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The origin of magic numbers: Why some atomic nuclei are unusually stable

For the first time, physicists have developed a model that explains the origins of unusually stable magic nuclei based directly on the interactions between their protons and neutrons. Published in Physical Review Letters, the research could help scientists better understand the exotic properties of heavy atomic nuclei and the fundamental forces that hold them together.

While every chemical element is defined by a fixed number of protons in its atomic nucleus, the number of neutrons it contains is far less constrained. For almost every known element, there are at least two different nuclear configurations, or isotopes, which vary only in their number of neutrons.

However, if the number of protons and neutrons becomes too unbalanced in either direction, the nucleus becomes unstable. Since heavier elements tend to have fewer stable isotopes, these radioactive nuclei grow increasingly rare as this imbalance increases. Yet for certain specific numbers of protons and neutrons (collectively known as “nucleons”), some isotopes are found to be exceptionally stable, for reasons that physicists have struggled to fully explain.

Ammonia leaks can be spotted in under two seconds using new alveoli-inspired droplet sensor

Researchers from Guangxi University, China have developed a new gas sensor that detects ammonia with a record speed of 1.4 seconds. The sensor’s design mimics the structure of alveoli—the tiny air sacs in human lungs—while relying on a triboelectric nanogenerator (TENG) that converts mechanical energy into electrical energy. The sensor uses a process that is driven by A-droplets, which are tiny water droplets containing a trapped air bubble. These droplets exploit ammonia’s affinity for water to rapidly capture NH₃ when it is present.

When an ammonia-laden droplet falls onto the sensor, its mechanical impact completes an electrical circuit, generating signals that are converted into accurate gas measurements at a speed that exceeds existing ammonia gas sensors.

To take detection precision a step further, the team integrated an AI model that analyzes electrical signals and converts them into time-frequency images. After training on these images, the system classified ammonia into five concentration levels (0–200 ppm), achieving up to 98.4% detection accuracy.

A familiar magnet gets stranger: Why cobalt’s topological states could matter for spintronics

The element cobalt is considered a typical ferromagnet with no further secrets. However, an international team led by HZB researcher Dr. Jaime Sánchez-Barriga has now uncovered complex topological features in its electronic structure. Spin-resolved measurements of the band structure (spin-ARPES) at BESSY II revealed entangled energy bands that cross each other along extended paths in specific crystallographic directions, even at room temperature. As a result, cobalt can be considered as a highly tunable and unexpectedly rich topological platform, opening new perspectives for exploiting magnetic topological states in future information technologies.

The findings are published in the journal Communications Materials.

Cobalt is an elementary ferromagnet, and its properties and crystal structure have long been known. However, an international team has now discovered that cobalt hosts an unexpectedly rich topological electronic structure that remains robust at room temperature, revealing a surprising new level of quantum complexity in this material.

Electronic friction can be tuned and switched off

Researchers in China have isolated the effects of electronic friction, showing for the first time how the subtle drag force it imparts at sliding interfaces can be controlled. They demonstrate that it can be tuned by applying a voltage, or switched off entirely simply by applying mechanical pressure. The results, published in Physical Review X, could inform new designs that allow engineers to fine-tune the drag forces materials experience as they slide over each other.

In engineering, friction causes materials to wear and degrade over time, and also causes useful energy to be wasted as heat. While this problem can be mitigated through lubricants and smoother surfaces, friction can also arise from deeper, more subtle effects.

Among these is an effect which can occur at metallic or chemically active surfaces as they slide past one another. In these cases, atomic nuclei in one surface can transfer some of their energy to electrons in the other surface, exciting them to higher energy levels. This lost energy produces a drag force that increases with sliding velocity: an effect known as “electronic friction.”

Cell division spindles self-organize like active liquid crystals—a theory that holds up

When a cell divides, it performs a feat of microscopic choreography—duplicating its DNA and depositing it into two new cells. The spindle is the machinery behind that process: It latches onto chromosomes (where DNA is stored) and separates them so they can settle into their new homes. This tricky process can sometimes go wrong, causing infertility, genetic disorders, or cancer.

Scientists have a good understanding of what spindles are made of: long, thin rods called microtubules as well as a variety of associated motor proteins. However, how these microtubules interact and organize to guide the spindles’ function has remained a mystery.

One approach to understand how the spindle self-organizes is to treat it like an active liquid crystal. Liquid crystals, like spindles, are made up of elongated subunits. Unlike liquid crystals in LCD displays, which require an external electric field to reorient their subunits, spindles are active materials that generate forces internally.

NOvA maps neutrino oscillations over 500 miles with 10 years of data

Neutrinos are very small, neutral subatomic particles that rarely interact with ordinary matter and are thus sometimes referred to as ghost particles. There are three known types (i.e., flavors) of neutrinos, dubbed muon, electron and tau neutrinos.

Interestingly, physicists discovered that as they travel, neutrinos can change flavor, which requires that neutrinos have a small, but not zero, mass. This phenomenon, known as neutrino oscillation, has been widely investigated in recent years, as studying it could help to infer the properties of neutrinos.

The NOvA experiment, a U.S.-based particle physics research endeavor, has been collecting data with two neutrino detectors that are far apart from each other, one located at the Fermi National Laboratory (Fermilab) in Illinois and the other at a facility in Northern Minnesota. In a recent paper, published in Physical Review Letters, the researchers involved in the NOvA experiment published some of the most precise neutrino oscillation measurements to date.

Scientists reveal formation mechanism behind spherical assemblies of nanocrystals

From table salt to snowflakes, and from gemstones to diamonds—we encounter crystals everywhere in daily life, usually cubic (table salt) or hexagonal (snowflakes). Researchers from Noushine Shahidzadeh’s group at the UvA Institute of Physics now demonstrate how mesmerizing spherical crystal shapes arise through structures called spherulites.

A new study done in Shahidzadeh’s lab at the Institute of Physics / Van der Waals Zeeman-Institute, reveals how neatly ordered (hemi-) spherical or pancake-like structures in nature can emerge from completely disordered salt solutions. Moreover, scientists can now harness these structures to create advanced materials. The work is published in the journal Communications Chemistry.

AI method accelerates liquid simulations by learning fundamental physical relationships

Researchers at the University of Bayreuth have developed a method using artificial intelligence that can significantly speed up the calculation of liquid properties. The AI approach predicts the chemical potential—an indispensable quantity for describing liquids in thermodynamic equilibrium. The researchers present their findings in a new study published in Physical Review Letters.

Many common AI methods are based on the principle of supervised machine learning: a model—for instance, a neural network—is specifically trained to predict a particular target quantity directly. One example that illustrates this approach is image recognition, where the AI system is shown numerous images in which it is known whether or not a cat is depicted. On this basis, the system learns to identify cats in new, previously unseen images.

“However, such a direct approach is difficult in the case of the chemical potential, because determining it usually requires computationally expensive algorithms,” says Prof. Dr. Matthias Schmidt, Chair of Theoretical Physics II at the University of Bayreuth. He and his research associate Dr. Florian Sammüller address this challenge with their newly developed AI method. It is based on a neural network that incorporates the theoretical structure of liquids—and more generally, of soft matter—allowing it to predict their properties with great accuracy.

Space mining without heavy machines? Microbes harvest metals from meteorites aboard space station

If humankind is to explore deep space, one small passenger should not be left behind: microbes. In fact, it would be impossible to leave them behind, since they live on and in our bodies, surfaces and food. Learning how they react to space conditions is critical, but they could also be invaluable fellows in our endeavor to explore space.

Microorganisms such as bacteria and fungi can harvest crucial minerals from rocks and could provide a sustainable alternative to transporting much-needed resources from Earth.

Researchers from Cornell and the University of Edinburgh collaborated to study how those microbes extract platinum group elements from a meteorite in microgravity, with an experiment conducted aboard the International Space Station. They found that “biomining” fungi are particularly adept at extracting the valuable metal palladium, while removing the fungus resulted in a negative effect on nonbiological leaching in microgravity.

Subaru observations suggest an intrinsic gap in NGC 5466’s tidal stream

Astronomers from the National Astronomical Observatory of Japan (NAOJ) and elsewhere have used the Subaru Telescope to perform deep imaging observations of a distant globular cluster known as NGC 5466. The observational campaign yields important information about the structure of the cluster’s tidal stream. The new findings were published February 4 on the arXiv preprint server.

In general, stellar tidal streams are the result of tidal interactions between a central galaxy and lower mass systems such as satellite galaxies or globular clusters (GCs). Therefore, they could keep the memory of their progenitors’ chemical and dynamical information, even after a few billion years.

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