Electrical interconnects may very well be the unsung heroes of modern microchips. These tiny wires—typically made of copper due to its high conductivity—string together the billions of transistors that drive our computers and electronic devices. But as the technology advances and additional transistors are piled on, the components must shrink to the nanoscale. And that's when copper begins to fail.

Facing mounting pressure to acknowledge that Grok can still be used to generate non-consensual sexualized images of adults and minors, xAI filed a lawsuit Tuesday, suing the first user that Elon Musk’s firm has accused of using its chatbot to create illegal content.

The complaint targets Terry Wayne Harwood, who was arrested earlier this year for possession and distribution of child sexual abuse materials (CSAM), the South Carolina attorney’s office announced.

As xAI alleged, the company assisted in that arrest after discovering that Harwood had been using two xAI accounts for months to undress or “nudify” non-sexual images of multiple victims, including a young girl who appeared to be as young as 10.

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Nanocrystals are so useful that they formed the basis of the 2023 Nobel Prize in Chemistry. But despite their usefulness, scientists have so far been able to make these microscopic crystals from only a limited palette of materials. A group of chemists at the University of Chicago and Argonne National Laboratory has announced a way to make nanocrystals from a useful class of materials known as metal nitrides—a previously impossible task.

Bacterial cellulose—nature’s own polymer scaffold—is gaining attention as a renewable feedstock for supercapacitor electrodes. With modern devices demanding fast charge, high power bursts, and long cycle life, researchers are searching for electrode materials that combine performance with sustainability.

A new systematic literature review led by Prof. Dahlang Tahir at Hasanuddin University, Indonesia, maps how bacterial cellulose-derived carbon (BCC) is turned into energy-storage electrodes—and why some fabrication paths outperform others. The study synthesizes evidence from the scientific record to clarify which processing choices control electrochemical behavior.

The review focuses on BCC as a precursor to porous carbon. Bacterial cellulose forms a naturally pure, interconnected network of nanoscale fibers, and heat treatment can convert that architecture into carbon structures with tunable porosity—critical for charge storage. The authors emphasize that electrical performance is not just a matter of “making carbon,” but of preserving and engineering the fiber network before carbonization.

Across 49 Scopus-indexed journal articles, the team compares major strategies including direct carbonization, chemical activation to enlarge pore systems, heteroatom doping to modify surface chemistry, and composite fabrication with materials that can add rapid redox (pseudocapacitive) contributions.

A recurring message is the importance of pre-carbonization drying. Freeze-drying appears as the most commonly used approach because it limits collapse of the wet nanofiber structure during water removal. Since pore architecture governs ion access and charge transport, maintaining nanoscale structure can translate into higher effective capacitance.

The review also distinguishes test formats. Three-electrode measurements are frequently reported, but two-electrode devices better represent real supercapacitor operation, where electrode–electrode interactions shape performance.

When processing is optimized, the results point toward a pathway for BCC-based electrodes to rival or surpass commercial activated carbon under comparable conditions. Activation and heteroatom doping generally increase accessible surface area and create additional active sites, while composites often achieve the strongest capacitance by combining electrical double-layer effects with fast surface reactions.

Yet the authors warn that progress is constrained by inconsistent experimental reporting, uneven protocols, and limited mechanistic understanding. To move beyond laboratory demonstrations, they call for predictive design, data-driven structure–performance models, scalable carbonization methods, and robust flexible devices resistant to deformation and humidity.

Subject of Research: Supercapacitor electrodes using bacterial cellulose-derived carbon
Article Title: Bacterial cellulose-derived carbon electrodes for supercapacitors: Fabrication strategies, electrochemical performance, and mechanical properties — A review
News Publication Date: 9 June 2026
Web References: https://doi.org/10.1016/j.est.2026.123044
References: 10.1016/j.est.2026.123044
Image Credits: Lightenoughtotravel from Wikimedia Commons

Keywords

bacterial cellulose; supercapacitors; porous carbon; freeze-drying; chemical activation; heteroatom doping; electrode materials; two-electrode testing; pseudocapacitance; sustainable energy storage

A meteorite recovered immediately upon its fall to Earth on July 16, 2024, is helping NASA scientists uncover new clues about ancient water, the chemical evolution of primitive asteroids, and the ingredients that may have helped make life possible throughout the early solar system.

This rapid recovery began when an amateur astronomer in New Jersey quickly recognized that a newly fallen meteorite had landed on his property. Recognizing its scientific value and wearing protective gloves, he collected the fragments and stored them in aluminum foil and glass containers, which preserved delicate minerals and organic compounds that are often altered by moisture, weather, and contamination.

As the meteorite fell to Earth, cameras across New Jersey captured its fiery passage through the atmosphere. Scientists used these observations to reconstruct the fireball’s trajectory and, after recovering the meteorite, combined this data with laboratory analyses to determine where in the solar system the rock most likely originated. In a study published Wednesday in the journal Science Advances, researchers found evidence that ancient salty water altered minerals within the meteorite’s parent asteroid, preserving unique minerals and a rich inventory of organic compounds.

“When we have both a documented fireball and a quick recovery of its meteorite, we can learn not only what the rock is made of, but where it came from in the asteroid belt,” said Peter Jenniskens, meteor astronomer at both NASA’s Ames Research Center in California’s Silicon Valley and the SETI Institute, and lead author of the study.

Named for the township where it was recovered, the Hillsborough meteorite belongs to a class of carbon-rich meteorites known as CM carbonaceous chondrites. These primitive rocks preserve some of the oldest materials in the solar system, recording the chemical processes that shaped asteroids more than 4.5 billion years ago.

While examining the unusually pristine meteorite, researchers found a mosaic of tiny broken-up rocks and noticed that some contained unusually high concentrations of sodium — an unexpected finding for this type of meteorite. The surprising signal prompted a closer investigation using powerful electron microscopes that allowed scientists to examine the meteorite from the millimeter scale down to individual atoms. By combining observations across multiple scales, researchers reconstructed the history of the minerals and the fluids that once flowed through them.

These analyses revealed microscopic fractures filled with sodium-rich material left behind by ancient brines. Unlike pure water, brines contain dissolved salts that allow them to transport elements and chemically alter the rocks they move through. In the case of the Hillsborough sample, those ancient fluids altered the asteroid’s minerals and left behind chemical evidence that remained preserved for billions of years.

Scientists were also able to detect fragile sodium-carbonate salts that normally react with moisture in Earth’s atmosphere before they can be studied. Jangmi Han, a paper co-author and mineralogist at NASA’s Johnson Space Center in Houston, identified evidence of ancient brines preserved within microscopic fractures. Similar salts were identified in samples returned from the asteroids Bennu and Ryugu by NASA’s OSIRIS-REx mission and JAXA’s (Japan Aerospace Exploration Agency) Hayabusa2 mission. However,Hillsborough marks the first time the salts have been identified in a CM carbonaceous chondrite meteorite, offering a new glimpse into the surfaces of the primitive asteroids that produced these meteorites.

Together, these findings suggest that ancient, salt-rich brines were more widespread among primitive asteroids than previously recognized, and provide scientists with new opportunities to compare how water altered different asteroid bodies across the early solar system.

“The chips of the most salt-rich bits of this meteorite are quite comparable to the samples returned by the Hayabusa2 and OSIRIS-REx missions,” said Mike Zolensky, a meteorite researcher at NASA Johnson and co-author of the study. “They’re not identical. They’re different in some very interesting ways, but they’ve seen very similar processes.”

Scientists expected Hillsborough to contain a rich suite of organic compounds because it is a CM carbonaceous chondrite. What made the meteorite exceptional was how quickly it was recovered, allowing researchers to study those compounds before prolonged exposure to Earth’s environment could contaminate the sample.

“One of the big surprises for me when we analyzed a small chip of the Hillsborough meteorite was the complexity of amino acids and other organic compounds,” said Danny Glavin, senior scientist in the Astrobiology Analytical Laboratory at NASA’s Goddard Space Flight Center in Greenbelt, Maryland, and co-author of the study.

Its diversity of amino acids and other organic compounds is, comparable to the Murchison meteorite, a nearly 100-kilogram carbonaceous chondrite that fell in Australia in 1969 and became the benchmark for extraterrestrial organic chemistry.

“It’s just more proof that the chemical building blocks of life could have been delivered — and are still being delivered — to Earth today by these carbonaceous asteroid fragments,” said Glavin, who was a co-investigator on OSIRIS-REx, leading an international team that studied the organic composition of the samples delivered to Earth from asteroid Bennu in 2023.

Understanding the Hillsborough meteorite required expertise from multiple scientific disciplines.

Astronomers reconstructed the meteorite’s journey through space, finding evidence that it may have originated from the Erigone asteroid family in the inner asteroid belt, home to the asteroid Donaldjohanson, which was visited in 2025 by NASA’s Lucy spacecraft. Mineralogists identified evidence of ancient brines preserved within microscopic fractures, while organic chemists analyzed the meteorite’s inventory of amino acids and other organic compounds.

“Together, those complementary studies are helping scientists build one of the clearest pictures yet of how primitive asteroids such as the asteroid Erigone evolved chemically over billions of years,” said Jenniskens.

Researchers continue to study the Hillsborough meteorite, revealing new details about how water transformed primitive asteroids and shaped the early solar system.

By tracing the history of water on primitive asteroids, scientists are learning how water and the chemical ingredients for life were distributed throughout the early solar system.

“If you follow the water through the solar system, you’re actually following life,” Zolensky said. “Following the history of water through the solar system is an essential part of understanding the origin of life.”

For more information on NASA’s astromaterials research and exploration, visit:

https://science.nasa.gov/astromaterials

Karen Fox / Molly Wasser
Headquarters, Washington
240-285-5155 / 240-419-1732
karen.c.fox@nasa.gov / molly.l.wasser@nasa.gov

Victoria Segovia
NASA’s Johnson Space Center, Houston 281-483-5111
victoria.segovia@nasa.gov

In the past weeks, two sites in the United States have halted operations.

© Maddie McGarvey for The New York Times

US rare earths produced by Washington-backed companies are flowing to Japan and South Korea, as American demand has yet to materialize despite the Trump administration’s push to develop a national supply chain.

Rare earths products produced by MP Materials, Energy Fuels and Phoenix Tailings—which together have won billions of dollars in US government support—are being sold to companies in Asia, where the scale of magnet manufacturing remains larger than the nascent production in the US.

China’s lock on global supplies of rare earths and critical minerals has become a national security concern in the US and other Western nations, since Beijing started restricting access to them. The metals are crucial to 21st-century technology and are used in the manufacturing of everything from weapons guidance systems to electric vehicle batteries.

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© David McNew / Contributor

US rare earths produced by Washington-backed companies are flowing to Japan and South Korea, as American demand has yet to materialize despite the Trump administration’s push to develop a national supply chain.

Rare earths products produced by MP Materials, Energy Fuels and Phoenix Tailings—which together have won billions of dollars in US government support—are being sold to companies in Asia, where the scale of magnet manufacturing remains larger than the nascent production in the US.

China’s lock on global supplies of rare earths and critical minerals has become a national security concern in the US and other Western nations, since Beijing started restricting access to them. The metals are crucial to 21st-century technology and are used in the manufacturing of everything from weapons guidance systems to electric vehicle batteries.

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© David McNew / Contributor

A single machine learning model just turned one of condensed matter physics’ most persistent problems into a navigable map.

In a study published in National Science Review, researchers led by Nanjing University used AI to sort millions of magnetoresistance curves into 13 clean categories, then charted exactly how tuning a material’s electronic properties pushes it from one behavior to another.

The work targets the anomalous Hall effect, a decades-old phenomenon that governs how electrons move through certain magnetic materials, and it could hand physicists a shortcut to some of the most sought-after states in modern electronics.

A Problem Too Complex to Map by Hand

The anomalous Hall effect occurs when electrons moving through a magnetic material exhibit behavior beyond the predictions of the conventional Hall effect. Standard Drude theory predicts that resistance should shift as electrons move through a magnetic field. Anomalous Hall systems break that rule, producing resistance curves with double peaks, plateaus, and non-saturating shapes that shift with the material’s band structure and the way its electrons scatter.

The challenge becomes even greater when multiple electron energy bands are active near the Fermi level. Each additional parameter increases the difficulty of interpreting the resulting resistance curves, and until now, constructing a comprehensive map linking electronic properties to resistance behaviors has not been possible.

The Nanjing team set out to create that map. They generated more than 2.27 million magnetoresistance curves from a two-band model spanning five electronic parameters, then set an unsupervised algorithm loose on the dataset with no instructions beyond finding the pattern.

Millions of Curves

Every curve in the dataset collapsed into just 13 distinct types, a verdict so blunt that even the researchers seemed surprised. A trained neural network took over from there, sorting new curves into those same 13 categories with 99% accuracy and turning what had looked like an unruly set of data into something closer to a field guide.

Next, the team created phase diagrams and topological networks to visualize how changing just one parameter could shift a material from one type of resistance curve to another. Instead of a fixed chart, the result is more like an interactive roadmap; adjust a setting, and you can see the transition happen in real time.

To check whether any of this held up outside a computer model, the researchers tested their framework against real measurements from gated Fe5GeTe2 nanoflakes, a magnetic material whose properties can be tuned electrically. The experimental data closely matched the AI-generated phase diagrams, providing the model with a real-world anchor rather than a purely theoretical one.

“This is just the beginning. We believe this framework can be extended to a wide range of intricate models and will help uncover many more complex problems in condensed matter physics,” said Hongtao Yuan, professor at Nanjing University and an author of the study.

A Map That Points to New Physics

The significance of this work extends beyond classification. The phase diagrams indicate which parameter combinations are likely to produce large magnetoresistance, including giant magnetoresistance, a phenomenon already used in hard drive technology. These maps also identify regions where the quantum anomalous Hall effect may occur, a state in which an electric current flows along the edges of a material without energy loss due to resistance.

That kind of lossless conduction has long been seen as a target for low-power electronics, but identifying the narrow parameter windows that produce it has historically relied on trial-and-error. A phase diagram that predicts those windows in advance changes the search from guesswork into targeting.

“Our model provides a framework for comprehensively addressing the complex magnetoresistance behavior in anomalous Hall systems, and serves as a platform for predicting parameter regions that may host intriguing quantum phenomena such as giant magnetoresistance,” said author of the study Ganyu Chen.

The team frames the two-band model as a starting point rather than a finished tool. The same machine-learning approach, they note, should extend to more complex systems beyond ferromagnets, including topological insulators and superconducting junctions, areas of physics that have their own tangled, high-dimensional parameter spaces still waiting for a map. Whether AI-built phase models can keep pace with that added complexity, or whether some of these systems will require entirely new modeling strategies, remains an open question the field is only beginning to test.

Austin Burgess is a writer and researcher with a background in sales, marketing, and data analytics. He holds an MBA, a Bachelor of Science in Business Administration, and a data analytics certification. His work focuses on breaking scientific developments, with an emphasis on emerging biology, cognitive neuroscience, and archaeological discoveries.

Building electronic devices often requires stacking different crystalline materials on top of one another with atomic precision. This process, called heteroepitaxy, works best when the two crystals share similar atomic patterns. When their symmetries differ, the upper crystal tends to grow in several orientations at once instead of a single one, creating defects that degrade device performance. Engineers have long dealt with this problem by treating the surface of the lower crystal beforehand, for example by etching steps into it, exposing it to plasma, or adding a buffer layer, to help the two materials bond more effectively. Now, a team of researchers explores ¹ a different route: rather than forcing the two crystals to fit together, it takes advantage of a crystal that is naturally flexible at the atomic scale.

Flexible by design

The material at the center of this approach is a layered compound called TaCo₂Te₂. Like graphite, its layers are held together by weak van der Waals forces, which makes it easy to combine with other materials without demanding a perfect chemical match. Unlike most layered crystals, though, its atoms are not arranged in a perfectly regular pattern. At room temperature they undergo a small, periodic shift known as a Peierls distortion, a phenomenon first described in the 1930s by the physicist Rudolf Peierls, who showed that in some materials the electrons and the surrounding lattice can lower their combined energy by nudging atoms slightly out of their ideal positions. When TaCo₂Te₂ is heated above about 523 kelvin, or 250 degrees Celsius, this distortion disappears and the crystal adopts a more symmetric structure, although traces of the original pattern linger as fleeting, temperature-driven fluctuations.

These fluctuations are not random. Calculations of how the crystal vibrates, backed up by Raman spectroscopy and electron diffraction experiments, show that the lattice is especially unstable along one crystallographic direction, meaning atoms can shift more easily there than in the perpendicular direction. This directional softness turns out to be the key ingredient that controls how a second material grows on the surface.

Heating TaCo₂Te₂ also sets its atoms in motion across the surface. Tantalum atoms drift away, while cobalt and tellurium reorganize into a thin crystalline layer whose composition resembles cobalt telluride. Rather than clumping into isolated islands, this new layer spreads sideways first and only then thickens, producing a smooth, continuous interface with the crystal beneath it, a layer-by-layer growth style that electron microscopy confirms is energetically favorable.

Directionally locked

The most striking result is that the new crystal does not settle into just any orientation. Even though its symmetry differs from that of the underlying TaCo₂Te₂, it becomes directionally locked. Along one in-plane direction, the atomic spacings of the two materials match closely, giving a tight, well-ordered registry. Along the perpendicular direction the mismatch is far larger, but this happens to be exactly the direction where TaCo₂Te₂ is most flexible, thanks to its lattice instability. Instead of rotating to relieve that mismatch, as symmetry-mismatched interfaces typically do, the interface absorbs it through a gentle, ongoing structural adjustment. The result is a crystal that stays aligned while only one direction bears most of the strain.

That flexible direction does not settle into a simple repeating pattern either. Instead, it forms what is called a one-dimensional incommensurate superlattice: the repeating atomic spacings of the two crystals never line up exactly, yet they stay locked together over long distances. Physicists have long recognized this kind of arrangement as an efficient way to accommodate mismatched lattice spacings without piling up large numbers of defects, since the strain is spread out through a slowly changing modulation rather than concentrated at sharp boundaries. Diffraction measurements and a geometric analysis of the two lattices both confirm that no perfectly repeating, shared lattice can account for what is observed, so the incommensurate description fits best.

Directional lattice instability is key

To check that the lattice instability is truly responsible for this behavior, the same experiment was repeated with a closely related compound, TaNi₂Te₂, which has nearly the same crystal structure but lacks the structural instability. Under similar heating, a surface layer still forms, but its orientation varies from spot to spot, and rotational disorder sets in. This comparison shows that matching crystal symmetry is not enough on its own to produce a locked, well-ordered interface. The directional lattice instability is the extra ingredient that guides the growth.

The local reconstruction effect

One more effect helps stabilize the interface. Even above the temperature where the bulk crystal should be fully symmetric, the atomic layers right beneath the interface partly slip back into the low-temperature distorted structure. This local reconstruction stays confined to the surface rather than spreading through the whole crystal, and it appears to strengthen the bond between the two materials, keeping the aligned structure stable even at high temperature.

As we have just seen, structural instabilities, usually considered as a liability because they signal that a crystal is on the verge of changing shape, can instead become useful design tools. A substrate whose atoms rearrange more easily in certain directions can guide the growth of otherwise incompatible materials, producing well-aligned interfaces without extensive surface treatment. This idea could widen the range of material combinations available for future electronic, optical, and quantum devices, where the atomic arrangement at an interface often determines how well the whole device performs.

Author: César Tomé López is a science writer and the editor of Mapping Ignorance

Disclaimer: Parts of this article may have been copied verbatim or almost verbatim from the referenced research paper/s.

The post Rewriting the rules of heteroepitaxy with a flexible crystal appeared first on Mapping Ignorance.

Southwest Research Institute (SwRI) and St. Mary’s University have embarked on a groundbreaking collaboration aimed at revolutionizing the future of hydrogen storage technology. This joint venture seeks to develop a comprehensive, physics-based forecasting tool capable of predicting the degradation and long-term durability of titanium-iron (TiFe)-based metal hydride vessels. Leveraging a chemical and thermo-mechanical modeling framework, the project represents a significant leap forward in tackling one of the most persistent challenges in hydrogen storage materials science. This initiative is made possible through a $125,839 grant from the St. Mary’s-SwRI Technology and Applied Research (S²TAR) Program, designed to foster impactful research collaborations.

Metal hydrides are pivotal materials in the hydrogen energy landscape, serving as one of the few safe and compact solid-state options for hydrogen storage. These materials function by chemically binding hydrogen to metal lattices through chemisorption, effectively locking hydrogen atoms within the structure. Unlike high-pressure gaseous tanks or cryogenic liquid hydrogen storage, metal hydrides offer improved safety profiles and reduce energy-intensive requirements. However, despite these advantages, their widespread application is hindered by their limited durability and performance degradation resulting from repeated hydrogen absorption and desorption cycles.

At the heart of this ambitious research endeavor is the intricate problem of metal hydride vessel degradation. Over numerous charge and discharge cycles, physical changes ensue within the alloy powders comprising the metal hydride bed. These changes include fracturing of particles, densification of the powder bed, and mechanical stresses that collectively impair hydrogen storage capacity and vessel performance. Traditional evaluation methods rely heavily on prolonged, costly experimental testing to assess long-term vessel integrity—a bottleneck that impedes innovation and commercialization.

The project team, headed by Dr. Richard S. Fu of SwRI and Dr. Mohamed Shaat of St. Mary’s University, seeks to transcend these limitations by integrating advanced physics-based simulations with targeted experimental validation. This dual approach aims to create a robust modeling framework capable of forecasting the lifecycle and operational durability of TiFe metal hydride vessels. The predictive tool will account for the coupled chemical, thermal, and mechanical phenomena driving hydrogen cycling performance over hundreds or even thousands of cycles.

Dr. Shaat is spearheading the development of an intricate, multiphysics computational framework at St. Mary’s University. This model is designed to encapsulate the complex interplay between hydrogen diffusion, chemisorption reactions, phase transformations within the metal hydride, heat generation and transfer, and the evolution of mechanical stress within the vessel. By accurately simulating these interconnected processes, the framework seeks to facilitate precise performance predictions, while also identifying design parameters and operational regimes that optimize both efficiency and vessel longevity.

Central to the team’s vision is the translation of fundamental physical and chemical principles of hydrogen interaction into computationally efficient predictive models. Dr. Shaat emphasizes the importance of capturing the coupling mechanisms—particularly how hydrogen transport influences thermal and mechanical responses and vice versa—in order to provide reliable simulations that replace and reduce the dependence on lengthy physical testing. This innovative approach promises to significantly reduce costs, accelerate development cycles, and improve hydrogen storage system designs.

On the experimental front, SwRI’s team, under Dr. Fu’s guidance, is undertaking controlled, long-term cycling experiments to collect essential validation data. By subjecting metal hydride vessels to realistic charge-discharge scenarios, they aim to quantitatively monitor performance degradation mechanisms as they unfold. These experiments will provide the empirical backbone necessary to calibrate and refine the modeling framework, ensuring that simulation outputs faithfully represent real-world behavior under operational conditions.

The synergy between modeling efforts and experiments forms the cornerstone of the project’s iterative design loop. As Dr. Fu explains, modeling insights will direct the focus of experimental tests to critical phenomena, while the resulting data will feedback to correct and enhance the models. This dynamic interplay is expected to unravel previously obscure degradation mechanisms by illuminating the coupled chemical, thermal, and mechanical interactions at work within the metal hydride vessels. Ultimately, this feedback-driven methodology will yield a predictive tool capable of guiding material improvements and engineering decisions.

Hydrogen’s emergence as a clean, versatile energy carrier is a key cornerstone of global efforts to decarbonize energy and transportation sectors. However, to fulfill its potential, advances in storage technology are imperative. Unlike batteries or fossil fuels, hydrogen storage demands novel materials and engineering solutions due to its unique physical and chemical characteristics. By focusing on metal hydride vessels, this project addresses a promising yet underdeveloped storage pathway that could unlock safer, more efficient hydrogen deployment across diverse applications.

The S²TAR funding mechanism plays an instrumental role in enabling this interdisciplinary collaboration by providing seed resources that draw on the expertise strengths of both SwRI and St. Mary’s University. This partnership is not only poised to produce high-impact scientific outcomes but also lays the foundation for sustained cooperation. Through this collaboration, the team is well-positioned to attract larger external funding sources from federal agencies and industry players focused on advancing hydrogen infrastructure and clean energy technology.

As work progresses, the team anticipates that their predictive modeling tool will be an invaluable resource for the hydrogen storage community. It will offer designers and researchers an evidence-based platform to evaluate new materials, optimize vessel designs, and predict operational lifetimes with higher confidence. By systematically addressing the core durability challenges of metal hydride storage, this project represents a wide-reaching advancement that could significantly accelerate solid-state hydrogen storage adoption worldwide.

In conclusion, this joint initiative by Southwest Research Institute and St. Mary’s University epitomizes the cutting edge of hydrogen storage research. Through combining rigor in experimental validation and sophistication in physics-based modeling, the project champions a future where hydrogen is stored more safely and efficiently. The knowledge generated here promises to not only rewrite the fundamentals of storage vessel degradation understanding but also to catalyze practical applications in the global hydrogen economy.

---

Subject of Research: Physics-based modeling and experimental validation of TiFe-based metal hydride hydrogen storage vessel degradation and durability.

Article Title: Advancing Solid-State Hydrogen Storage: Predictive Modeling and Experimental Synergy for Metal Hydride Vessel Durability

News Publication Date: June 24, 2026

Web References: https://www.swri.org/markets/energy-environment/power-generation-utilities/advanced-power-systems/hydrogen-energy-research?&utm_medium=referral&utm_source=eurekalert!&utm_campaign=s2tar-metal-hydrides-pr

Image Credits: Southwest Research Institute

Keywords

Hydrogen storage, metal hydrides, TiFe alloys, chemisorption, physics-based modeling, thermo-mechanical framework, hydrogen diffusion, phase transformation, performance degradation, solid-state hydrogen storage, experimental validation, multiphysics simulations

In a groundbreaking development poised to redefine waste management and sustainable energy production, researchers have unveiled a revolutionary method for photoreforming solid waste on an unprecedented 1-square-meter scale. This innovative advance, detailed in the latest issue of Nature Chemical Engineering, leverages single-source precursor-derived co-catalyst films to convert ubiquitous solid waste into valuable chemical fuels, heralding a new era in environmental remediation coupled with renewable energy generation.

At the heart of this innovation lies the principle of photoreforming, a process by which sunlight catalyzes the chemical transformation of organic materials into hydrogen and other energy-rich molecules. Historically limited to small-scale demonstrations and powdered catalysts, this method struggled with scalability and practical application outside controlled laboratory settings. The reported technique shatters these limitations by fabricating robust co-catalyst films derived from a unified precursor source, enabling efficient photoconversion across large surface areas with enhanced stability and performance.

The researchers crafted these functional films through an ingenious synthesis route, where a single molecular precursor simultaneously yields both the active catalytic sites and the supporting matrix. This contrasts with conventional multi-step fabrication approaches that often result in inconsistent catalyst dispersion and energy losses. By integrating the catalyst components at the molecular level, the team ensured homogeneity, maximized photon absorption, and optimized charge separation dynamics, all critical parameters for sustained photocatalytic activity.

Transforming real-world solid waste – encompassing plastics, biomass residues, and mixed refuse – into clean fuels presents a formidable challenge due to their complex chemical compositions and structural heterogeneity. The co-catalyst films demonstrated remarkable versatility and adaptability, efficiently processing these diverse substrates under simulated sunlight without requiring extensive pre-treatment. This robustness signals a significant leap toward practical deployment in municipal waste processing facilities and industrial settings.

The experimental setup encompassed a square meter of coated substrate exposed to controlled illumination, mirroring natural sunlight intensity conditions. Over extended operation, the system consistently yielded high rates of hydrogen and other value-added chemicals, outperforming benchmark photocatalysts by a considerable margin. Importantly, the films manifested remarkable photostability and mechanical adhesion, demonstrating resilience against degradation mechanisms like photo-corrosion and mechanical abrasion that typically afflict photocatalytic layers.

At the nanoscale, characterization techniques revealed uniform distribution of nanosized catalytic domains embedded within a conductive, photoactive matrix. This architecture ensures rapid electron-hole separation and transport, minimizing recombination losses which commonly plague photocatalytic systems and limit hydrogen evolution rates. Spectroscopic analyses corroborated enhanced visible-light absorption, attributed to tailored bandgap engineering achieved during precursor design, broadening the usable solar spectrum beyond ultraviolet wavelengths.

The process design also embraced mass transport optimization, incorporating porous film structures that facilitated effective diffusion of reactants and removal of gaseous products. This morphologic control prevented stagnation zones and concentration gradients, enhancing catalytic turnover and ensuring stable long-term performance. Additionally, the modular film fabrication approach promises scalability and integration into various reactor geometries without compromising catalytic efficiency.

Beyond hydrogen generation, the system also showcased the capacity to produce liquid fuels and chemical feedstocks, capitalizing on selective reaction pathways induced by co-catalyst composition tuning. This selectivity allows tailored conversion routes matching industrial chemical demands, moving beyond mere waste disposal toward circular chemical economies. The dual benefit of environmental waste mitigation coupled with clean energy and chemical synthesis embodies transformative potential for sustainable industrial practices.

Critically, the team emphasized the environmental and economic implications of adopting such technology at scale. By converting problematic solid waste streams into valuable resources using sunlight – a free and abundant energy source – this approach diminishes reliance on fossil fuels and reduces landfill burden. Cost analyses suggested that, once scaled, the technique could rival established catalytic processes in operational expenditure, thus offering an attractive proposition for policymakers and industry leaders aiming to meet stringent environmental targets.

The interdisciplinary collaboration instrumental in achieving this advance integrated expertise across materials chemistry, photophysics, environmental engineering, and nanofabrication. Such synthesis of disciplines underscored the inherent complexity of developing scalable photocatalytic platforms capable of handling real-world waste complexities while maintaining high efficiency and durability.

Looking ahead, the researchers are exploring further enhancements including tandem catalyst layers, optimized co-catalyst configurations, and hybrid photochemical-electrochemical systems to elevate the energy conversion efficiency and broaden substrate compatibility. In parallel, pilot-scale demonstrations are underway to validate system performance in outdoor environments subject to variable weather conditions, pivotal for transitioning laboratory innovation into field applications.

This pioneering work sets a new benchmark in photoreforming science, illustrating how precise molecular engineering and thoughtful system design can transform a pressing global challenge—solid waste accumulation—into a renewable energy opportunity. Its implications resonate strongly with global sustainability aspirations, promising an economically feasible and environmentally benign pathway to simultaneously address climate change mitigation, waste reduction, and clean energy production.

As society grapples with mounting waste generation paired with escalating energy demands, innovations such as these underscore the invaluable role of scientific ingenuity in crafting solutions that are as elegant as they are practical. By harnessing the synergy of sunlight and advanced material chemistry, the future of waste management is illuminated—not as a burden, but as a wellspring of renewable chemical energy.

In summary, this major scientific milestone demonstrates that photoreforming solid waste at a 1 m² scale using single-source precursor-derived co-catalyst films is no longer a theoretical possibility but a tangible technological reality. It unequivocally paves the way for deploying solar-driven catalytic systems in addressing environmental and energy crises through smart design and scalable engineering.

Subject of Research:
Photoreforming of solid waste using single-source precursor-derived co-catalyst films.

Article Title:
Photoreforming of solid waste on 1 m² scale using single-source precursor-derived co-catalyst films.

Article References:
Bin Mohamad Annuar, A., Liu, Y., Bhattacharjee, S. et al. Photoreforming of solid waste on 1 m² scale using single-source precursor-derived co-catalyst films. Nat Chem Eng 3, 351–362 (2026). https://doi.org/10.1038/s44286-026-00406-y

Image Credits:
AI Generated

DOI:
10.1038/s44286-026-00406-y

Keywords:
Photoreforming, solid waste conversion, co-catalyst films, solar energy, hydrogen production, photocatalysis, renewable energy, waste-to-fuel, sustainable chemistry, scalable catalysis

The move targets two U.S. manufacturers at the center of the Trump administration’s effort to rebuild the domestic supply chain for critical magnets.

© Steve Marcus/Reuters

What if your daily coffee routine could help power the world around you? 

Scientists are exploring ways to transform everyday waste, like spent coffee grounds, into materials for greener energy production. One promising method, called triboelectrification, uses electricity generated from motion. With this approach, materials that gain positive and negative charges are rubbed together to generate an electrical charge that can be harnessed by devices called triboelectric nanogenerators, or TENGs. TENGs are a greener alternative to fossil fuels because they generate electricity from everyday movement, such as pedestrian or vehicle motion. 

However, many negatively charged materials used in these devices are made from nonbiodegradable plastics, like PTFE, that persist as long-term waste in the environment. Over time, TENGs are also easily damaged by environmental conditions outside the laboratory. To create sustainable TENGs for real-world applications, scientists need to develop biodegradable and durable designs. 

To address this problem, researchers from Guangxi University in China developed a TENG that uses coffee grounds as its negatively charged material. Their design repurposes coffee waste to create a robust, low-cost device that can harvest energy.

To create the coffee-based material, the researchers mixed coffee grounds with a biodegradable glue-like plastic called polycaprolactone (PCL). They heated this mixture to 80°C (176°F) and molded it into cylindrical pellets. Then, they used a biodegradable plastic called polylactic acid (PLA) to 3D-print a fully enclosed box-like TENG containing vertical channels arranged like the parallel slots of a file organizer to house the pellets. 

Their TENG worked by shaking the coffee ground pellets within the channels, causing them to repeatedly rub against the PLA surfaces. Because coffee grounds strongly attract electrons, the pellets become negatively charged while the PLA surfaces become positively charged. This repeated pellet movement drove the electrons back and forth, generating electricity that was harvested through the TENG’s external circuit. 

To evaluate their TENG’s electrical performance, the researchers used an instrument that measures small amounts of electricity, called an electrometer. The electrometer measures electrical output based on how strongly the device pushes electrons through a circuit, called its voltage, how quickly electrons flow through it, called its current, and how many electrons flow through it, called its transferred charge.

The researchers verified that coffee ground pellets could serve as a viable TENG material by comparing their performance with commercial plastic pellets. The coffee ground pellet system produced a voltage of 0.6 Volts (V), a current of 18 nanoamperes (nA), and a charge transfer of 0.25 nanocoulombs (nC). This output was more than twice that of pure PCL pellets and over half that of nonbiodegradable PTFE. The researchers suggested that the negatively charged components of the coffee grounds altered the surface of the PCL to increase its charge generation and transfer. 

To optimize the TENG, the researchers experimented with different coffee grounds to PCL mass ratios, pellet lengths, and device vibrational frequencies. They found that their device achieved the best electrical performance at a 3:1 ratio of coffee grounds to PCL, with 8 millimeter long pellets, at a vibrational frequency of 2.6 Hertz. These conditions boosted the device’s electrical output to 4.7 V, 75 nA, and 1.3 nC, high enough to power tiny environmental sensors. 

The team also tested their device’s long-term durability by comparing its electrical performance before and after 6 months of storage. They observed only a 6% drop in voltage after storage, demonstrating that it could maintain stable long-term electrical performance for at least 6 months. 

Finally, to explore real-world applications, the researchers integrated 4 TENGs into a wind-energy harvesting system designed for remote island settings. The system used a wheel of rotating cups to capture wind energy and a sliding mechanism to convert it into repeated shaking of the TENGs. As the TENGs shook, the moving pellets inside generated enough electricity to power an LED light and a small weather-monitoring device. 

The researchers concluded that spent coffee grounds can function as biodegradable TENG materials while reducing dependence on fossil fuel-derived materials. By combining coffee waste with biodegradable plastics, the researchers created a TENG with good electrical performance, sustainability, and long-term stability. They suggested that with further development, the device could someday power road warning lights and provide sound-based alerts in remote areas.

The post Brewing sustainable power from used coffee grounds appeared first on Sciworthy.

In the realm of advanced manufacturing and materials science, nickel-based alloys have long stood as a cornerstone for various high-performance applications, especially within nuclear reactors where exposure to neutron irradiation is inevitable. A recently corrected study by Roy, Mondal, Clement, and colleagues published in npj Advanced Manufacturing delves deep into how these materials respond to such extreme conditions. This comparative analysis between powder metallurgy-hot isostatic pressing (PM-HIP) and forging manufacturing methods unravels critical insights that could revolutionize the future selection and engineering of nuclear materials.

Neutron irradiation, a primary stressor in nuclear environments, can dramatically change the microstructural and mechanical properties of alloys. The study foregrounds two predominant fabrication techniques for Ni-based superalloys—PM-HIP and forging—and investigates how each method governs the alloys’ resilience against such irradiation. Given that any degradation in these materials could undermine reactor safety and efficiency, understanding these effects is paramount.

The PM-HIP process, an innovatively additive approach, involves pressing powdered alloy under high temperature and pressure to densify the structure. This technique inherently produces fine-grained microstructures with minimal defects. Conversely, forging, a traditional approach, shape-controls metals through plastic deformation at elevated temperatures, often yielding distinct grain structures and phase distributions. Each method imparts unique starting conditions affecting how the materials evolve under neutron bombardment.

Roy and team meticulously subjected both PM-HIP and forged Ni-based alloys to neutron irradiation, closely mimicking operational reactor environments. Through advanced characterization methods—such as transmission electron microscopy (TEM), atom probe tomography (APT), and nanoindentation—they captured the nuanced transformations occurring within the alloys on an atomic scale. These analyses provided a granular view of irradiation-induced embrittlement, swelling, and phase stability.

One striking revelation from their findings was how PM-HIP produced alloys exhibited superior radiation tolerance compared to their forged counterparts. The uniform and refined microstructure generated by PM-HIP appeared to restrict defect cluster growth, subsequently limiting radiation-induced hardening and swelling. This effect can be attributed to a higher density of grain boundaries acting as defect sinks—a critical factor in mitigating radiation damage.

In contrast, forged alloys manifested more pronounced irradiation-induced phase segregation and precipitate coarsening. The larger grain sizes and residual stresses inherent to forging seemingly amplified defect accumulation. This phenomenon has deleterious effects on mechanical performance, increasing susceptibility to crack initiation and propagation under operational stressors.

Another fascinating aspect explored was the comparative evolution of the gamma-prime (γ’) phase, a key strengthening phase in Ni-based superalloys. The study detailed how PM-HIP processing results in a more homogenous γ’ distribution, facilitating a stable microstructure even as irradiation introduces energetic defects. The forged alloys, however, showed more significant γ’ coarsening and dissolution, jeopardizing their long-term strength.

The authors pointed out the role of irradiation temperature in modulating damage severity. While neutron flux intensity was kept consistent, variations in testing temperatures revealed differing diffusion kinetics in the alloys. At elevated temperatures, PM-HIP samples maintained microstructural integrity longer, hinting at a critical thermal activation component in defect annealing processes.

In terms of mechanical properties, nanoindentation experiments post-irradiation revealed that PM-HIP alloys retained a higher fraction of their original hardness and modulus values. The forging counterparts suffered more drastic declines, underscoring the enhanced radiation resilience provided by processing route optimization. This directly correlates to potential improvements in structural components exposed to neutron-rich environments.

Perhaps one of the most compelling implications of this research is its potential impact on reactor lifespan and maintenance costs. Using PM-HIP fabricated Ni-based alloys could meaningfully delay the onset of irradiation-induced failure mechanisms. This, in turn, translates into longer service intervals, reducing downtime and enhancing overall operational safety margins.

Furthermore, the study highlights the versatility of PM-HIP in producing complex geometries while retaining superior radiation resistance. This opens avenues not just for power generation but also for aerospace and defense sectors where neutron exposure is a concern. The ability to tailor microstructures and properties via powder metallurgy could redefine the boundaries of material engineering.

Roy and colleagues also addressed the necessity for future investigations into the synergistic effects of thermal aging and irradiation, as well as the influence of alloying elements on radiation tolerance. Their research sets a benchmark, encouraging interdisciplinary efforts to merge computational modeling with empirical work for predictive material design.

The meticulous correction appended to the original publication underscores the authors’ commitment to scientific rigor and transparency, reinforcing the reliability of these groundbreaking insights. The study’s implications resonate beyond academic circles, paving the way for industrial adaptations in manufacturing irradiated-resistant components.

In conclusion, this comparative study underscores how manufacturing choices at the microstructural level profoundly sway the performance of Ni-based alloys under neutron irradiation. The findings advocate for a preferential adoption of PM-HIP techniques to bolster material longevity and safety in the nuclear sector, marking a pivotal advance in materials science.

As the energy landscape evolves, and the demand for robust, radiation-resistant materials escalates, such research not only provides a technical beacon but also paves the pathway towards safer, more efficient nuclear technology. The intersection of advanced manufacturing and irradiation science, as exemplified by this study, embodies the future of resilient engineering materials.

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Subject of Research: Effects of neutron irradiation on Ni-based alloys fabricated by powder metallurgy-hot isostatic pressing (PM-HIP) and forging manufacturing methods.

Article Title: Author Correction: Effects of neutron irradiation on Ni-based alloys: a comparative study between PM-HIP and forging.

Article References:
Roy, R., Mondal, S., Clement, C.D. et al. Author Correction: Effects of neutron irradiation on Ni-based alloys: a comparative study between PM-HIP and forging. npj Adv. Manuf. 3, 25 (2026). https://doi.org/10.1038/s44334-026-00096-7

Image Credits: AI Generated

In a groundbreaking stride toward the future of quantum technology, researchers have unlocked a new mechanism to control quantum emitters embedded in hexagonal boron nitride (hBN), a layered two-dimensional material. This breakthrough could serve as a critical leap forward in integrating quantum emitters into practical devices, bringing quantum computing, secure quantum communications, and ultra-sensitive quantum sensors closer to reality. This promising development emerged from meticulous experimental studies demonstrating the ability to reversibly tune the color and wavelength of light emitted by quantum defects in hBN simply by twisting its atomic layers.

Quantum emitters are nanoscopic sources of light that emit photons with quantum characteristics, pivotal for next-generation technologies reliant on quantum information processing. However, a formidable challenge in utilizing these emitters has been the difficulty in precisely controlling their optical properties post-fabrication. Dr. Angus Gale, the lead scientist behind this research, elucidated how their work provides an innovative “lever” by exploiting the intrinsic layered nature of hBN to adjust the quantum emission properties dynamically. Unlike traditional solid-state hosts such as diamond or silicon carbide, whose structures are rigid and three-dimensional, hBN’s atomically thin layers can be mechanically manipulated, lending itself to an unprecedented degree of tunability.

The team’s experiments demonstrated that by picking up, stacking, twisting, and restacking these ultra-thin hBN slices at varied angles, they could modulate the quantum emitters’ light emission dramatically. These shifts in wavelength were not minor perturbations but substantial changes far exceeding what has been typically achievable in similar solid-state quantum systems. This finding challenges the traditional paradigm which often seeks to stabilize quantum defect behavior within rigid host materials. Instead, the researchers embraced the inherent flexibility and twistability of hBN to realize a new class of quantum control.

Professor Igor Aharonovich, the supervising author, emphasized the broader implications of this twist-controlled modulation approach. He noted that coupling two-dimensional materials at specific twist angles opens up an entirely fresh set of physical phenomena that were previously inaccessible. By assembling layers with precision angular misalignment, new quantum states can emerge, potentially transforming the landscape of material engineering for quantum applications. This strategic layering and twisting, known as “twistronics,” has already inspired revolutionary advances in graphene-based systems, yet this study extends the concept into the realm of quantum emitters embedded in hBN.

One key advantage of using hBN is that its layered crystal structure allows researchers to systematically alter the interaction between layers, effectively tuning the electronic environment surrounding the defects responsible for quantum light emission. As Dr. Gale metaphorically illustrated, far from being a solid block, hBN behaves more like a stack of cheese slices. Just as peeling and recombining slices of cheese can change how flavors intermingle, twisting hBN slices changes the interaction between atomic planes, which in turn alters quantum emitter characteristics. This mechanical engineering at the nanoscale provides a versatile platform for on-demand tuning of quantum light sources.

This technology breakthrough is fundamentally experimental but holds widespread implications. Adjustable quantum emitters could accelerate the development of quantum computing hardware, where precise photon control is essential for encoding and manipulating qubits. Similarly, secure quantum communication protocols rely on tailored quantum light sources to guarantee the generation and distribution of entangled photons immune to interception. Moreover, quantum sensing that exploits the extreme sensitivity of quantum states to environmental changes stands to benefit significantly from tunable quantum emitters, pushing the boundaries of detection limits in fields ranging from medicine to navigation.

Another remarkable facet of the research is the ability to repeatedly pick up, twist, and restack hBN layers without degrading the quantum emitter properties. This reversibility enables iterative fine-tuning of device characteristics and might yield adaptable quantum systems reconfigurable post-production, a feature highly desirable from a technological deployment perspective. This contrasts sharply with permanent structural modifications used in conventional quantum emitter fabrication, which restrict adjustable control and device flexibility.

The team’s methodology involved sophisticated nanofabrication techniques and high-resolution optical spectroscopy to probe the quantum emitters’ response to twist-induced modifications. By correlating the angular misalignment with spectral shifts in emitted light, they constructed a detailed understanding of the underlying physics governing emission control. These insights provide a blueprint for engineering tailored quantum emitters with bespoke characteristics suitable for specific applications.

The research findings have been detailed in a paper published in the eminent journal Science Advances, signaling the scientific community’s enthusiasm for the potential unlocked by twistable quantum systems. Importantly, the authors have declared no competing interests, affirming the independent and foundational nature of this inquiry. Funded by the Australian Research Council and the Air Force Office of Scientific Research, the work highlights the vibrant collaboration between academic institutions and funding bodies committed to propelling quantum technologies.

This discovery heralds a new era in quantum material science, where layered two-dimensional crystals are no longer passive hosts but active tunable platforms. By harnessing the mechanical and electronic versatility of materials like hBN, researchers are gradually unlocking complex quantum phenomena with precision control previously deemed unachievable. The implications extend well beyond fundamental research, marking exciting progress toward quantum devices that could redefine computational power, information security, and sensory precision globally.

In summary, the twist-controlled modulation of quantum emitters in hexagonal boron nitride exemplifies the convergence of cutting-edge experimental physics and materials science innovation. By capitalizing on the unique layered architecture of hBN and the emerging discipline of twistronics, this research pioneers functional tunability in quantum light sources. As the scientific community continues to develop and refine these approaches, we edge closer to the practical implementation of quantum technologies that promise profound societal and technological transformation.

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Subject of Research: Not applicable

Article Title: Twist-controlled modulation of quantum emitters in hexagonal boron nitride

News Publication Date: 19-Jun-2026

Web References: 10.1126/sciadv.aec0101

References:

• Gale, A., Aharonovich, I., et al. Twist-controlled modulation of quantum emitters in hexagonal boron nitride. Science Advances. 19 June 2026.

Keywords:
Quantum mechanics, Hexagonal boron nitride, Two-dimensional materials, Quantum emitters, Twistronics, Quantum light sources, Quantum computing, Quantum communication, Quantum sensing, Layered materials, Nanofabrication, Photonic modulation

At the cutting edge of aerospace, energy, and computing, material innovation stands as a critical frontier. Traditionally, companies seeking to enhance the performance and resilience of metals encounter formidable challenges: understanding the intricate behavior of novel materials under real-world conditions often necessitates actual physical synthesis and testing. This step has remained indispensable because even the most sophisticated computational simulations struggle to capture the full complexity of atomic arrangements inherent in many solid materials, particularly metallic alloys. The result is a costly and time-consuming cycle that slows progress in material science and engineering.

A breakthrough has now emerged from a team of researchers at the Massachusetts Institute of Technology (MIT), who have devised a computational framework that promises to transform the way metals are modeled across diverse compositions. Central to this innovation are advanced machine-learning algorithms designed to predict the properties of chemically complex metal alloys with unprecedented accuracy and efficiency. By refining the training datasets used to develop these models, the team has succeeded in encapsulating the vast heterogeneity of atomic environments that characterize disordered metallic materials, thereby enhancing predictive precision.

The research, detailed in a recent publication in the journal Science Advances, offers compelling evidence that machine-learning potentials, when constructed with carefully curated training data, can reliably simulate material properties spanning a broad spectrum of metal alloys and operational conditions. This methodological advancement stands to significantly reduce reliance on expensive experimental validation, enabling faster development cycles and more cost-effective exploration of alloy design spaces.

Rodrigo Freitas, MIT’s TDK Career Development Professor of Materials Science and Engineering and senior author on the paper, emphasizes the versatility of this approach. While the study focuses primarily on metallic alloys—a domain fraught with chemical disorder—the underlying principles and techniques have the potential to be adapted for other classes of materials, including semiconductors and sustainable steels. Freitas envisions wide-ranging applications, from aerospace materials engineered for extreme environments to novel components in energy systems and beyond.

The crux of the challenge in modeling metals lies in capturing the influence of intrinsic chemical disorder on material properties. Two alloys with identical elemental compositions can exhibit starkly different mechanical behaviors depending on their atomic-scale structure; one might be brittle, while another offers impressive ductility. Computational models must therefore operate at the atomic level, simulating interactions between individual atoms to predict macroscopic properties. Over the past twenty years, machine learning has emerged as an indispensable tool for constructing these atomic interaction potentials. However, existing models conventionally assume ordered or near-ordered atomic arrangements, limiting their efficacy when faced with the irregular, heterogeneous atomic environments typical of real-world alloys.

Chemical disorder implies a staggering diversity of local atomic neighborhoods, each subtly unique in terms of bonding patterns and energetic stability. This diversity poses a formidable obstacle for machine learning, which relies heavily on representative training data to generalize effectively. Conventional data-generation approaches involve brute-force sampling, which is computationally prohibitive—often demanding upwards of 100,000 hours of supercomputer time for a single alloy—and lacking in adaptability when alloy compositions shift. Thus, the crux of the innovation lies in constructing training datasets that capture the widest possible range of relevant atomic configurations without redundancy.

Building on prior work where they quantified chemical complexity by analyzing the frequency and distribution of small atomic clusters, Freitas’ group has now developed an information-theoretic approach to optimize training data generation. Through a process of atomic substitutions and iterative refinements, they curate datasets that maximize the diversity of local chemical environments presented to the machine-learning models. This ensures that each training example uniquely contributes to the model’s learning, avoiding the pitfalls of repetitive and uninformative data.

Implemented in this way, the newly trained potentials exhibit marked improvements in predicting metallurgical properties compared to models trained on randomly sampled data or even other sophisticated sampling strategies. The fidelity of these simulations to true chemical bonding dynamics is critical; without it, models risk providing generic insights into material behavior rather than precise predictions applicable to specific alloys and practical conditions. This heightened level of chemical realism opens the door to highly reliable simulations that can stand in place of expensive, time-consuming lab tests.

The team put their methodology through rigorous validation against a variety of metal alloys, assessing the performance of their machine-learning models against industry-standard counterparts developed by tech giants like Google and Microsoft. Remarkably, the MIT-trained models consistently outperformed these much larger, computationally intensive models, demonstrating that thoughtful data curation can eclipse brute-force data volume in enhancing machine learning potentials.

Killian Sheriff, a lead author of the paper and a PhD candidate at MIT, spearheaded extensive testing across alloy systems and a wide array of material properties, supported by complementary efforts from colleagues Daniel Xiao, Yifan Cao, and University of Sheffield’s Lewis R. Owen, who contributed experimental data for benchmarking. This collaborative effort provided comprehensive evidence that the models could predict phase diagrams—a cornerstone of materials science that chart alloy phase stability across temperatures and compositions—with accuracy rivaling direct experimental observations.

Phase diagrams are particularly important because they inform practical metallurgical processes like welding, casting, and heat treatment. Accurately capturing the subtle energetic preferences for different atomic arrangements within alloys is essential to forecasting phase transformations and resultant material properties under various conditions. The models’ ability to reveal these “subtle energetic biases” is a testament to the deep chemical insight that machine-learning potentials can now attain.

Beyond phase stability, the researchers are actively deploying their technique to predict mechanical resilience and radiation damage tolerance in alloys—a critical consideration for materials operating in extreme environments such as nuclear reactors and aerospace applications. Their goal is to design alloys that maintain strength and resist degradation under stressors that include high temperatures, intense radiation, and mechanical loads.

A key element of this initiative is harmonizing these advanced computational tools with existing industrial workflows and engineering software. Freitas underscores the necessity of integrating these innovations seamlessly into current decision-making frameworks if they are to foster widespread adoption in materials development pipelines. This pragmatic orientation aims to ensure that the profound scientific advances translate swiftly and effectively into tangible industrial benefits.

The work has garnered support from the U.S. Air Force Office of Scientific Research, reflecting its strategic significance for advanced materials in defense and aerospace contexts where performance margins are tight and failure costs are high. As industries seek materials that combine unmatched performance, sustainability, and economic viability, the MIT team’s contribution could represent a paradigm shift—linking detailed atomic-level understanding with scalable, robust material design accelerated by intelligent data-driven simulations.

By dismantling the bottleneck of excessive computational costs and enabling high-fidelity modeling of chemically complex alloys, this research charts a path toward rapid, informed innovation in materials science. With this toolkit in hand, scientists and engineers can explore uncharted compositional spaces more confidently, accelerating progress toward next-generation metals engineered for the demands of the future.

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Subject of Research: Machine-learning models for atomic-level simulations of chemically disordered metal alloys

Article Title: Machine learning potentials for modeling alloys across compositions

News Publication Date: 19-Jun-2026

Web References: DOI: 10.1126/sciadv.aea9951

References: Science Advances, 2026

Image Credits: Not provided

Keywords

Materials science, Material properties, Metals, Alloys, Chemical disorder, Machine learning, Atomic simulations, Phase diagrams, Materials engineering, Computational materials science, Artificial intelligence, Chemistry

Almost everyone has experienced the frustration of trying to get ketchup out of a bottle. At first, nothing happens. The ketchup seems almost solid and refuses to move. Then, suddenly, it starts flowing, sometimes too quickly and all at once. Scientists call this surprising change in behavior “yielding.” Yielding happens when a material switches from […]

The post Why ketchup suddenly pours: Scientists discover the hidden physics of soft materials appeared first on Knowridge Science Report.

In a groundbreaking advancement that promises to reshape the landscape of wearable technology and soft robotics, researchers at the Korea Advanced Institute of Science and Technology (KAIST) have developed a pioneering self-powered piezoelectric fiber sensor exhibiting unprecedented stretchability and resilience. This novel device, capable of enduring elongations up to 668%, heralds a new era of flexible sensors that can reliably monitor physiological signals over extended periods without relying on external power sources such as batteries.

Wearable medical devices designed to continuously track vital signs—heart rate, respiration, and joint movement—have long been limited by the mechanical and electrical degradation of their sensing components under repeated deformations. Traditional piezoelectric fiber sensors, which convert mechanical forces into electrical signals, often suffer from diminished performance as their thin electrode layers and fragile piezoelectric materials falter during routine bending and stretching. Overcoming this barrier, the KAIST research team led by Professor Miso Kim has introduced a transformative approach that stabilizes sensor function even after thousands of mechanical cycles.

Central to this breakthrough is the adoption of a meticulously engineered “Hierarchical Resilient Design,” wherein resilience is embedded across multiple structural levels of the sensor. This concept mirrors the elasticity of a rubber band, allowing the piezoelectric fibers to recover their original form and function after extensive stretching. The research team achieved this by dispersing elastic polymer microparticles within the piezoelectric nanofibers, creating a Velcro-like interlocking microstructure that enhances the sensor’s mechanical robustness and self-restorative capabilities.

The interface engineering between the sensor’s electrical components also proved critical. By enhancing the bonding between the piezoelectric layers and the electrodes, the team prevented delamination—a common failure mode under deformation—that typically degrades signal integrity. This strong adhesion at the material interfaces ensures continuous charge transfer and maintains stable electrical outputs even when the sensor is twisted, pressed, or stretched significantly.

One of the most remarkable aspects of the sensor’s design is its coil and knot structures. By coiling the piezoelectric fibers, the researchers capitalized on geometric extensions, enabling the sensor to stretch over six times its initial length without losing sensitivity. Furthermore, knot configurations were tested to validate sensor durability under complex and dynamic mechanical stresses such as sudden impacts or localized pressure, demonstrating stable electrical signals regardless of deformation mode.

Beyond mechanical resilience, the KAIST team integrated artificial intelligence techniques to interpret the electrical outputs from the sensor. Utilizing machine learning algorithms, they could accurately distinguish between distinct mechanical stimuli—such as bending, stretching, and pressing—offering a valuable platform for intuitive and precise biosignal monitoring in real-world environments. This capability opens new horizons for dynamic human-machine interfacing, where nuanced movement detection is critical.

What sets this breakthrough apart is its self-powered nature. Unlike conventional wearable sensors that depend on finite battery life, the piezoelectric polymer fibers harvest mechanical energy directly from body movements, converting it into usable electrical signals. This sustainable mechanism not only eliminates maintenance concerns but also allows for ultra-lightweight, unobtrusive devices suitable for long-term wear.

The implications of this technology extend well beyond healthcare monitoring devices. Electronic skins capable of mimicking human sensory reception, soft robotic actuators with sensitive feedback loops, and next-generation digital health tools could all benefit from the combination of stretchability, mechanical resilience, and stable self-generated electrical outputs demonstrated by this platform. Such multifunctional usability places this development firmly at the forefront of flexible electronics innovation.

Professor Miso Kim emphasized the significance of their approach: “By simultaneously achieving mechanical resilience and electrical reliability through fiber structure design combined with precise electrode interface engineering, we have laid the foundation for wearable devices capable of long-term operation under strenuous conditions,” she remarked. This robust design paradigm, she asserts, will empower a new generation of devices that continuously monitor biosignals with unprecedented accuracy and durability.

Published in the prestigious journal ACS Nano, the team’s research showcases both fundamental materials science and applied engineering excellence. The paper titled “Mechanically and Functionally Resilient Piezoelectric Fiber Coils and Knots for Reliable Self-Powered Sensing” details the synthesis of the nanofiber composites, the meticulous fabrication of coil and knot geometries, and a comprehensive analysis of the sensor’s electromechanical performance over extensive cyclical tests.

The scientific community has lauded this work for addressing long-standing issues in flexible sensor design. By merging nanoscale material innovations with macrostructural strategies, the KAIST team has created a versatile sensing platform that bridges the gap between laboratory prototypes and real-world applications. Their work sets a precedent for future interdisciplinary efforts aimed at wearable electronics that demand both high performance and durability.

Funded by multiple National Research Foundation of Korea initiatives, this research underscores Korea’s growing leadership in advanced materials and smart sensor development. The integration of elastic polymer microparticles within piezoelectric nanofibers, combined with interface science and architectural engineering, exemplifies the kind of holistic approach necessary to push boundaries in next-generation electronics.

Looking ahead, this resilient, self-powered fiber sensor technology could transform how we collect and interpret physiological data in ambulatory settings, enable more sophisticated prosthetics with tactile feedback, and empower soft robots with human-like sensory capabilities. As devices become lighter, more adaptable, and energy-autonomous, the convergence of materials science and artificial intelligence will accelerate innovations that make previously impossible applications a reality.

In conclusion, the KAIST team’s pioneering piezoelectric fiber sensor offers a robust and reliable solution to historic challenges in wearable sensory technology. Its remarkable combination of extreme stretchability, stable electricity generation without batteries, and intelligent signal analysis marks a significant leap forward that is poised to impact multiple sectors, from healthcare to robotics and beyond.

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Subject of Research: Development of highly stretchable, mechanically resilient, self-powered piezoelectric fiber sensors for wearable devices and soft robotics.

Article Title: Mechanically and Functionally Resilient Piezoelectric Fiber Coils and Knots for Reliable Self-Powered Sensing

News Publication Date: June 18, 2026

Web References: https://doi.org/10.1021/acsnano.5c19628

References: Choi, Y. J., Park, J., Nam, J., Sim, G.-D., Kim, M.-G., & Kim, M. (2026). Mechanically and Functionally Resilient Piezoelectric Fiber Coils and Knots for Reliable Self-Powered Sensing. ACS Nano.

Image Credits: KAIST

Keywords

Self-powered sensor, piezoelectric polymer, wearable medical devices, stretchable electronics, fiber coil sensor, interface engineering, mechanical resilience, electrical stability, nanofiber composites, artificial intelligence, biosignal monitoring, flexible sensors.

They used farmed Korean shells instead.