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Amino acids and a bone mineral may help control magnesium implant breakdown, three student papers suggest

Seeing a bachelor’s thesis published in a scientific journal is relatively uncommon. For three theses linked to the same research group to result in scientific publications within just three months is downright rare. Yet that is exactly what has happened in Elsebeth Schröder’s research group at the Division of Quantum Device Physics.

To celebrate the milestone, Schröder invited all three student teams to a cake party at the Department of Microtechnology and Nanoscience.

“It feels fantastic, of course. I certainly wasn’t expecting this,” says Alva Limbäck, whose article “A density functional theory study of amino acids on pristine Mg(0001) and with sparse alloying elements” was published in Applied Surface Science in August, together with fellow students Olof Hildeberg, John Bolin and Amanda Goold.

Laser experiments recreate solar flare physics and reveal consistent magnetic reconnection rates

Magnetic reconnection is a process that occurs when the magnetic fields of a conductive plasma quickly rearrange and release massive amounts of stored magnetic energy. It is widely thought to be the underlying mechanism behind cosmic events such as solar flares and substorms in Earth’s magnetosphere.

Now, researchers from Kyushu University have used high-power lasers to recreate and investigate this puzzling physical phenomenon of magnetic reconnection in a controlled environment. Their results indicate that fast magnetic reconnection is governed by the local physics of the reconnection layer rather than the properties of the surrounding plasma.

Briefly, magnetic reconnection involves the splicing and reconnection of magnetic field lines pointing in opposite directions as two plasma flows meet, causing plasma heating and high-speed plasma outflows.

Coffee on the nanoscale: Graphene oxide membrane removes half the caffeine while retaining key compounds

The humble coffee filter has the relatively simple job of letting the coffee through and leaving the grounds behind. But researchers from the ARC Center of Excellence for Carbon Science and Innovation (COE-CSI) are exploring whether a filter operating on the molecular scale can do something much more difficult: separate the caffeine from the coffee itself.

It’s no ordinary challenge. A cup of coffee isn’t simply water and caffeine; it’s a mixture of various chemical compounds, many of which contribute to its flavor, aroma and other characteristics. Removing caffeine while leaving the things that make coffee taste like coffee means distinguishing between molecules at an extraordinarily small scale.

For UNSW Team Graphene master’s research student Yihan Tian, the challenge had plenty of appeal. Not only is she a huge fan of coffee, but the science itself also excites her.

Pilot system couples seawater desalination with hydrogen production

Seawater holds enormous potential for green hydrogen production, but realizing that potential remains far from straightforward. Direct seawater electrolysis can damage electrodes, whereas conventional freshwater electrolysis loses much of its energy as low-grade waste heat. Researchers have now found a way to harness this otherwise wasted heat, using it to desalinate seawater and produce fresh water alongside hydrogen.

In a study published in Nature Energy on Sept. 15, a team led by Prof. Deng Dehui and Associate Prof. Liu Yanting from the Dalian Institute of Chemical Physics (DICP) of the Chinese Academy of Sciences (CAS) proposed a “seawater to hydrogen and fresh water (STHW)” route that couples alkaline water electrolysis (AWE) with low-temperature vacuum distillation desalination.

The STHW process uses waste heat generated during AWE to drive low-temperature seawater desalination, producing fresh water for electrolysis and external use. The resulting concentrated brine can also be used for resource recovery, including salt, uranium and bromine.

Open-source benchmark tests whether AI agents can engineer working robots

With the rapid rise of artificial intelligence in daily life, software coding has become increasingly automated, with powerful AI systems known as coding agents able to write and revise computer programs almost autonomously. But what happens when an AI agent must contend not just with digital command lines, but with the physical world of robotics?

Researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) and the Georgia Tech School of Computational Science and Engineering are taking a systematic approach to finding out.

A team led by Na Li, the Winokur Family Professor of Electrical Engineering and Applied Mathematics at Harvard SEAS, and Bo Dai, assistant professor at Georgia Tech, have developed an evaluation tool known as a benchmark that tests how well AI coding agents can perform the challenging task of engineering an actual, physical robot. The team includes Harvard graduate student Haitong Ma and Chenxiao Gao and Rushi Qiang at Georgia Tech.

An extremely stable quantum gas offers a new lens on strongly interacting systems

Molecular gases are a new form of artificial quantum matter. However, when these molecules collide, they are often lost extremely rapidly. Researchers from Radboud University and Columbia University have been able to suppress this collisional loss, paving the way for strongly interacting quantum matter. This new artificial quantum matter allows researchers to study quantum behavior relevant to electrons in real materials. Their results are published in Science.

Quantum gases of molecules are created by cooling to nanokelvin temperatures, only 0.000000001° above absolute zero. Under these conditions, quantum effects dominate, and the molecules behave collectively, forming a Bose-Einstein condensate in which many molecules share the same quantum state. This new artificial quantum matter acts as a controllable model material that allows researchers to study quantum behavior relevant to electrons in real materials.

New Membrane Removes Nearly 100% of Ammonia From Wastewater in Minutes

A new electrically powered filter can strip nearly all ammonia nitrogen from wastewater in just over a minute while also breaking down carbon-based pollution.

Researchers have developed an electro-filtration process that uses chlorine oxide radicals (·ClO) generated inside a specialized membrane to remove nitrogen and organic contaminants simultaneously. Reported in Engineering, the technology could provide a faster and more efficient way to treat complex wastewater without adding chemical precursors.

Wastewater treatment plants often rely on separate biological, chemical, and physical steps to control different pollutants. Ammonia can damage aquatic ecosystems by consuming oxygen and promoting excessive algal growth, while organic contaminants contribute to the chemical oxygen demand (COD) of discharged water.

Computer Scientist Pushes a 1996 Algorithm Beyond Its Longstanding Limit

A new algorithm solves a blind spot that has challenged computer scientists since 1996, improving distance estimates for nearby points in massive networks.

Navigation apps usually solve one route at a time, such as finding the fastest way from a hotel to an airport. Computer scientists face a far larger version of that challenge: calculating the shortest distance between every possible pair of locations in a network.

Known as the All-Pairs Shortest Paths (APSP) problem, this task applies to far more than road maps. A graph can represent computers connected by data links, stations joined by rail lines, proteins interacting inside a cell, or neurons communicating in the brain. The points are called vertices, and the connections between them are edges.

Google Gemini Broke Into Real Company Systems After Security Test Domain Mix-Up

Google’s Gemini model has become the latest artificial intelligence (AI) system to access the internet and break into other companies during a cybersecurity evaluation. The development was first reported by The Wall Street Journal.

The incidents occurred in May 2026 as part of a test run conducted by Israeli company Irregular. The evaluation partner was also involved in similar hacks disclosed by OpenAI, Anthropic, and Meta.

According to the Journal, the model gained access to a protected system after repeatedly guessing its password. Two other cases related to the model finding credentials in a public repository, allowing it to obtain unauthorized access to protected systems.

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