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Forensic test recovers fingerprints from fired ammunition casings despite intense heat

A pioneering new test that can recover fingerprints from ammunition casing, once thought nearly impossible, has been developed by two Irish scientists.

Dr. Eithne Dempsey, and her recent Ph.D. student Dr. Colm McKeever, of the Department of Chemistry in Ireland’s Maynooth University have developed a unique electrochemical method which can visualize fingerprints on brass casings, even after they have been exposed to the high temperature conditions experienced during gunfire. The study is published in the journal Forensic Chemistry.

For decades, investigators have struggled to recover fingerprints from weapons because any biological trace is usually destroyed by the , friction and gas released after a gun is fired. As a result, criminals often abandon their weapons or casings at , confident that they leave no fingerprint evidence behind.

Security researchers say G1 humanoid robots are secretly sending information to China and can easily be hacked

Researchers have uncovered serious security flaws with the Unitree G1 humanoid robot, a machine that is already being used in laboratories and some police departments. They discovered that G1 can be used for covert surveillance and could potentially launch a full-scale cyberattack on networks.

It sounds like the stuff of science fiction nightmares, robots that are secretly spying on you and could be controlled by remote hackers. However, the concern is real, as these types of robots are becoming increasingly common in homes, businesses, and .

Simulations show Saturn’s moon Enceladus shoots less ice into space than previous estimates

In the 17th century, astronomers Christiaan Huygens and Giovanni Cassini trained their telescopes on Saturn and uncovered a startling truth: the planet’s luminous bands were not solid appendages, but vast, separate rings composed of countless nested arcs.

Centuries later, NASA’s Cassini–Huygens (Cassini) probe carried the exploration of Saturn even further. Beginning in 2005, it sent back a stream of spectacular images that transformed scientists’ understanding of the system. Among its most dramatic revelations were the towering geysers on Saturn’s icy moon Enceladus, which blasted debris into space and left behind a faint sub-ring encircling the planet.

New supercomputer simulations from the Texas Advanced Computing Center (TACC) based on the Cassini space probe’s data have found improved estimates of ice mass Enceladus is losing to space. These findings help with understanding and future robotic exploration of what’s below the surface of the icy moon, which might harbor life.

Long-term radio observations track the evolution of a tidal disruption event

Astronomers from Curtin University in Australia and elsewhere have performed radio observations of a tidal disruption event known as AT2019azh. Results of the new study, published September 22 on the arXiv preprint server, provide crucial information regarding the evolution of this event.

Tidal events (TDEs) are phenomena that occur when a star passes close enough to a (SMBH) and is destroyed by the black hole’s tidal forces. As a result, around half of the stellar debris is unbound from the system, while the rest of the material remains bound, producing a luminous flare as it accretes onto the SMBH.

AT2019azh is a TDE at a redshift of 0.022, detected in 2019 in the galaxy KUG 0180+227. It showcases persistent blue colors, has a high blackbody temperature, and previous observations have reported a lack of spectroscopic features associated with a supernova or an (AGN), which confirmed its TDE nature.

Tiny nanoparticles conquer the big three in polymer glasses: Strength, toughness and processability

Scientists have found a nanoparticle-inspired solution to the age-old strength issue of polymer glasses. Seasoning the polymer glass recipe with single-chain nanoparticles, which are tiny, folded-up polymer strands, can make the glass stronger, tougher, and easier to process by acting as reinforcements.

In a study published in Physical Review Letters, researchers from China overcame these issues by using nanoparticles made from balled-up single-chain polymers (SCNPs). According to the researchers, their approach opens a new pathway for creating advanced polymer glasses that combine strength, , and processability in ways previously thought to be incompatible.

Polymer glass, also known as plexiglass, is widely used for making eyeglasses and enclosures for aquariums and museums. For decades, researchers have been seeking ways to enhance the mechanical properties of plexiglass, with a primary focus on improving its strength and toughness.

Collective Bloch oscillations observed in 1D Bose gas system

Bloch oscillations are periodic oscillations of quantum particles in a repeating energy “landscape” (e.g., a crystal lattice) that are subjected to a constant force. These particle motions have been the focus of numerous physics studies, as they are intriguing quantum effects that are not predicted by classical mechanics theories.

Probing Bloch oscillations experimentally could thus yield new insight into the fundamental properties of quantum matter. So far, they have been primarily studied in individual particles or two-particle systems, as opposed to quantum many-body systems comprised of several particles.

Researchers at CNRS-ENS-PSL University and Sorbonne University report the observation of collective Bloch oscillations in a one-dimensional (1D) Bose gas, a quantum fluid comprised of bosons, which are particles that can occupy the same quantum state.

AI tensor network-based computational framework cracks a 100-year-old physics challenge

Researchers from The University of New Mexico and Los Alamos National Laboratory have developed a novel computational framework that addresses a longstanding challenge in statistical physics.

The Tensors for High-dimensional Object Representation (THOR) AI framework employs tensor network algorithms to efficiently compress and evaluate the extremely large configurational integrals and central to determining the thermodynamic and mechanical properties of materials.

The framework was integrated with machine learning potentials, which encode interatomic interactions and dynamical behavior, enabling accurate and scalable modeling of materials across diverse physical conditions.

Core electron bonding may not always require extreme pressure, study finds

You probably learned in high school chemistry class that core electrons don’t participate in chemical bonding.

They’re thought to be too deep inside an atom and close to the nucleus to meaningfully interact with the of other atoms, leaving the outer valence electrons to get all the glory in textbooks.

The actual science is more complicated, as some elements’ core electrons are theorized to activate when squeezed hard enough, like at the pressure levels found deep inside Earth.

Physicists solve mystery of loop current switching in kagome metals

Quantum metals are metals where quantum effects—behaviors that normally only matter at atomic scales—become powerful enough to control the metal’s macroscopic electrical properties.

Researchers in Japan have explained how electricity behaves in a special group of quantum metals called kagome metals. The study is the first to show how reverse tiny loop electrical currents inside these metals. This switching changes the material’s macroscopic electrical properties and reverses which direction has easier electrical flow, a property known as the diode effect, where current flows more easily in one direction than the other.

Notably, the research team found that quantum geometric effects amplify this switching by about 100 times. The study, published in Proceedings of the National Academy of Sciences, provides the theoretical foundation that could eventually lead to new electronic devices controlled by simple magnets.

Human intuition fuels AI-driven quantum materials discovery

Many properties of the world’s most advanced materials are beyond the reach of quantitative modeling. Understanding them also requires a human expert’s reasoning and intuition, which can’t be replicated by even the most powerful artificial intelligence, mixed with fortuitous accident, according to Eun-Ah Kim, the Hans A. Bethe Professor of physics in the College of Arts and Sciences.

Kim and collaborators have developed a that encapsulates and quantifies the valuable intuition of human experts in the quest to discover new quantum materials. The model, Materials Expert-Artificial Intelligence (ME-AI), “bottles” this intuition into descriptors that predict the functional properties of a material. The team used the method to solve a quantum materials problem.

“We are charting a new paradigm where we transfer experts’ knowledge, especially their intuition and insight, by letting an expert curate data and decide on the fundamental features of the model,” said Kim, director of the Cornell-led National Science Foundation AI-Materials Institute. “Then the machine learns from the data to think the way the experts think.”

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