Snails are well-known for their mucus. The slime is highly valued as an anti-inflammatory and anti-aging ingredient in certain skin care products, and snails can secrete different kinds of mucus with properties tailored to specific functions. A team of German scientists has determined a recipe for how snails achieve that so-called tunability: Collagen and calcium work in tandem to achieve the varying mechanical properties, according to a new paper in the journal Science.
Snail mucus is mostly water; it's the mucins (glycoproteins) and complex carbohydrates that give it that slimy, viscous texture. In addition to cosmetics, the mucus holds great potential for drug delivery. In 2020, scientists found that combining snail slime from a common garden snail with gold nanoparticles helped accelerate wound healing and exhibited anti-inflammatory properties in mice. Most relevant to this latest paper is a 2023 study on garden snail slime that identified three distinct types of secreted mucus: a hydrating version to protect its skin, one that acts as an adhesive glue, and a third that serves as a lubricant so the snail can more easily glide across a surface.
The team focused their research efforts on the grove snail/lemon snail (Cepaea nemoralis), which they deemed well-suited as subjects because the snails secrete five different kinds of mucus with distinct functions. As with the garden snails, there is one that acts as a lubricant to help with locomotion and an adhesive mucus to help the snail stick to various surfaces. Then there's a mucus layer known as the epiphragm that the snail secretes when it hibernates in winter, which contains calcite and effectively seals the shell to protect the snail from predators and harsh environmental conditions.
© Max Planck Institute of Colloids and Interfaces
Snails are well-known for their mucus. The slime is highly valued as an anti-inflammatory and anti-aging ingredient in certain skin care products, and snails can secrete different kinds of mucus with properties tailored to specific functions. A team of German scientists has determined a recipe for how snails achieve that so-called tunability: Collagen and calcium work in tandem to achieve the varying mechanical properties, according to a new paper in the journal Science.
Snail mucus is mostly water; it's the mucins (glycoproteins) and complex carbohydrates that give it that slimy, viscous texture. In addition to cosmetics, the mucus holds great potential for drug delivery. In 2020, scientists found that combining snail slime from a common garden snail with gold nanoparticles helped accelerate wound healing and exhibited anti-inflammatory properties in mice. Most relevant to this latest paper is a 2023 study on garden snail slime that identified three distinct types of secreted mucus: a hydrating version to protect its skin, one that acts as an adhesive glue, and a third that serves as a lubricant so the snail can more easily glide across a surface.
The team focused their research efforts on the grove snail/lemon snail (Cepaea nemoralis), which they deemed well-suited as subjects because the snails secrete five different kinds of mucus with distinct functions. As with the garden snails, there is one that acts as a lubricant to help with locomotion and an adhesive mucus to help the snail stick to various surfaces. Then there's a mucus layer known as the epiphragm that the snail secretes when it hibernates in winter, which contains calcite and effectively seals the shell to protect the snail from predators and harsh environmental conditions.
© Max Planck Institute of Colloids and Interfaces
Human eyes don’t register the infrared portion of the light spectrum because infrared photons don't carry enough energy to trigger the signaling pathway inside our light-sensing cells. But we’ve been able to make devices that give us a visual representation of what’s happening in the infrared.
A team at the Beijing Institute of Technology, led by Xin Tang and Ge Mu, has now built a device that lets people see infrared in a new way. Instead of just translating it to visible shades of green as it’s done in standard night-vision goggles, it translates different infrared wavelengths into distinct parts of the visual spectrum, giving the eye something closer to natural vision.
Researchers achieved that by combining mercury telluride colloidal quantum dots, which absorb infrared light, and a dual-layer OLED, which converts that absorbed energy into visible color. Stacked together with the right internal wiring, they make incoming infrared radiation come out the other side as an ordinary-looking, full-color image.
© Dmitri Toms
Human eyes don’t register the infrared portion of the light spectrum because infrared photons don't carry enough energy to trigger the signaling pathway inside our light-sensing cells. But we’ve been able to make devices that give us a visual representation of what’s happening in the infrared.
A team at the Beijing Institute of Technology, led by Xin Tang and Ge Mu, has now built a device that lets people see infrared in a new way. Instead of just translating it to visible shades of green as it’s done in standard night-vision goggles, it translates different infrared wavelengths into distinct parts of the visual spectrum, giving the eye something closer to natural vision.
Researchers achieved that by combining mercury telluride colloidal quantum dots, which absorb infrared light, and a dual-layer OLED, which converts that absorbed energy into visible color. Stacked together with the right internal wiring, they make incoming infrared radiation come out the other side as an ordinary-looking, full-color image.
© Dmitri Toms
A microscopic grain preserved an atomic structure created inside Hiroshima’s fireball.Study suggests a new method for tracking electronic and vibrational dynamics in real time. Within a few trillionths of a second after absorbing light, a molecule bends out of shape, shifts its electrons, and converts electronic energy into vibrations. These changes happen too quickly for ordinary instruments to follow, but flashes from the European XFEL [...]A new 3D material can guide heat around complex objects while shielding them from extreme temperatures and infrared detection. On an infrared camera, a warm object normally stands out clearly against cooler surroundings. Researchers have now built a three-dimensional cloak that bends heat around an object, erasing its thermal outline while protecting the space inside [...]Researchers are turning toxic, centuries-old ammunition into highly efficient and cheap solar panels.A predatory sea worm known as Perinereis cultrifera possesses jaws that do not fit neatly into either biological or metallic categories. These jaws consist of structural proteins reinforced with metal ions, giving them mechanical properties unlike those of either conventional metals or ordinary biological tissue.
This unique combination of composition and mechanical behavior led researchers from TU Wien and the University of Vienna to propose an entirely new material classification. In a recent paper published in Biophysics Reviews, the team suggests that the creature’s jaws represent a distinct class of material, which they term bio-metal.
The jaws of bristle worms such as P. cultrifera are formed by linking structural proteins with metal ions, producing a material tough enough to crush and bite prey. Previous studies called these tissues metallike biomaterials, meaning they share some properties with metals, like strength or conductivity. The new study argues that this term is too broad. According to the researchers, a true bio-metal must meet three criteria: it must be hard, display specific strain mechanics, and have a well-defined ion-protein structure.
To test if the jaw met these criteria, the researchers used nanoindentation, which means pressing a tiny probe into the tissue to measure its properties. They also conducted chemical tests and imaging at the same time. The results confirmed earlier findings that the jaw tips contain higher concentrations of metal ions than the center, likely making the tips harder.
When the probe pressed into the jaw at different depths, the researchers observed something called the Nix-Gao size effect. This effect, seen before in metals like copper and silver, means that smaller areas resist deformation more than larger ones. The reason is that strain gradients become larger in smaller regions, increasing resistance to deformation. Finding this metal-like behavior in worm jaws is one reason scientists have compared the material to metals.
However, the jaw does not behave exactly like metals such as copper. Its elasticity changes depending on the size of the area tested, which is not seen in crystalline metals.
“Bristle worm jaws also showed size-dependent elasticity — this is a distinguishing feature of bio-metals when compared to standard crystalline metals like copper or silver,” said Christian Hellmich, one of the paper’s authors. Copper and silver can show a hardness size effect, but neither exhibits the size-dependent elasticity that the team measured in the worm jaw. That distinction is one reason the researchers argue that these materials deserve their own category.
To explain this property, the researchers built a mathematical model showing how the jaw’s atomic structure leads to the elastic size effect. This model helps explain the effect and gives a basis for future research.
“We plan to extend the experimental database by investigating additional species to refine the theoretical concept and perform dedicated computations, and — perhaps most interestingly — to explore the link between genetic interventions and the corresponding material design space,” Hellmich said. “All this comes with true excitement about the beauty, elegance, and refinement found in and produced by nature.”
Next, the researchers plan to explore how genetic changes influence the development of these materials. If successful, the work could eventually help engineers develop new biomimetic materials inspired by the ion-protein architecture found in these worms.
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.
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.
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
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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