Researchers at Hokkaido University and Duke University have developed a hydrogel that heals and strengthens itself as it is overloaded and damaged. The proof-of-concept demonstration could lead to improved performance for situations where soft but durable materials are required, such as load-bearing connections and joints within machines, robots and even people.

A team of scientists from Princeton University has measured the energies of electrons in a new class of quantum materials and has found them to follow a fractal pattern. Fractals are self-repeating patterns that occur on different length scales and can be seen in nature in a variety of settings, including snowflakes, ferns, and coastlines. A quantum version of a fractal pattern, known as “Hofstadter’s butterfly,” has long been predicted, but the new study marks the first time it has been directly observed experimentally in a real material. This research paves the way toward understanding how interactions among electrons, which were left out of the theory originally proposed in 1976, give rise to new features in these quantum fractals. 

The study was made possible by a recent breakthrough in materials engineering, which involved stacking and twisting two sheets of carbon atoms to create a pattern of electrons that resembles a common French textile known as a moiré design. 

New double network hydrogel technology features automated self-strengthening that rapidly activates upon deformation of its polymer network.

As electric vehicles (EVs) gain momentum in the fight against climate change, the conversation around public charging infrastructure is growing increasingly complex. Xinwu Qian , assistant professor of civil and environmental engineering at Rice University, is spearheading research that reimagines how and where charging stations should be deployed — ensuring that alignment with people’s daily routine and activities, beyond mere accessibility, are at the forefront.

Most AI diagnostic tools are black boxes, but the new approach allows doctors and patients to understand how the computer reached a diagnosis.

By watching their own motions with a camera, robots can teach themselves about the structure of their own bodies and how they move, a new study from researchers at Columbia Engineering now reveals. Equipped with this knowledge, the robots could not only plan their own actions, but also overcome damage to their bodies.

"Like humans learning to dance by watching their mirror reflection, robots now use raw video to build kinematic self-awareness," says study lead author Yuhang Hu, a doctoral student at the Creative Machines Lab at Columbia University, directed by Hod Lipson, James and Sally Scapa Professor of Innovation and chair of the Department of Mechanical Engineering. "Our goal is a robot that understands its own body, adapts to damage, and learns new skills without constant human programming."

Ethylene oxide is a “platform chemical” with a $40 billion annual worldwide market used in the production of plastics, textiles and many other common products. Tufts University chemists discovered an inexpensive way to reduce CO₂ emissions and decrease the need for chlorine to produce the chemical. 

esearchers at Purdue University have developed a computational solution to this problem named NuFold, which will model 3D RNA structures that could expedite medical discovery decades ahead of schedule. The research team, led by Daisuke Kihara, professor of biological sciences and computer science in the Purdue College of Science, published their findings in Nature Communications.