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Self-healing layer improves the safety and lifespan of all-solid-state lithium batteries

Scientists have come up with a new way to improve the safety and performance of all-solid-state lithium metal batteries (ASSLMBs), the next-generation energy source technology that is set to power everything from electric vehicles to renewable energy grids.

Most batteries that are in common use today contain flammable liquid electrolytes. The next evolution in batteries is the ASSLMB, which replaces the flammable liquid with a non-flammable solid material to move between electrodes. While they are significantly safer, there is a critical flaw that prevents them from being reliable and long-lasting. That is, repeated charging and discharging cause gaps to form between the solid lithium metal anode and the solid electrolyte, which means the quickly breaks down and stops working.

To solve this problem, researchers from the Chinese Academy of Sciences developed a self-healing layer they call DAI (Dynamically Adaptive Interphase) that keeps the battery connected.

Mom’s voice boosts language-center development in preemies’ brains, study finds

Hearing the sound of their mother’s voice promotes development of language pathways in a premature baby’s brain, according to a new Stanford Medicine-led study.

During the study, which is published in Frontiers in Human Neuroscience, hospitalized preemies regularly heard recordings of their mothers reading to them. At the end of the study, MRI brain scans showed that a key language pathway was more mature than that of preemies in a who did not hear the recordings. It is the first randomized controlled trial of such an intervention in .

“This is the first causal evidence that a speech experience is contributing to at this very young age,” said the lead author, Katherine Travis, Ph.D., who was an assistant professor at Stanford Medicine when the study was conducted and is now an assistant professor at Weill Cornell Medical School and Burke Neurological Institute.

Twisting sound: Scientists discover a new way to control mechanical vibrations in metamaterial

Scientists at the Advanced Science Research Center at the CUNY Graduate Center (CUNY ASRC) have discovered a way to control sound and vibrations using a concept inspired by “twistronics,” a phenomenon originally developed for electronics.

Their research, published in the journal PNAS, introduces “twistelastics”—a technique that uses tiny rotations between layers of engineered surfaces to manipulate how mechanical waves travel.

Sound and control are essential for technologies like ultrasound imaging, microelectronics, and advanced sensors. Traditionally, these systems rely on fixed designs, limiting flexibility. The new approach allows engineers to reconfigure wave behavior by twisting two layers of engineered surfaces, enabling unprecedented adaptability.

Global lead exposure still costs trillions and endangers children, study finds

Lead poisoning was once thought to largely be a problem of the past, as the globe gradually weaned itself off leaded gasoline in road vehicles in 2021. But has global lead pollution truly been resolved?

A new study led by Dr. Chen Mengli, a Research Fellow from the Tropical Marine Science Institute at the National University of Singapore (NUS), in collaboration with researchers from Imperial College London, University of Warwick, University of Oxford, Jadavpur University, University of Michigan, Ann Arbor, Hebrew University of Jerusalem, Massachusetts Institute of Technology, and University of Bristol, showed the answer is not yet: Lead exposure remains a pressing public health and economic challenge in the 21st century.

The researchers estimated that ongoing childhood lead exposure costs the world more than US$3.4 trillion in lost economic potential each year, with disproportionate impacts on low-and middle-income countries.

Living in an unequal society impacts the structure of children’s brains, study finds

The distribution of wealth between different people living in specific geographical regions has changed substantially over the past decades, with some segments of the population benefiting most from economic growth than others. In some parts of the United States, the United Kingdom and various European countries, the distribution of wealth has become increasingly uneven.

An uneven wealth distribution essentially means that there is significant disparity in the income and resources of the general population, with some people earning good salaries and others living in the same place struggling to meet their basic needs. This is typically measured with a value ranging from 0 to 1, known as the Gini coefficient, where 0 represents perfect equality and 1 extreme inequality.

Researchers at King’s College London, Harvard University and the University of York recently carried out a study aimed at exploring the possible impact of living in a society where wealth is unevenly distributed on the brain’s development in late childhood and pre-adolescence. Their findings, published in Nature Mental Health, suggest that living in places with a high income inequality is associated with differences in the structure of some brain regions, which could in turn predict the emergence of mental health disorders.

Super-thin semiconductor overcomes trade-off between speed and thermal stability

A team led by academician Huang Ru and Professor Wu Yanqing from the School of Integrated Circuits at Peking University has developed a super-thin, high-performance semiconductor with enhanced heat conductivity, enabled by a silicon carbide (SiC) substrate. The research, published in Nature Electronics under the title “Amorphous indium tin oxide transistors for power amplification above 10 GHz,” marks a significant step forward in next-generation radio-frequency (RF) electronics.

Amorphous oxide semiconductors (AOS) enable low-temperature, large-area, and chip-compatible processing with . However, their inherently low thermal conductivity leads to self-heating effects, which limit top-gate scaling and high-frequency operation in applications such as 5G communications and the Internet of Things. Overcoming this trade-off between speed and thermal stability remains a central challenge.

This breakthrough using a SiC substrate overcomes the trade-off between speed and in AOS, paving the way for low-cost, flexible, and chip-compatible RF electronics. It demonstrates how combining high-frequency design with effective thermal management can deliver both performance and reliability in high-speed devices.

Artificial muscle can switch from soft to rigid to support 4,000 times its own weight

A research team affiliated with UNIST has unveiled a new type of artificial muscle that can seamlessly transition from soft and flexible to rigid and strong—much like rubber transforming into steel. When contracting, this innovative muscle can lift many times its own weight, delivering energy output far surpassing that of human muscles.

Led by Professor Hoon Eui Jeong in the Department of Mechanical Engineering at UNIST, the research team has successfully created a soft artificial muscle capable of dynamically adjusting its stiffness.

The study is published online in Advanced Functional Materials.

Nanomaterial-based wireless sensor can monitor pressure injuries and hygiene risks in real time

A research team has co-developed a nanomaterial-based ‘wireless multi-sensing platform’ for the early detection of pressure injuries, which have a high prevalence among individuals with limited mobility, including the elderly and people with disabilities. The team’s findings are published in Advanced Functional Materials.

Pressure injuries are among the most painful conditions affecting elderly and disabled individuals in long-term care and rehabilitation facilities. They result from sustained pressure that damages , making regular repositioning and meticulous hygiene care essential.

For patients with , in particular, contact with bio-contaminants such as urine and feces can further irritate the damaged skin and worsen the injuries. However, in hospital settings, a shortage of caregivers or staff makes real-time monitoring of patients’ conditions extremely challenging.

Rewriting the rules of genetics: Study reveals gene boundaries are dynamic, not fixed

Molecular biologists have long believed that the beginning of a gene launched the process of transcription—the process by which a segment of DNA is copied into RNA and then RNA helps make the proteins that cells need to function.

But a new study published in Science by researchers at Boston University and the University of Massachusetts T.H. Chan School of Medicine challenges that understanding, revealing that the beginning and end of genes are not fixed points, but move together—reshaping how cells build proteins and adapt through evolution.

“This work rewrites a textbook idea: the beginning of a gene doesn’t just launch transcription—it helps decide where it stops and what protein you ultimately make,” says Ana Fiszbein, assistant professor of biology and faculty fellow of computing & data sciences, and one of the lead authors of the study.

Physics-informed AI excels at large-scale discovery of new materials

One of the key steps in developing new materials is property identification, which has long relied on massive amounts of experimental data and expensive equipment, limiting research efficiency. A KAIST research team has introduced a new technique that combines physical laws, which govern deformation and interaction of materials and energy, with artificial intelligence. This approach allows for rapid exploration of new materials even under data-scarce conditions and provides a foundation for accelerating design and verification across multiple engineering fields, including materials, mechanics, energy, and electronics.

Professor Seunghwa Ryu’s research group in the Department of Mechanical Engineering, in collaboration with Professor Jae Hyuk Lim’s group at Kyung Hee University and Dr. Byungki Ryu at the Korea Electrotechnology Research Institute, proposed a new method that can accurately determine material properties with only limited data. The method uses physics-informed machine learning (PIML), which directly incorporates physical laws into the AI learning process.

In the first study, the researchers focused on hyperelastic materials, such as rubber. They presented a physics-informed neural network (PINN) method that can identify both the deformation behavior and the properties of materials using only a small amount of data obtained from a single experiment. Whereas previous approaches required large, complex datasets, this research demonstrated that material characteristics can be reliably reproduced even when data is scarce, limited, or noisy.

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