In a research lab at the University of Tokyo, scientists have developed a new kind of glue. It's incredibly strong and highly stretchable, yet it washes away completely with a little alcohol.

Researchers in Thailand have developed a prototype high-performance bicycle tire that replaces conventional nylon fabric reinforcement with natural silk. The team combined laboratory-scale material testing with pilot-scale tire manufacturing. Natural rubber composites were produced by placing a layer of silk or nylon fabric between rubber sheets and bonding the layers using a resorcinol-formaldehyde-latex coating. The selected composite formulation was then used to manufacture full bicycle tires.

A recent study examined a transparent material used in high-impact applications such as helicopter windshields at the molecular level to measure its toughness. Researchers at the University of Illinois Urbana-Champaign and the University of California, Irvine, followed a crack along a grain boundary in magnesium aluminate spinel, or transparent aluminum, adding new data about this unique crystal-structured material. Their paper is published in the Journal of the American Ceramic Society.

Researchers have uncovered compelling evidence about the elusive structure of a high-pressure phase of solid nitrogen. Nitrogen makes up most of Earth’s atmosphere, but under intense pressure and low temperatures, it can form solid phases with unexpectedly complex structures. One of these phases, γ-N2, remained poorly understood for more than 50 years despite repeated experimental [...]

Electrostatic forces enable low-voltage actuation without relying on rare earth materials. Nearly every electric motor in modern life, from the fan on your desk to the motor in an electric vehicle, relies on magnetism. For more than a century, magnetic fields have dominated motor design because electrostatic forces were considered too weak for practical machines. [...]

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.

Researchers have uncovered a critical mechanism behind battery failure in solid-state batteries, offering new insights that could help unlock safer, longer-lasting energy storage technologies. Every time a smartphone is charged or an electric vehicle is plugged in, billions of lithium ions move through a battery to store energy. Future devices could perform far better with [...]

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

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While we make batteries based on many different chemistries, nothing has approached the massive scale at which we can produce lithium batteries. That scale makes the economics of lithium-ion batteries hard to compete with. Even if we develop a superior battery technology, it's unclear whether we can get manufacturing costs down quickly enough to compete with the efficiency of the lithium supply chain and manufacturing.

The one thing that could change the dynamics is a supply crunch. While lithium is extremely widespread, lithium that can be extracted economically is a different matter. It's cheapest to extract it from brines, and lithium-rich brines are largely limited to South America. We do obtain some lithium from other sources, but it's considerably more expensive.

In today's issue of Science, however, a research team has identified an energy-efficient means of extracting lithium from rocks. The process they've designed uses far less energy than existing ones, regenerates all its starting chemicals, and produces byproducts that could also be sold.

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Metal-organic framework, (noun, “MEH-tal Or-GAN-ik FRAYM-werk”)

Metal-organic frameworks — or MOFs — are a type of material made of metal– and carbon-based molecules. These molecules are linked together into complex, 3-D shapes.

Imagine a house that’s being built. After laying the foundation, builders construct a skeleton-like wooden frame. This framework consists mostly of empty space. MOFs are structured much the same way. Their molecules assemble into a scaffold-like material. And like that wooden frame, an MOF’s interior is also mostly empty space.

Unlike the holes of a sponge, the empty spaces inside an MOF are not random. Scientists can choose the sizes, shapes and chemistry of these gaps. That’s important. It allows scientists to custom-make MOFs for specific tasks. A very porous MOF may be especially good at sopping up substances. Another MOF with certain chemistry may work like a filter, letting some substances through it but blocking others.

Scientists custom-build MOFs for many different uses. In medicine, MOFs may tote drugs to specific places inside the body for release. Or they might release medicines only under certain conditions.

MOFs may also help manage climate change by absorbing carbon dioxide (CO₂) from the air. These MOFs often contain exposed metal ions. (An ion is an atom that carries an electric charge.) Those metal ions can bind to the oxygen atoms in CO₂ molecules, snatching them out of the atmosphere.

Still other MOFs can pull water from desert air and release it for drinking. Others can filter out harmful wavelengths of sunlight to protect crops. Or they can shield against toxic chemicals. In fact, MOFs have so many uses that they won the 2025 Nobel Prize in chemistry.

In a sentence

Scientists developed metal-organic frameworks (MOFs) that yank pollution from our water sources.

Check out the full list of Scientists Say.

Over 20 million tons of polystyrene plastic are produced annually, yet only a small fraction is recycled worldwide. Current recycling methods consume large amounts of energy and often rely on harsh and toxic chemicals to break the strong molecular chains that make up polystyrene. One possible solution is the use of sulfur, which is an inexpensive byproduct formed when refining crude oil. Its unique chemical structure allows it to break up strong chemical chains in long plastic molecules. Despite its abundance, sulfur has very limited applications, and converting it into more usable forms tends to require a lot of heat, rendering it unused for long periods of time. 

Researchers at the Dalian Institute of Chemical Physics hypothesized that sulfur could help break down polystyrene waste to form more valuable chemicals. To power this reaction, they converted sunlight into heat energy through a process called photothermal conversion. They used this heat to transform polystyrene and sulfur into valuable chemicals like 2,4-diphenylthiophene, or chemical D, and 1,3,5-triphenylbenzene, or chemical T, which are used to make semiconductors and chemical sensors. 

To test this, the team mixed ground polystyrene and sulfur at a molar ratio of 1:0.5 in a glass test tube. They sealed the tube with a balloon and secured it onto an iron stand. Then, they focused sunlight onto the bottom of the tube using a curved mirror. As the mixture heated up, the yellow-white solids gradually melted and transformed into a reddish-black liquid after 2 minutes. After heating, the researchers removed the mirror and allowed the system to cool before collecting the gaseous products from the balloon and dissolving the remaining solids for further purification and analysis. 

The researchers then adjusted the reaction conditions to understand what factors influenced their results. They tested the reaction without sulfur, varied the sulfur ratios from 0.2 to 0.8, and replaced elemental sulfur with other sulfur-containing compounds. They also explored adding known photothermal agents, specifically metal oxide additives, to the mixture. 

To compare the difference between sunlight and artificial light, the researchers repeated the experiment indoors using a 100 Watt LED bulb and monitored temperature changes with a thermal camera. They also ran a control experiment using only polystyrene to check how sulfur affected the yield under LED light. They also tested exposure times from 1 to 6 minutes in 1-minute increments to determine how long it took to achieve the highest yields under LED. The researchers used these tests to identify which conditions were necessary for the reaction to occur and how different factors influenced its outcome.

They found that without sulfur or with alternative sulfur-containing compounds, the reaction did not produce chemical D or T under sunlight. In contrast, reactions that included sulfur successfully produced these target products, with the highest yields of 34% for D and 16% for T at a sulfur ratio of 0.5. When they added metal oxides, the chemical yields decreased to 22% and 12%, respectively, suggesting that these additives interfered with the desired reactions. In addition, when the researchers switched from sunlight to LED, the reaction yields dropped to 26% for D and 13% for T. 

Next, they examined how reaction time influenced product formation. They found that yields increased gradually before reaching the maximum at 4 minutes and leveling off. They also noted that mixtures containing sulfur heated up from room temperature to 320°C (608°F), while the control setup only showed a slight temperature increase. The researchers interpreted these results as confirmation of sulfur’s dual role as a reactant and a light-to-heat converter that enables the conversion of polystyrene to useful chemicals.

Taking it a step further, the researchers tested their method on real-world polystyrene wastes, including food packaging, cup lids, and foamed plastics. They successfully produced chemicals D and T from these materials, demonstrating that their process works beyond laboratory samples.

The team concluded that their study presents a simple, fast, and solvent-free approach to converting 2 abundant waste materials into valuable chemicals using sunlight. By combining polystyrene waste and excess sulfur, the researchers offer a new pathway for sustainable polymer upcycling that uses clean energy and is broadly applicable to everyday plastics.

The post Upcycling polystyrene with sunlight and sulfur appeared first on Sciworthy.