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Novel catalyst design could make green hydrogen production more efficient and durable

A new type of catalyst—a material that speeds up chemical reactions—that could make the production of clean hydrogen fuel more efficient and long-lasting has been developed by a team led by City University of Hong Kong, including researchers from Hong Kong, mainland China, and Japan.

This breakthrough uses high-density single atoms of iridium (a rare metal) to greatly improve the process of splitting water into and , which is key to like hydrogen fuel cells and large-scale energy storage.

The researchers created a highly stable and active by placing single iridium atoms on ultra-thin sheets made of cobalt and cerium compounds. Called CoCe–O–IrSA, the final product performs exceptionally well in the water-splitting process. It requires very little extra energy (just 187 mV of overpotential at 100 mA cm-2) to drive the oxygen evolution reaction at a high rate, and it stays stable for more than 1,000 hours under demanding conditions.

New light-powered gears fit inside a strand of hair

Researchers at the University of Gothenburg have made light-powered gears on a micrometer scale. This paves the way for the smallest on-chip motors in history, which can fit inside a strand of hair. The research is published in the journal Nature Communications.

Gears are everywhere—from clocks and cars to robots and wind turbines. For more than 30 years, researchers have been trying to create even smaller gears in order to construct micro-engines. But progress stalled at 0.1 millimeters, as it was not possible to build the drive trains needed to make them move any smaller.

Researchers from Gothenburg University, among others, have now broken through this barrier by ditching traditional mechanical drive trains and instead using to set the gears in motion directly.

MicroBooNE detector excludes electron neutrino cause of MiniBooNE anomaly

A recent Physical Review Letters publication presents a thorough analysis of MicroBooNE detector data, investigating the anomalous surplus of neutrino-like events detected by the preceding MiniBooNE experiment.

In 1990, the LSND (Liquid Scintillator Neutrino Detector) experiment observed an anomalous signal indicating the potential existence of sterile neutrinos—a fourth neutrino species beyond the three established flavors (electron, muon, and tau neutrinos).

MiniBooNE was constructed to examine this anomaly utilizing the same neutrino beam methodology. However, instead of resolving the mystery, MiniBooNE discovered an anomaly of its own.

‘Like talking on the telephone’: Quantum computing engineers get atoms chatting long distance

UNSW engineers have made a significant advance in quantum computing: they created ‘quantum entangled states’—where two separate particles become so deeply linked they no longer behave independently—using the spins of two atomic nuclei. Such states of entanglement are the key resource that gives quantum computers their edge over conventional ones.

The research is published in the journal Science, and is an important step toward building large-scale quantum computers—one of the most exciting scientific and technological challenges of the 21st century.

Lead author Dr. Holly Stemp says the achievement unlocks the potential to build the future microchips needed for quantum computing using existing technology and manufacturing processes.

The AI model that teaches itself to think through problems, no humans required

Artificial intelligence is getting smarter every day, but it still has its limits. One of the biggest challenges has been teaching advanced AI models to reason, which means solving problems step by step. But in a new paper published in the journal Nature, the team from DeepSeek AI, a Chinese artificial intelligence company, reports that they were able to teach their R1 model to reason on its own without human input.

When many of us try to solve a problem, we typically don’t get the answer straight away. We follow a methodical process that may involve gathering information and taking notes until we get to a solution. Traditionally, training AI models to reason has involved copying our approach. However, it is a long, drawn-out process where people show an AI model countless examples of how to work through a problem. It also means that AI is only as good as the examples it is given and can pick up on human biases.

Instead of showing the R1 model every step, researchers at DeepSeek AI used a technique called reinforcement learning. This trial-and-error approach, using rewards for , encouraged the model to reason for itself.

Magnetic tunnel junctions mimic synapse behavior for energy-efficient neuromorphic computing

The rapid development of artificial intelligence (AI) poses challenges to today’s computer technology. Conventional silicon processors are reaching their limits: they consume large amounts of energy, the storage and processing units are not interconnected and data transmission slows down complex applications.

As the size of AI models is constantly increasing and they are having to process huge amounts of data, the need for new computing architectures is rising. In addition to quantum computers, focus is shifting, in particular, to neuromorphic concepts. These systems are based on the way the works.

This is where the research of a team led by Dr. Tahereh Sadat Parvini and Prof. Dr. Markus Münzenberg from the University of Greifswald and colleagues from Portugal, Denmark and Germany began. They have found an innovative way to make computers of tomorrow significantly more energy-efficient. Their research centers around so-called magnetic tunnel junctions (MTJs), tiny components on the nanometer scale.

Researchers develop colorized X-ray imaging for clearer material and tissue analysis

When German physicist Wilhelm Röntgen discovered X-rays in the late 1800s while experimenting with cathode ray tubes, it was a breakthrough that transformed science and medicine. So much so that the basic concept remains in use today. But a team of researchers at Sandia National Laboratories believes they’ve found a better way, harnessing different metals and the colors of light they emit.

“It’s called colorized hyperspectral X-ray imaging with multi-metal targets, or CHXI MMT for short,” said project lead Edward Jimenez, an optical engineer. Jimenez has been working with materials scientist Noelle Collins and electronics engineer Courtney Sovinec to create X-rays of the future.

“With this new technology, we are essentially going from the old way, which is black and white, to a whole new colored world where we can better identify materials and defects of interest,” Collins said.

‘Quantum squeezing’ a nanoscale particle for the first time

Researchers Mitsuyoshi Kamba, Naoki Hara, and Kiyotaka Aikawa of the University of Tokyo have successfully demonstrated quantum squeezing of the motion of a nanoscale particle, a motion whose uncertainty is smaller than that of quantum mechanical fluctuations.

As enhancing the measurement precision of sensors is vital in many modern technologies, the achievement paves the way not only for basic research in fundamental physics but also for applications such as accurate autonomous driving and navigation without a GPS signal. The findings are published in the journal Science.

The physical world at the macroscale, from to planets, is governed by the laws of discovered by Newton in the 17th century. The physical world at the microscale, atoms and below, is governed by the laws of quantum mechanics, which lead to phenomena generally not observed at the macroscale.

Physicist proves unsolvability beyond one dimension for quantum Ising models

By extending a proof of a physically important behavior in one-dimensional quantum spin systems to higher dimensions, a RIKEN physicist has shown in a new study that the model lacks exact solutions. The research is published in the journal Physical Review B.

Theoretical physicists develop mathematical models to describe material systems, which they can then use to make predictions about how materials will behave.

One of the most important models is the Ising model, which was first developed about a century ago to model such as iron and nickel.

Shape-shifting collisions offer new tool for studying early matter produced in Big Bang’s aftermath

This summer, the Large Hadron Collider (LHC) took a breath of fresh air. Normally filled with beams of protons, the 27-km ring was reconfigured to enable its first oxygen–oxygen and neon–neon collisions. First results from the new data, recorded over a period of six days by the ALICE, ATLAS, CMS and LHCb experiments, were presented during the Initial Stages conference held in Taipei, Taiwan, on 7–12 September.

Smashing into one another allows physicists to study the quark–gluon plasma (QGP), an extreme state of matter that mimics the conditions of the universe during its first microseconds, before atoms formed. Until now, exploration of this hot and dense state of free particles at the LHC relied on collisions between (like lead or xenon), which maximize the size of the plasma droplet created.

Collisions between lighter ions, such as oxygen, open a new window on the QGP to better understand its characteristics and evolution. Not only are they smaller than lead or xenon, allowing a better investigation of the minimum size of nuclei needed to create the QGP, but they are less regular in shape. A neon nucleus, for example, is predicted to be elongated like a bowling pin—a picture that has now been brought into sharper focus thanks to the new LHC results.

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