The breakthrough by researchers at the University of Houston has transformed ceramics from fragile and brittle materials into tough, flexible structures. By blending ancient design with modern materials science, they have created a new class of ceramic structures that can bend under pressure without breaking.

Traditionally, ceramics were known for their inability to withstand stress, making them unsuitable for high-impact or adaptive applications. However, this limitation may soon change as the UH researchers have shown that origami-inspired shapes with a soft polymer coating can transform fragile ceramic materials into resilient and adaptable structures.

Led by Maksud Rahman, assistant professor of mechanical and aerospace engineering, and Md Shajedul Hoque Thakur, postdoctoral fellow, the team has successfully 3D printed ceramic structures based on the Miura-ori origami pattern. This innovative approach allowed them to create materials that can handle stress in ways ordinary ceramics cannot.

The coated structures flexed and recovered when compressed in different directions, while their uncoated counterparts cracked or broke. The researchers tested these structures under both static and cyclic compression, with computer simulations backing up their experiments. The results consistently showed greater toughness in the coated versions, especially in directions where the original ceramic was weakest.

“This work demonstrates how folding patterns can unlock new functionalities in even the most fragile materials,” said Rahman. “Origami is more than an art – it’s a powerful design tool that can reshape how we approach challenges in both biomedical and engineering fields.”

The potential applications for this technology are vast, ranging from medical prosthetics to impact-resistant components in aerospace and robotics. With their newfound ability to create lightweight yet tough materials, researchers may soon revolutionize various industries and transform ceramics into versatile and reliable materials for future innovations.

MIT researchers have created a groundbreaking periodic table that reveals how more than 20 classical machine-learning algorithms are connected. This innovative framework sheds light on how scientists can fuse strategies from different methods to improve existing AI models or come up with new ones.

The researchers used their framework to combine elements of two different algorithms to create a new image-classification algorithm that performed 8 percent better than current state-of-the-art approaches. This breakthrough demonstrates the potential of the periodic table to unlock AI discovery and innovation.

The periodic table stems from one key idea: All these algorithms learn a specific kind of relationship between data points. While each algorithm may accomplish that in a slightly different way, the core mathematics behind each approach is the same. Building on these insights, the researchers identified a unifying equation that underlies many classical AI algorithms.

They used this equation to reframe popular methods and arrange them into a table, categorizing each based on the approximate relationships it learns. Just like the periodic table of chemical elements, which initially contained blank squares that were later filled in by scientists, the periodic table of machine learning also has empty spaces.

These spaces predict where algorithms should exist, but which haven’t been discovered yet. The researchers filled one gap by borrowing ideas from a machine-learning technique called contrastive learning and applying them to image clustering. This resulted in a new algorithm that could classify unlabeled images 8 percent better than another state-of-the-art approach.

The flexible periodic table allows researchers to add new rows and columns to represent additional types of datapoint connections. Ultimately, having I-Con as a guide could help machine learning scientists think outside the box, encouraging them to combine ideas in ways they wouldn’t necessarily have thought of otherwise.

This research was funded, in part, by the Air Force Artificial Intelligence Accelerator, the National Science Foundation AI Institute for Artificial Intelligence and Fundamental Interactions, and Quanta Computer. The researchers’ work will be presented at the International Conference on Learning Representations.

The tiny parasitic worm, nematode, has long been a subject of fascination for scientists. These creatures can jump as high as 20 times their body length, which is an incredible feat considering they don’t have legs. Inspired by this remarkable ability, researchers at Georgia Tech have created a soft robot that can leap 10 feet high without any legs.

The robot’s design is based on the unique way nematodes move. They can bend their bodies into different shapes to propel themselves forward and backward. By watching high-speed videos of these creatures, the researchers were able to develop simulations of their jumping behavior. This led them to create soft robots that could replicate the leaping worms’ movement.

The key to the robot’s success lies in its ability to store energy when it kinks its body. This stored energy is then rapidly released to propel the robot forward or backward. The researchers found that by reinforcing the robot with carbon fibers, they could accelerate the jumps and make them more efficient.

This breakthrough has significant implications for robotics and engineering. With the ability to create simple elastic systems made of carbon fiber or other materials, engineers can design robots that can hop across various terrain. This technology could be used in search and rescue missions where robots need to traverse unpredictable terrain and obstacles.

Lead researcher Sunny Kumar said, “We’re not aware of any other organism at this tiny scale that can efficiently leap in both directions at the same height.” The researchers are continuing to study the unique way nematodes use their bodies to move and build robots to mimic them. This research has the potential to lead to innovative solutions for robotics and engineering.

Associate Professor Saad Bhamla’s lab collaborated on this project with researchers from the University of California, Berkeley, and the University of California, Riverside. The study was published in Science Robotics on April 23.