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The gravitino: A new candidate for dark matter

Dark matter remains one of the biggest mysteries in fundamental physics. Many theoretical proposals (axions, WIMPs) and 40 years of extensive experimental searches have failed to provide any explanation of the nature of dark matter.

Several years ago, in a theory unifying and gravity, new, radically different candidates were proposed: superheavy charged gravitinos.

Now, a paper published in Physical Review Research by scientists from the University of Warsaw and Max Planck Institute for Gravitational Physics shows how new underground detectors, in particular the JUNO detector starting soon to take data, even though designed for neutrino physics, are also extremely well suited to eventually detect charged dark matter gravitinos.

Supercomputer unveils new cell sorting principle in microfluidic channels

Researchers have discovered a novel criterion for sorting particles in microfluidic channels, paving the way for advancements in disease diagnostics and liquid biopsies. Using the supercomputer “Fugaku,” a joint team from the University of Osaka, Kansai University and Okayama University revealed that soft particles, like biological cells, exhibit unique focusing patterns compared to rigid particles.

The outcomes, published in the Journal of Fluid Mechanics, pave the way for next-generation microfluidic devices leveraging cell and particle deformability, promising highly efficient cell sorting with such as early cancer detection.

Microfluidics involves manipulating fluids at a microscopic scale. Controlling particle movement within microchannels is crucial for cell sorting and diagnostics, expected to realize early cancer detection and treatment. While prior research focused on rigid particles, which typically focus near channel walls, the behavior of deformable particles remained largely unexplored.

New design tackles integer factorization problems through digital probabilistic computing

Probabilistic Ising machines (PIMs) are advanced and specialized computing systems that could tackle computationally hard problems, such as optimization or integer factorization tasks, more efficiently than classical systems. To solve problems, PIMs rely on interacting probabilistic bits (p-bits), networks of interacting units of digital information with values that randomly fluctuate between 0 and 1, but that can be biased to converge to yield desired solutions.

Life’s building blocks may not be stable—just really, really long-lived

Although the building blocks of life such as hydrogen and oxygen appear stable to us, many theories of physics predict that they are actually just tremendously long-lived, with the particles found in their nuclei slowly, but ultimately decaying.

To investigate this idea, researchers have been hunting for evidence of this by looking for faint signals of decaying in Japan’s Super-Kamiokande observatory.

So far, no definitive signals of decay have emerged, implying that if the proton does decay, it probably has a lifetime exceeding 1033 years—that’s 10 with 32 zeros behind it.

Activity of large-scale cortical networks follows cyclical pattern, study finds

The human brain can concurrently support a wide range of advanced mental functions, including attention, memory and the processing of sensory stimuli. While past neuroscience studies have gathered valuable insight into the neural underpinnings of each of these processes, the mechanisms that ensure that they are performed efficiently and in a timely fashion have not yet been fully elucidated.

Researchers at the University of Oxford and other institutes recently set out to explore how the activity of large-scale cortical functional networks, interconnected in the brain’s outermost layer, changes over time. Their findings, published in Nature Neuroscience, suggest that the overall order in which these networks become active follows an inherently cyclical pattern.

“This research was inspired by observations that transitions between large-scale brain networks are asymmetric: we have seen that in many cases it is much more likely that network X follows network Y than the other way around,” Dr. Mats W.J. van Es, postdoctoral researcher at the University of Oxford and first author of the paper, told Medical Xpress.

Time crystals arise from quantum interactions once thought to prevent their formation

Nature has many rhythms: the seasons result from Earth’s movement around the sun, the ticking of a pendulum clock results from the oscillation of its pendulum. These phenomena can be understood with very simple equations. However, regular rhythms can also arise in a completely different way—by themselves, without an external clock, through the complex interaction of many particles. Instead of uniform disorder, a fixed rhythm emerges—this is referred to as a “time crystal.”

Calculations by TU Wien (Vienna) now show that such time crystals can also be generated in a completely different way than previously thought. The quantum physical correlations between the particles, which were previously thought to be harmful for the emergence of such phenomena, can actually stabilize time crystals. This is a surprising new insight into the quantum physics of many-particle systems.

The findings are published in the journal Physical Review Letters.

Catalyst evolution reveals the unsung heroes in industrial ammonia production

Researchers at the Fritz Haber Institute of the Max Planck Society, in collaboration with the Max Planck Institute of Chemical Energy Conversion and Clariant have unveiled new insights into the complex catalyst systems used in industrial ammonia production. By examining the structural evolution of these catalysts, the study highlights the critical role of promoters in enhancing performance and stability.

The Haber-Bosch process, a cornerstone of industrial ammonia production, has remained largely unchanged for over a century. However, researchers at the Departments of Inorganic Chemistry and Interface Science of the Fritz Haber Institute, the Max Planck Institute for Chemical Energy Conversion, and Clariant have made significant strides in the mechanistic understanding of the highly complex industrial catalyst that drives this process.

By using advanced characterization techniques like operando scanning and near-ambient pressure X-ray photoelectron spectroscopy, the team has decoded the complex interactions within multi-promoted ammonia synthesis catalysts.

New tool steers AI models to create materials with exotic quantum properties

The artificial intelligence models that turn text into images are also useful for generating new materials. Over the last few years, generative materials models from companies like Google, Microsoft, and Meta have drawn on their training data to help researchers design tens of millions of new materials.

But when it comes to designing materials with exotic quantum properties like superconductivity or unique magnetic states, those models struggle. That’s too bad, because humans could use the help. For example, after a decade of research into a class of materials that could revolutionize , called quantum spin liquids, only a dozen material candidates have been identified. The bottleneck means there are fewer materials to serve as the basis for technological breakthroughs.

Now, MIT researchers have developed a technique that lets popular generative materials models create promising quantum materials by following specific design rules. The rules, or constraints, steer models to create materials with unique structures that give rise to quantum properties.

Quantum memories reach new milestone with secure quantum money protocol

Integration into a quantum money protocol shows that memories can now handle very demanding applications for quantum networking.

Researchers at the Kastler Brossel Laboratory (Sorbonne Université, CNRS, ENS-Université PSL, Collège de France), together with colleagues from LIP6 (Sorbonne Université, CNRS), have taken a major step forward in : for the first time, they have integrated an optical quantum memory into a cryptographic protocol. This achievement, based on Wiesner’s unforgeable quantum money scheme, demonstrates that quantum memories are now mature enough to operate under very demanding conditions for networking.

In a study published on September 19 in Science Advances, the Paris team implemented Wiesner’s quantum money, a foundational idea in that relies on the no-cloning theorem to prevent counterfeiting. Unlike previous demonstrations that bypassed storage, this experiment incorporated an intermediate memory step—an essential capability for real-world applications where quantum data must be held and released on demand.

Brain Cells Behind Depression Identified for the First Time

Research on rare post-mortem brain tissue shows changes in gene activity, offering new insight into the biological basis of depression. Researchers from McGill University and the Douglas Institute have discovered two distinct types of brain cells that show alterations in individuals with depressi

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