to be used for a variety of applications.

AI can be used to detect cervical cancer in women in resource-limited parts of the world. However, for this method to work, investments are needed in healthcare staff, reliable supply chains and trust in these communities. This has been shown in a new study from Uppsala University, Karolinska Institutet and the University of Helsinki, where researchers tested an AI-supported diagnostic method at rural hospitals in Kenya and Tanzania.

Tilted metasurface nanostructures, with excellent physical properties and enormous application potential, pose an urgent need for manufacturing methods. Here, electric-field-driven generative-nanoimprinting technique is proposed. The electric field applied between the template and the substrate drives the contact, tilting, filling, and holding processes. By accurately controlling the introduced included angle between the flexible template and the substrate, tilted nanostructures with a controllable angle are imprinted onto the substrate, although they are vertical on the template. By flexibly adjusting the electric field intensity and the included angle, large-area uniform-tilted, gradient-tilted, and high-angle-tilted nanostructures are fabricated. In contrast to traditional replication, the morphology of the nanoimprinting structure is extended to customized control. This work provides a cost-effective, efficient, and versatile technology for the fabrication of various large-area tilted metasurface structures. As an illustration, a tilted nanograting with a high coupling efficiency is fabricated and integrated into augmented reality displays, demonstrating superior imaging quality.

One of the key steps in developing new materials is “property identification,” which has long relied on massive amounts of experimental data and expensive equipment, limiting research efficiency. A KAIST research team has introduced a new technique that combines “physical laws,” which govern deformation and interaction of materials and energy, with artificial intelligence. This approach allows for rapid exploration of new materials even under data-scarce conditions and provides a foundation for accelerating design and verification across multiple engineering fields, including materials, mechanics, energy, and electronics.

KAIST (President Kwang Hyung Lee) announced on the 2nd of October that Professor Seunghwa Ryu’s research group in the Department of Mechanical Engineering, in collaboration with Professor Jae Hyuk Lim’s group at Kyung Hee University (President Jinsang Kim) and Dr. Byungki Ryu at the Korea Electrotechnology Research Institute (President Namkyun Kim), proposed a new method that can accurately determine material properties with only limited data. The method uses Physics-Informed Machine Learning (PIML), which directly incorporates physical laws into the AI learning process.