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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.

Major depressive disorder shares immune abnormalities and potential therapies with inflammatory skin diseases

A team of leading clinical research scientists from the Departments of Psychiatry and Dermatology at the Icahn School of Medicine at Mount Sinai has found that the serum of patients with major depressive disorder shares immune abnormalities with inflammatory skin diseases, most notably the common Th2 immune pathway that is implicated in atopic dermatitis. Because these skin diseases are treatable, the findings suggest new therapeutic possibilities for psychiatric illness as well.

The study findings, published in Molecular Psychiatry, underscore the potential role of the Th2 axis in major depressive disorder and highlight the potential of targeting this specific immune pathway that involves interleukin-4 receptor alpha, a cell receptor known to play a key role in regulating inflammation, as a disease-modifying treatment for this psychiatric disorder.

Furthermore, the back-translational drug repurposing strategy employed in this study may offer a new approach to identifying immunomodulatory drugs in psychiatry.

Rocket science? 3D printing soft matter in zero gravity

What happens to soft matter when gravity disappears? To answer this, UvA physicists launched a fluid dynamics experiment on a sounding rocket. The suborbital rocket reached an altitude of 267 km before falling back to Earth, providing six minutes of weightlessness.

In these six minutes, the researchers 3D-printed large droplets of a soft material similar to the inks used for bioprinting —a developing technology that shows huge potential for regenerative and personalized medicine, tissue engineering and cosmetics. Bioprinting involves 3D-printing a mix of cells and bio-inks or bio-materials in a desired shape, often to construct living tissues.

The experiment was called COLORS (COmplex fluids in LOw gravity: directly observing Residual Stresses). Using a special optical set-up, the researchers could see where the printed material experienced internal stresses (forces) as the droplets spread and merged. Stressed regions stand out as bright colors in the experiment. Investigating how and where these stresses emerge is important because they can get frozen in a material as it solidifies, creating weak points where 3D-printed objects are most likely to break.

Majorana qubits become readable as quantum capacitance detects even-odd states

The race to build reliable quantum computers is fraught with obstacles, and one of the most difficult to overcome is related to the promising but elusive Majorana qubits. Now, an international team has read the information stored in these quantum bits. The findings are published in the journal Nature.

“This is a crucial advance,” explains Ramón Aguado, a Spanish National Research Council (CSIC) researcher at the Madrid Institute of Materials Science (ICMM) and one of the study’s authors.

“Our work is pioneering because we demonstrate that we can access the information stored in Majorana qubits using a new technique called quantum capacitance,” continues the scientist, who explains that this technique “acts as a global probe sensitive to the overall state of the system.”

Parabolic mirror-enhanced Raman spectroscopy enables high-sensitivity trace gas detection

A research team led by Prof. Fang Yonghua from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences proposed and systematically optimized a novel parabolic mirror cavity-enhanced Raman spectroscopy (PMCERS) technique, achieving a marked improvement in gas detection sensitivity through the integration of advanced optical design and signal processing methods. These results were published in Optics & Laser Technology.

Multi-component gas detection is important for environmental, industrial, and medical applications. Raman spectroscopy is well-suited for this purpose because it enables the simultaneous, water-vapor-free detection of multiple gas species. However, its inherently weak scattering limits sensitivity. Conventional cavity-enhanced approaches relying on lens-based collection have a limited numerical aperture, resulting in inefficient capture of three-dimensionally distributed Raman signals.

In this study, the team developed a parabolic mirror-based cavity-enhanced Raman spectroscopy system that leverages the large-aperture characteristics of parabolic mirrors to significantly improve Raman signal collection. Through the systematic optimization of the cavity structure, an efficient closed-loop optical path was established, effectively eliminating signal collection blind spots and suppressing stray-light interference.

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