A theoretical model of a surface-wave instability matches observations of a muddy debris flow in an Alpine valley.
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Topological Twist for Phase Transitions
Contrary to conventional wisdom, so-called order parameters that distinguish symmetry-governed phases of matter can have topological structure.
From materials developing magnetization patterns to metals becoming superconductors, a wide range of phase transitions can be qualitatively described by a single framework known as Ginzburg-Landau theory [1, 2]. This framework generally assumes that a key quantity in its descriptions, called an order parameter, has trivial topology. But now, Canon Sun and Joseph Maciejko at the University of Alberta, Canada, have shown that order parameters can have hidden topological structure [3]. The researchers have developed an extension to Ginzburg-Landau theory that incorporates such hidden topology, revealing features absent from the original framework.
Symmetry constitutes a fundamental concept in physics. It appears in many guises but is especially important when studying how interactions of countless microscopic constituents give rise to macroscopic order in condensed-matter systems. For example, below a critical temperature, an ordinary magnet has a net magnetization because its spins all align in the same direction, breaking rotational symmetry. If the magnet is heated above that temperature, it loses its magnetization as its spins point in random directions, restoring rotational symmetry.
New Approach to Controlling Light Signals
A concept based on an exotic effect in periodic structures may be useful for developing future photonic devices.
A new way to marshal light within optical devices has been demonstrated experimentally by researchers in China. They have been able to induce light to organize itself into specific patterns of pulses as it circulates within a pair of optical fiber loops using a version of a phenomenon—called the non-Hermitian skin effect (NHSE)—that has been predicted but not observed previously [1]. The effect could be used to control light signals in photonic devices such as switches and routers.
In the standard theory for electron behavior in a metallic crystal, the periodic atomic structure leads to so-called Bloch waves—electron quantum states that spread across the entire crystal. But in recent years, theorists have found surprising results for a scenario in which one assumes that a particle such as an electron hops between neighboring sites in a periodic lattice asymmetrically—say, rightward hopping is more probable than leftward hopping. The particle’s quantum states become localized at the edge or surface of the lattice rather than spreading across it. This localization is the NHSE.
Affordable laser could be mass-produced for use in self-driving cars and fiber optics
Laser technology is used in many areas, where precise measurements are required and in communication. This means that they are important for everything from self-driving cars to the fiber optic internet and for detecting gases in the air.
Now, a research group has come up with a new type of laser that solves several problems associated with current-day lasers. The group is led by Associate Professor Johann Riemensberger at NTNU’s Department of Electronic Systems.
“Our results can give us a new type of laser that is both fast, relatively cheap, powerful and easy to use,” says Riemensberger.
More pathways that previously thought can lead to optical topological insulators
The candidate pool for engineered materials that can help enable tomorrow’s cutting-edge optical technologies—such as lasers, detectors and imaging devices—is much deeper than previously believed.
That’s according to new research from the University of Michigan that examined a class of materials known as topological insulators. These materials have exciting and tunable properties when it comes to how they transmit energy and information.
“We see this as a step toward building a more versatile and powerful foundation for future photonic technologies,” said Xin Xie, a research fellow in the U-M Department of Physics and lead author of the recent study in the journal Physical Review X.
Compute-in-memory chip shows promise for enhanced efficiency and privacy in federated learning systems
In recent decades, computer scientists have been developing increasingly advanced machine learning techniques that can learn to predict specific patterns or effectively complete tasks by analyzing large amounts of data. Yet some studies have highlighted the vulnerabilities of some AI-based tools, demonstrating that the sensitive information they are fed could be potentially accessed by malicious third parties.
A machine learning approach that could provide greater data privacy is federated learning, which entails the collaborative training of a shared neural network by various users or parties that are not required to exchange any raw data with each other. This technique could be particularly advantageous when applied in sectors that can benefit from AI but that are known to store highly sensitive user data, such as health care and finance.
Researchers at Tsinghua University, the China Mobile Research Institute, and Hebei University recently developed a new compute-in-memory chip for federated learning, which is based on memristors, non-volatile electronic components that can both perform computations and store information, by adapting their resistance based on the electrical current that flowed through them in the past. Their proposed chip, outlined in a paper published in Nature Electronics, was found to boost both the efficiency and security of federated learning approaches.
Surprising versatility of boron nitride nanotubes displayed in fusion of art and science
In an elegant fusion of art and science, researchers at Rice University have achieved a major milestone in nanomaterials engineering by uncovering how boron nitride nanotubes (BNNTs)—touted for their strength, thermal stability and insulating properties—can be coaxed into forming ordered liquid crystalline phases in water. Their work, published in Langmuir, was so visually striking it graced the journal’s cover.
That vibrant image, however, represents more than just the beauty of science at the nanoscale. It captures the essence of a new, scalable method to align BNNTs in aqueous solutions using a common bile-salt surfactant—sodium deoxycholate (SDC)—opening the door to next-generation materials for aerospace, electronics and beyond.
“This work is very interesting from the fundamental point of view because it shows that BNNTs can be used as model systems to study novel nanorod liquid crystals,” said Matteo Pasquali, the A.J. Hartsook Professor of Chemical and Biomolecular Engineering, professor of chemistry, materials science and nanoengineering and corresponding author on the study.
Quantum spin currents in graphene without external magnetic fields pave way for ultra-thin spintronics
Scientists from TU Delft (The Netherlands) have observed quantum spin currents in graphene for the first time without using magnetic fields. These currents are vital for spintronics, a faster and more energy-efficient alternative to electronics. This breakthrough, published in Nature Communications, marks an important step towards technologies like quantum computing and advanced memory devices.
Quantum physicist Talieh Ghiasi has demonstrated the quantum spin Hall (QSH) effect in graphene for the first time without any external magnetic fields. The QSH effect causes electrons to move along the edges of the graphene without any disruption, with all their spins pointing in the same direction.
“Spin is a quantum mechanical property of electrons, which is like a tiny magnet carried by the electrons, pointing up or down,” Ghiasi explains. “We can leverage the spin of electrons to transfer and process information in so-called spintronics devices. Such circuits hold promise for next-generation technologies, including faster and more energy-efficient electronics, quantum computing, and advanced memory devices.”
A new atomistic route to viscosity—even near the glass transition
We rarely think about how liquids flow—why honey is thick, water is thin or how molten plastic moves through machines. But for scientists and engineers, understanding and predicting the viscosity of materials, especially polymers, is essential.
Viscosity governs how substances deform and flow under stress, which in turn affects how they are processed, how they behave in industrial pipelines, in environmental settings, or in consumer products, and how they respond to changing temperatures.
Traditionally, to calculate the viscosity of a liquid or polymer melt based on molecular simulations on computers, people rely on a method called the Green–Kubo formalism. It works by tracking how internal stresses fluctuate and decay over time inside a simulated material at thermodynamic equilibrium.