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Astronomers trace a runaway star to a former companion’s supernova

Astronomers have strengthened long-standing predictions that massive runaway stars could have originated in binary pairs, and were dramatically ejected into space when their companion stars underwent supernova explosions. Through a combination of observations and stellar models, a team led by Baha Dinçel at the University of Jena in Germany revealed that the star HD 254577 likely did just this—and that its origins can be tied back to a companion whose remnants now form the Jellyfish nebula. The research is published in Astronomy & Astrophysics.

Electron-phonon ‘surfing’ could help stabilize quantum hardware, nanowire tests suggest

That low-frequency fuzz that can bedevil cellphone calls has to do with how electrons move through and interact in materials at the smallest scale. The electronic flicker noise is often caused by interruptions in the flow of electrons by various scattering processes in the metals that conduct them.

The same sort of noise hampers the detecting powers of advanced sensors. It also creates hurdles for the development of quantum computers—devices expected to yield unbreakable cybersecurity, process large-scale calculations and simulate nature in ways that are currently impossible.

A much quieter, brighter future may be on the way for these technologies, thanks to a new study led by UCLA. The research team demonstrated prototype devices that, above a certain voltage, conducted electricity with lower noise than the normal flow of electrons.

Graphene sealing enables first atomic images of monolayer transition metal diiodides

Two-dimensional (2D) materials promise revolutionary advances in electronics and photonics, but many of the most interesting candidates degrade within seconds of air exposure, making them nearly impossible to study or integrate into real-world technology. Transition metal dihalides represent a particularly compelling yet challenging class of materials, with predicted properties ideal for next-generation devices, but their extreme reactivity when exposed to air prevents even basic structural characterization.

Researchers at The University of Manchester’s National Graphene Institute have now achieved the first atomic-resolution imaging of monolayer transition metal diiodides, made possible by creating graphene-sealed TEM samples that prevent these highly reactive materials from degrading on contact with air.

The study, published in ACS Nano, demonstrates that fully encapsulating the crystals in graphene preserves atomically clean interfaces and extends their usable lifetime from seconds to months.

Honest or deceptive? What a new signaling model means for animal displays and human claims

For decades, scientists have tried to answer a simple question: why be honest when deception is possible? Whether it is a peacock’s tail, a stag’s roar, or a human’s résumé, signals are means to influence others by transmitting information and advantages can be gained by cheating, for example by exaggeration. But if lying pays, why does communication not collapse?

The dominant theory for honest signals has long been the handicap principle, which claims that signals are honest because they are costly to produce. It argues that a peacock’s tail, for example, is an honest signal of a male’s condition or quality to potential mates because it is so costly to produce. Only high-quality birds could afford such a handicap, wasting resources growing it, demonstrating their superb quality to females, whereas poor quality males cannot afford such ornaments.

A new synthesis by Szabolcs Számadó, Dustin J. Penn and István Zachar (from the Budapest University of Technology and Economics, University of Veterinary Medicine Vienna and HUN-REN Centre for Ecological Research, respectively) challenges that logic. They argue that honesty does not depend on how costly or wasteful a signal is, but rather on the trade-offs between investments and benefits, faced by signalers.

New design tool 3D-prints woven metamaterials that stretch and fail predictably

Metamaterials—materials whose properties are primarily dictated by their internal microstructure, and not their chemical makeup—have been redefining the engineering materials space for the last decade. To date, however, most metamaterials have been lightweight options designed for stiffness and strength.

New research from the MIT Department of Mechanical Engineering introduces a computational design framework to support the creation of a new class of soft, compliant, and deformable metamaterials. These metamaterials, termed 3D woven metamaterials, consist of building blocks that are composed of intertwined fibers that self-contact and entangle to endow the material with unique properties.

‘Discovery learning’ AI tool predicts battery cycle life with just a few days’ data

An agentic AI tool for battery researchers harnesses data from previous battery designs to predict the cycle life of new battery concepts. With information from just 50 cycles, the tool—developed at University of Michigan Engineering—can predict how many charge-discharge cycles the battery can undergo before its capacity drops below 90% of its design capacity.

This could save months to years of testing, depending on the conditions of cycling experiments, as well as substantial electrical power during battery prototyping and testing. The team estimates that the cycle lives of new battery designs could be predicted with just 5% of the energy and 2% of the time required by conventional testing.

“When we learn from the historical battery designs, we leverage physics-based features to construct a generalizable mapping between early-stage tests and cycle life,” said Ziyou Song, U-M assistant professor of electrical and computer engineering and corresponding author of the study in Nature. “We can minimize experimental efforts and achieve accurate prediction performance for new battery designs.”

Quick test can curb antimicrobial resistance, identifying bacteria and antibiotic susceptibility in under 40 minutes

McGill researchers have developed a diagnostic system capable of identifying bacteria—and determining which antibiotics can stop them—in just 36 minutes, a major advance in the global effort to curb antimicrobial resistance (AMR). Current clinical testing methods typically take 48 to 72 hours, leaving physicians without timely guidance.

The researchers say this innovation arrives at a critical moment due to the urgency of the AMR crisis, which arises from bacteria developing resistance to antibiotics.

“We are losing the race against antimicrobial resistance,” said Sara Mahshid, associate professor in the Department of Bioengineering and lead author on the Nature Nanotechnology study. “Every year, more than one million people die, more than from HIV/AIDS or malaria, and delayed treatment is a major driver. Rapid testing isn’t a luxury; it’s the missing link between diagnosis and survival.”

Platinum nanostructure sensor can differentiate mirror-image volatile scent compounds

Terpenes are volatile organic compounds that are responsible for, among other things, the typical scents of plants, resins or citrus fruits. These compounds occur naturally in the environment and influence chemical processes in the atmosphere. At high concentrations, they can irritate the respiratory tract and contribute to the formation of harmful derivatives. Many terpenes exist in two mirror-image forms, known as enantiomers, which can differ significantly in terms of their effects and how they are perceived—but which are difficult to distinguish between using technical means.

Now, researchers from the Department of Chemistry at the University of Basel have presented a new approach that allows these mirror-image forms of the molecules to be detected specifically.

“Our work focused on a specially developed platinum-based molecule that works as a sensor,” explains Dr. Annika Huber, first author of the study and a former doctoral student at the Swiss Nanoscience Institute’s Ph.D. School. “This sensor molecule has a fixed, three-dimensional shape and aggregates with a large number of identical molecules to form tiny stack-like nanostructures that react differently to the two mirror-image forms of the terpenes.”

From cryogenic to red-hot: Optical temperature sensing from 77 K to 873 K

An international collaboration involving researchers from the University of Innsbruck has developed a novel luminescent material that enables particularly robust and precise optical temperature sensing across an exceptionally broad temperature range.

Optical luminescence thermometry has been gaining increasing attention, as it allows contactless temperature measurement even under extreme conditions. A key concept in this field is so-called ratiometric Boltzmann thermometry, in which the intensity ratio of two thermally coupled emission transitions directly follows the temperature. The performance of such thermometers crucially depends on the electronic structure of the luminescent ion and its incorporation into the host structure.

In a recent study, the two first authors, Gülsüm Kinik from the research group of Prof. Markus Suta at Heinrich Heine University Düsseldorf and Ingo Widmann from the research group of Prof. Hubert Huppertz at the Department of General, Inorganic and Theoretical Chemistry at the University of Innsbruck, reported the compound Al0.993 Cr0.007 B4 O6 N, which stands out as an exceptionally high-performance luminescence thermometer. The material is based on Cr3+ ions embedded in an almost ideal octahedral coordination environment, resulting in a particularly well-defined energy level scheme.

AI Faces Fool Most of Us, But 5 Minutes of Training May Help You Spot Fakes

AI image generators have become remarkably proficient in a very short period, capable of creating faces that are considered to be more realistic than the real thing.

However, a new study points to a way that we can improve our AI-face detection capabilities.

Researchers from the UK tested the face-assessing capabilities of a group of 664 volunteers, consisting of super-recognizers (who have shown a high level of skill for comparing and recognizing real faces in previous studies), and people with typical face-recognition abilities.

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