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 world of tissue engineering has just taken a significant leap forward with the advent of Freeform Reversible Embedding of Suspended Hydrogels (FRESH) 3D bioprinting. This innovative technique, developed by Carnegie Mellon’s Feinberg lab, allows for the printing of soft living cells and tissues with unprecedented structural resolution and fidelity. The result is a microphysiologic system entirely made out of collagen, cells, and other proteins – a first-of-its-kind achievement that expands the capabilities of researchers to study disease and build tissues for therapy.

Traditionally, tiny models of human tissue have been made using synthetic materials like silicone rubber or plastics, but these cannot fully recreate normal biology. With FRESH bioprinting, researchers can now create microfluidic systems in a Petri dish entirely out of collagen, cells, and other proteins – a major breakthrough that will revolutionize the field.

“We’re hoping to better understand what we need to print,” said Adam Feinberg, a professor of biomedical engineering and materials science & engineering at Carnegie Mellon University. “Ultimately, we want the tissue to better mimic the disease of interest or ultimately, have the right function, so when we implant it in the body as a therapy, it’ll do exactly what we want.”

The implications of this technology are vast, with potential applications in treating Type 1 diabetes and other diseases. FluidForm Bio, a Carnegie Mellon University spinout company, has already demonstrated that they can cure Type 1 diabetes in animal models using this technology, and plans to start clinical trials in human patients soon.

As Feinberg emphasized, “The work we’re doing today is taking this advanced fabrication capability and combining it with computational modeling and machine learning… We see this as a base platform for building more complex and vascularized tissue systems.”

With FRESH bioprinting, the possibilities are endless. This technology has the potential to change the face of medicine and improve countless lives. As researchers continue to push the boundaries of what is possible, one thing is certain – we will witness some incredible breakthroughs in the years to come.