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This unique material can behave like a fluid, flowing and deforming with minimal resistance, yet it can also instantly become rigid, acting like a solid. It’s called PAM (or Polycatenated Architected Material). Its unique structure, inspired by chain mail, features interlinked shapes forming intricate three-dimensional networks. Unlike traditional materials, which are either solid with fixed structures or granular with loose, independent particles, PAMs occupy a fascinating middle ground. When subjected to shear stress, for example, the interconnected components can slide past each other, offering little resistance, much like water or honey. However, when compressed, these same components lock together, creating a rigid structure. This transition between fluid and solid-like behavior is what makes PAMs so unique. PAMs represent a new class of matter, defying the traditional classification of materials as either solid or granular. They are a hybrid, bridging the gap between these two extremes. This dynamic behavior is achieved through the intricate design of PAMs. Researchers at Caltech create these materials using 3D printing. They begin by modeling the structures on a computer, mimicking crystal lattices but replacing the fixed particles with interconnected rings or cages. These designs are then brought to life using various materials, from polymers to metals. The resulting PAMs, often small cubes or spheres, undergo rigorous testing to understand their response to different forces. They are compressed, sheared, and twisted, revealing their unusual properties. The potential applications for PAMs are vast and varied. Their ability to absorb energy efficiently makes them ideal candidates for protective gear, such as helmets, potentially offering superior protection compared to current foam-based solutions. This same property could also be utilized in packaging and other applications requiring cushioning or stabilization. Experiments with microscale PAMs have shown that they respond to electrical charges, suggesting possibilities in biomedical devices and soft robotics. Researchers are also exploring the vast design space of PAMs, using advanced techniques like artificial intelligence to discover new configurations and functionalities. While still in its early stages, PAM research promises to revolutionize material science and engineering, opening up new possibilities for a wide range of applications.

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Imagine smartphones that can diagnose diseases, detect counterfeit drugs or warn of spoiled food. Spectral sensing is a powerful technique that identifies materials by analyzing how they interact with light, revealing details far beyond what the human eye can see.

Traditionally, this technology required bulky, expensive systems confined to laboratories and industrial applications. But what if this capability could be miniaturized to fit inside a smartphone or ?

Researchers at Aalto University in Finland have combined miniaturized hardware and intelligent algorithms to create a powerful tool that is compact, cost-effective, and capable of solving real-world problems in areas such as health care, food safety and autonomous driving. The research is published in the journal Science Advances.

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This new theoretical framework based on simulations allowed scientists to predict and design materials that exhibit almost no change in size with temperature.

With this new understanding, the team set out to create an even better material. And they succeeded.

Researchers developed a new alloy, the pyrochlore magnet, which exhibits even less thermal expansion than Invar.

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Researchers have used quantum physics and machine learning to quickly and accurately understand a mound of data – a technique, they say, could help extract meaning from gargantuan datasets.

Their method works on groundwater monitoring, and they’re trialling it on other fields like traffic management and medical imaging.

“Machine learning and artificial intelligence is a very powerful tool to look at datasets and extract features,” Dr Muhammad Usman, a quantum scientist at CSIRO, tells Cosmos.

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Early detection of earthquakes could be vastly improved by tapping into the world’s internet network with a groundbreaking new algorithm, researchers say.

Fiber used for cable television, telephone systems and the global web matrix now have the potential to help measure seismic rumblings thanks to recent technological advances, but harnessing this breakthrough has proved problematic.

A new paper published today in Geophysical Journal International seeks to address these challenges by adapting a simple physics-based algorithm to include fiber optic data that can then be used hand-in-hand with traditional seismometer measurements.

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New observational data from the James Webb Space Telescope and simulation models have confirmed a new type of planet unlike anything found in the Solar System. This provides another piece of the puzzle to understand how planets and planetary systems form.

To date, more than 5,000 exoplanets have been confirmed around stars other than the Sun.

Many exoplanets are unlike any of the planets in the Solar System, making it difficult to guess their true natures.

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A tool developed by Keele University researchers has been shown to help detect fake news with an impressive 99% level of accuracy, offering a vital resource in combating online misinformation.

The researchers Dr. Uchenna Ani, Dr. Sangeeta Sangeeta, and Dr. Patricia Asowo-Ayobode from Keele’s School of Computer Science and Mathematics, used a number of different machine learning techniques to develop their model, which can scan news content to give a judgment of whether a news source is trustworthy and genuine or not.

The method developed by the researchers uses an “ensemble voting” technique, which combines the predictions of multiple different machine learning models to give an overall score.

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