Researchers have developed a visible-light-driven method that could help chemists build more complex drug-like molecules in fewer steps. A blue light commonly used in aquariums and indoor gardens could help chemists solve one of drug discovery’s persistent challenges: building more intricate molecules without adding a long series of costly reactions. Many medicines are based on [...]
Scientists have taken another important step toward making next-generation solar panels more powerful and longer lasting. An international team of researchers has developed a new chemical treatment that helps perovskite solar cells convert sunlight into electricity more efficiently while also improving their durability. The breakthrough allowed the experimental solar cells to reach a power conversion […]
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Knitted fabrics are usually associated with warm sweaters, scarves, and blankets. But researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have shown that knitting can do much more than keep people warm. They have created a new type of smart fabric that can change shape, act like a switch, […]
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Humans have been around for only about 0.01% of Earth’s history, yet we’ve altered natural landscapes and processes that have remained stable for millions of years. A team of researchers from the University of Glasgow recently studied a formerly sandy beach in the UK that highlights how fast these changes can occur.
Industrial steel and iron furnaces operated extensively in the UK from 1856 until the 1980s. During these decades, workers dumped roughly 335 million cubic meters (90 billion gallons) of molten furnace waste, called slag, across the country. Of that, roughly 27 million cubic meters (6 million gallons) now sits on the coast of Derwent Howe in a massive 30-meter (100-foot), solidified cliff.
The researchers analyzed this region to understand how man-made, or anthropogenic, materials influence the way rocks form. Rocks naturally form and change into different types of rocks over million-year timescales, through a continuous process called the rock cycle. When the team uncovered a soda can tab manufactured in 1989 within the hardened grains, or clasts, of slag, they concluded that the beach had solidified within the last 35 years. Based on this discovery, they proposed a new term for rocks formed from anthropogenic sediments: the anthropoclastic rock cycle.
The team collected 1,300 grains of sediment from 13 sites in the study area and found that 83% were slag. Natural beach sediments, like quartz sand, comprised only about 17% of the grains. As wind and waves broke down the cliff, slag clasts fell off and became rounded by friction as tides transported them from the cliff to the beach.
At the boundary where the slag touched the beach, the researchers also saw evidence that the slag had seeped into the underlying wet sediment. The rocks there resembled a type of rock called peperite, which forms when lava flows over wet, loose sediments. They concluded that this peperite-like rock formed as the molten slag flowed over the still sandy beach. Slag is much thicker than wet sand, so the resulting rock looked like blobs of slag encased in sandy rock.
The researchers then visually observed how the slag grains were arranged in the solidified rock to reconstruct the tidewater speeds at different points in the coast’s history. The different ways sediments are arranged within a rock are called sedimentary features, and they reflect the processes that formed the rock. For example, as water flows over sand, it forms small, dune-like ripples. When the sand beds harden into rock, the ripples are preserved as features known as trough cross-bedding.
Faster water erases any ripple formations and instead forms horizontal layers in the sediment, known as planar lamination. The researchers observed both trough cross-bedding and planar laminations preserved in the slag beach rocks. These sedimentary features indicate that the beach at Derwent Howe turned from loose sediment into rock in a generation.
To investigate why this beach rapidly solidified, the researchers scanned the surfaces of the beach rock with electron beams and X-rays using a scanning electron microscope, or SEM, equipped with an energy dispersive X-ray, or EDX. Every chemical element emits a unique set of X-rays with different wavelengths when hit with EDX, so the researchers could compare the X-rays from their samples to databases of known mineral X-ray signatures. They identified the primary binding agents as calcium- and iron-rich minerals like calcite, goethite, and brucite. These minerals are called cements because they bind grains of sediment together.
The researchers expected the slag to produce these cement compositions, since extracting metal ores from rock enriches the resultant slag in calcium, magnesium, manganese, and iron, which readily dissolve in water. When water flowing between closely packed slag grains can no longer hold additional dissolved elements, those elements precipitate as solid mineral cements. Since cements only form when the water has very high concentrations of dissolved elements, the introduction of massive amounts of slag caused the cement to form faster than it would in waters with lower concentrations of elements.
The researchers suspected that the anthropoclastic rock cycle they observed at Derwent Howe is not uncommon. Since slag contains all the elements necessary to form rocks when in contact with seawater and air, slag deposits all over the world could be rapidly influencing their local environments. With this in mind, the team suggested that future researchers update geological models to account for human contributions to the natural rock cycle.
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Scientists have discovered an abundance of molecules in the cosmos, but for the first time ever, they were able to detect one in interstellar space very familiar to us here on Earth: sugar.
The discovery, made in the vast clouds of gas and dust between stars, could help scientists better understand how some of the chemical ingredients necessary for life formed long before Earth existed.
Researchers at the Centro de Astrobiología, part of the Spanish National Research Council (CSIC), identified the sugar molecule erythrulose within the interstellar medium.
Sugars are considered fundamental building blocks of life. On Earth, they play vital biological roles, including forming the backbone of RNA and contributing to the chemistry that ultimately supports DNA, while also serving as an important source of energy. Scientists studying the origins of life have long sought to understand how the first sugars formed, since they are essential ingredients in the molecules associated with life’s earliest development.
“The detection of erythrulose is very exciting because it opens up the possibility of discovering in space other sugars such as ribose, which is part of RNA, and other important molecules for the origin of life,” says Carlos Briones, co-author of the study, in a statement.
Even so, one major question remains: where did the first biologically important sugars originate?
Previous studies have identified sugars such as ribose and glucose in meteorites and asteroid samples, suggesting that some organic compounds may have been delivered to the early Earth from space. Until now, however, no sugar had been directly detected in the cold molecular clouds where stars and planets form.
The newly identified molecule, erythrulose, was found in the molecular cloud G+0.693−0.027 near the center of the Milky Way. On Earth, erythrulose is a naturally occurring four-carbon ketose sugar.
The discovery was made using highly sensitive radio astronomy observations from the Yebes Observatory’s 40-meter radio telescope and the 30-meter telescope operated by the Institute for Radio Astronomy in the Millimeter Range. Researchers analyzed the unique radio signatures, or spectral fingerprints, emitted by molecules in space.
The team identified 12 spectral lines matching laboratory measurements of erythrulose, confirming the molecule’s presence.
The findings also challenge some existing ideas about how complex molecules form in space. Scientists have generally proposed that interstellar chemistry builds larger molecules by gradually adding carbon atoms. Yet erythrulose proved to be more abundant than related three-carbon sugars that researchers expected would appear first.
“This finding was unexpected, as the prevailing view in astrochemistry is that interstellar molecules grow in size through the sequential addition of carbon atoms,” said Izaskun Jimenez Serra (CAB), the lead author of the recent study.
Instead, the researchers found that erythrulose could form inside icy dust grains from simpler two-carbon alcohols and aldehydes. These frozen environments may function as natural chemical laboratories, allowing increasingly complex organic molecules to develop over time.
The team estimates that during the Late Heavy Bombardment—roughly 4.1 to 3.8 billion years ago, when Earth experienced intense asteroid and comet impacts—between 500,000 and 50 million tonnes of erythrulose may have been delivered to the planet’s surface.
The discovery adds another piece to the puzzle of life’s origins, suggesting that some of its essential ingredients may have arrived with something surprisingly sweet.
This paper was published in the journal Nature Astronomy.
Chrissy Newton is a PR professional and the founder of VOCAB Communications. She currently appears on The Discovery Channel and Max and hosts the Rebelliously Curious podcast, which can be found on YouTube and on all audio podcast streaming platforms. Follow her on X: @ChrissyNewton, Instagram: @BeingChrissyNewton, and chrissynewton.com. To contact Chrissy with a story, please email chrissy @ thedebrief.org.
The discovery hints that life’s ingredients may form before planets exist.
Fluoride is an electrically charged form of the element fluorine.
An atom that carries such an overall charge is called an ion. Fluoride is a negative ion with a -1 charge. That’s because each fluoride ion contains nine (positively charged) protons and 10 (negatively charged) electrons.
Fluoride strengthens the enamel that makes up the outer layer of our teeth. That makes it an important component of toothpastes and other dental products. Many places also add fluoride to their drinking water. They don’t add much. The recommended level is just three drops of fluoride per 200 liters of water. But even that amount can reduce dental cavities by about 25 percent. That’s according to the U.S. Centers for Disease Control and Prevention (CDC).
In 1945, Grand Rapids, Mich., became the first city to add fluoride to its water. By 2015, about three-quarters of Americans had access to tap water that strengthens teeth. The CDC lists fluoride-treated water as one of the 10 great public health achievements of the 20th century.
Fluoride strengthens enamel thanks to its excellent ability to snag positively charged particles. Compared to other atoms or ions, a fluoride particle is tiny. That means all its negative charge is packed into a small area. In other words, fluoride comes with high charge density. And that allows it to form strong bonds with other particles.
Fluoride binds and holds onto positively charged particles, such as calcium ions. In our mouths, this process forms calcium-based minerals that reinforce enamel. That provides a barrier against acids that would dissolve our teeth.
When fluoride-amended water treatments drop, dental problems rise.
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Fullerenes — a class of molecules composed entirely of interconnected carbon atoms — are the largest molecules definitively detected in space. Researchers using JWST to study a planetary nebula have found new spectral features from a fullerene composed of 60 carbon atoms, offering another way to study how these molecules form and survive in space.
Planetary nebula Tc 1 as seen by JWST. [NASA / ESA / CSA / Western University, J. Cami]
Since that discovery, Tc 1 has been a hot spot for studies of buckyballs. However, researchers are still puzzling over how these large molecules form and survive in harsh space environments.
A closeup of the center of Tc 1, showing the region observed with JWST’s Mid-Infrared Instrument (black-to-yellow color scale) and Near-Infrared Spectrograph (white outline). The background color scale shows the nebula’s H-alpha emission as seen by the Very Large Telescope. [Adapted from Giese et al. 2026]
Morgan Giese (The University of Western Ontario) and collaborators recently reported on the results of a JWST observing program that aimed to understand how fullerenes respond to changes in their environment. The team mapped the distribution of C60 molecules at varying distances from the powerful ionizing radiation of Tc 1’s central star.
The team successfully spotted the emission features from the fullerene C60 seen in earlier observations, but they also turned up something unexpected: a handful of prominent emission features from 3.5 to 5.2 microns that had not been reported previously.
What’s the source of these emission features? The distribution of the emission provides a strong clue: known spectral features from fullerenes C60 and C70 are concentrated in a narrow, bright ring around the center of the nebula, with diffuse emission suffusing the rest of the nebula — and the newfound spectral features follow the same distribution.
JWST spectrum of Tc 1 (black line) extracted from the area indicated by a black circle in the previous image. The orange and blue dashed lines show fits to the newfound emission bands, and the results of the anharmonic calculations are shown as the teal line below the JWST spectrum. Click to enlarge. [Giese et al. 2026]
Giese and coauthors found that 17% of the energy emitted by the C60 molecules in Tc 1 is released in these combination bands, suggesting that these features must be taken into account when modeling the cooling of C60 molecules. The discovery of new fullerene spectral features also has implications for future studies of these molecules: since these features appear in a wavelength range where features from other complex molecules like polycyclic aromatic hydrocarbons are weak, these newly identified bands provide a promising way to spot and study fullerenes across space environments.
“Detection of C60 Combination Bands in the Near-IR Spectrum of Tc 1,” Morgan M. Giese et al 2026 ApJL 1004 L32. doi:10.3847/2041-8213/ae76d5
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Sugars are key biomolecules in living organisms, as they form the backbone of DNA and RNA and play a fundamental role in metabolic processes. In theories of the origin of life, sugars are also essential for the synthesis of the first nucleic acids. Despite their importance, one of the major questions in origin-of-life research is how the first sugars formed on Earth, since laboratory experiments show that they do not form in enough quantities under prebiotic conditions. Sugars such as ribose and glucose have previously been detected in meteorite and asteroid samples, suggesting that some of these molecules may have originated in the primordial molecular cloud from which our Solar System formed. However, until now, no sugar had ever been directly detected in the interstellar medium.
An international team led by Centro de Astrobiología (CAB, CSIC-INTA) researcher Izaskun Jiménez-Serra has now identified 1 the first sugar in interstellar space: erythrulose. This molecule is the only possible four-carbon ketose, and on Earth it is commonly found in raspberries and sunless tanning products. Erythrulose was detected toward the molecular cloud G+0.693−0.027, located near the centre of our Galaxy, the Milky Way. The discovery was made possible by ultra-sensitive, broadband spectroscopic surveys carried out with the 40-m Yebes radio telescope and the 30-m telescope of the Institute for Radio Astronomy in the Millimeter Range (IRAM).
The team identified 12 spectral lines matching the laboratory spectrum of erythrulose measured at the University of the Basque Country. The study also shows that this sugar is at least eight times more abundant than similar three-carbon sugars, none of which were detected in the same region. “This finding was unexpected, as the prevailing view in astrochemistry is that interstellar molecules grow in size through the sequential addition of carbon atoms”, says Izaskun Jimenez-Serra.
Working in collaboration with chemists from the University of Extremadura and Radboud University (the Netherlands), the CAB team discovered that erythrulose can form within interstellar ices from simpler two-carbon alcohols and aldehydes.
Based on the abundance of erythrulose measured in the G+0.693−0.027 molecular cloud, the researchers estimate that between 0.5 and 50 million tonnes of this sugar could have reached Earth’s surface during the Late Heavy Bombardment, which occurred approximately 4.1 to 3.8 billion years ago. The presence of erythrulose in interstellar space therefore provides an alternative source of sugars that may have contributed to the emergence of the first metabolic and replication processes on the early Earth.
“The detection of erythrulose is very exciting because it opens up the possibility of discovering in space other sugars such as ribose, which is part of RNA, and other important molecules for the origin of life,” says Carlos Briones, co-author of the study.
The post Sugar in interstellar space appeared first on Mapping Ignorance.
Enzymes and other biomolecules are often anchored onto solid materials so they can be reused, purified more easily, and made more stable, a strategy biotechnology has relied on since industrial-scale immobilized enzymes first appeared in chemical manufacturing in the mid-twentieth century. Immobilized enzymes today drive processes from food and pharmaceutical production to biosensors and medical diagnostics. Fixing a biomolecule in place, however, is not as simple as gluing it down. The exact spot where it attaches, the direction it faces, and how much freedom it retains to move can all change how well it works. Testing every possible arrangement in the laboratory is slow and costly, which is why computer simulation has become such a valuable complement to experiments.
One of the main tools for this kind of study is molecular dynamics simulation, which calculates how atoms move over time by applying the basic laws of physics. Simulating every single atom of a protein, its surrounding water, and a supporting material gives rich detail, but is extremely demanding on computing power, especially when many attachment geometries need comparing. A widely used shortcut is coarse-grained modeling, in which small clusters of atoms are grouped into single, simplified particles. This keeps the essential physical behavior of the system while making the calculations far cheaper. The most established coarse-grained approach for biological molecules is the Martini force field, first developed in 2004 to describe fatty, membrane-forming molecules and later extended to proteins and, more recently, to a more accurate third generation of the model. Martini has since become a standard tool for simulating large biological assemblies that would otherwise be out of reach.
A team of researchers now introduces MartiniSurf 1, an open-source software package that automates the preparation of these coarse-grained simulations for biomolecules attached to surfaces. Building such a system by hand normally involves several separate programs and many manual steps, which makes it hard to reproduce results or compare different setups fairly. MartiniSurf instead combines the whole process into a single, streamlined workflow. Starting from the structure of a protein or a DNA strand, it converts the molecule into its coarse-grained form, builds a model of the supporting surface, positions the molecule in a chosen orientation, adds water and dissolved ions, and produces all the files needed to run the simulation with GROMACS, a molecular dynamics program that has been developed and refined for decades. Because every step is controlled by explicit settings rather than manual adjustments, two researchers using the same settings should obtain the same starting system, making comparisons across studies far more reliable.
A central feature of the software is its flexibility in representing how a biomolecule is attached. In one mode, selected regions of the molecule are held near the surface through simple restraining forces, without modeling the physical connector itself. In another mode, an explicit molecular linker is placed between the biomolecule and the surface, so that the linker’s own length and flexibility can influence the simulation. Because the properties of such linkers are known to affect how active and stable an attached enzyme remains, this second option offers a more realistic picture whenever the necessary coarse-grained description of the linker is available.
The supporting surfaces themselves can also be varied widely. The software can generate simple flat reference surfaces or draw on previously developed coarse-grained models of real materials such as graphene, graphite, carbon nanotubes, and agarose-like gels, the type of porous material commonly used to immobilize enzymes in the laboratory. Properties such as surface chemistry, electric charge, particle spacing, and added chemical groups can all be adjusted. Recent studies have shown that surfaces built without care can cause water molecules near the interface to line up in unrealistic, almost frozen patterns, an artifact rather than genuine physical behavior. The software includes options that let users introduce controlled irregularity into the surface to reduce this problem, while staying consistent with the wider Martini modeling framework.
To demonstrate how the approach works in practice, the study presents examples involving both proteins and DNA. For an enzyme attached to an agarose-like surface, the software reproduces several different attachment arrangements, including a complete system containing the enzyme together with its natural helper molecule and reaction substrate. These examples show how changing the attachment point or adding chemical groups to the surface can be explored systematically, without needing to reconstruct every model from scratch by hand.
The approach is also extended to DNA tethered to a graphene-like surface, comparing an electrically neutral surface with a positively charged one. The coarse-grained simulations reproduce the same general behavior previously reported in much more computationally demanding all-atom studies: the DNA strand remains mostly upright above the neutral surface but bends progressively toward the surface when it carries a positive charge, drawn in by electrostatic attraction. This comparison is not meant to reproduce exact experimental values, but rather to confirm that the automated setup captures the right physical trends, providing a check on the reliability of the workflow.
Every simulation generated by the software automatically records the settings, software versions, and modeling choices used to build it, so that another researcher can repeat or directly compare a given calculation later. Importantly, the software does not introduce new physical models or new force-field parameters of its own. Instead, it draws on already validated Martini tools and organizes them into a consistent, automated pipeline.
Overall, MartiniSurf addresses a practical bottleneck in computational chemistry rather than proposing a new simulation method. By automating the construction of surface-bound protein and DNA systems, it makes coarse-grained molecular dynamics studies faster to set up, easier to reproduce, and simpler to scale across many attachment geometries. This kind of tool should make it easier to systematically study how surface properties, attachment geometry, and molecular flexibility shape the behavior of immobilized biomolecules, supporting the design of better catalysts, biosensors, and other biotechnology applications.
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 Overcoming bottlenecks in bio-interface simulations: The MartiniSurf Approach 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
Image Credits: Southwest Research Institute
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
A team of researchers at Cornell University has uncovered a surprising problem in a promising class of materials known as hybrid perovskites. The discovery could help scientists build better materials for future technologies, including solar cells, energy-harvesting devices, and advanced electronics. Hybrid perovskites have attracted enormous interest in recent years because they are efficient at […]
The post Scientists discover hidden ‘charge trap’ that could slow next-generation solar and electronic materials appeared first on Knowridge Science Report.
An ingredient commonly used in ice cream may help scientists transform ordinary dirt into a new generation of environmentally friendly building materials. Researchers at the University of Colorado Boulder have discovered that sodium alginate, a substance made from seaweed and often used to improve the texture of foods, can make natural materials like clay and […]
The post Ice cream ingredient could help turn dirt into 3D-printed homes appeared first on Knowridge Science Report.
In a groundbreaking advancement poised to revolutionize semiconductor analysis, a team of researchers from the Institute of Science Tokyo has pioneered a tandem neural network (TNN) architecture that dramatically accelerates the inference of vital material parameters from transistor measurements. This novel machine learning framework addresses one of the most vexing challenges in semiconductor physics—the multivalued inverse problem—achieving unprecedented speed and accuracy that eclipses traditional computational methods.
Semiconductor devices underpin nearly every aspect of modern electronics, from smartphones to sophisticated computing systems. The performance and reliability of these devices hinge on intricate material properties such as defect densities, trap states, and charge carrier mobilities. While engineers can rapidly measure transistor behavior through current-voltage characteristics, deciphering the precise material attributes driving this behavior remains an elusive inverse problem. Conventional approaches rely heavily on iterative simulations and optimization techniques, often consuming hours or even days to converge on plausible solutions.
At the heart of the difficulty lies the phenomenon of multivaluedness: distinct combinations of semiconductor material parameters can yield virtually indistinguishable transistor responses. This ambiguity complicates the task of tracing device performance back to its physical origins. The research team recognized that conventional simulation frameworks are ill-equipped to navigate this degeneracy efficiently. Their insight was to harness the power of tandem neural networks, an innovative deep learning configuration capable of simultaenously enforcing physical consistency and mathematical plausibility in parameter estimation.
The tandem neural network developed by the team couples two interconnected models operating in series. The initial inverse model proposes candidate sets of material parameters based on observed transistor behavior. This output then feeds into a forward model pre-trained to reconstruct transistor characteristics from material parameters. By integrating the forward model’s reconstructions into the training loss function of the inverse network, the system effectively teaches itself to generate solutions that not only fit the measurement data but also align with the physical laws governing transistor operation.
Training this sophisticated architecture required an extensive dataset of over 1,000 simulated transistor measurements, specifically focusing on amorphous indium-gallium-zinc oxide (a-IGZO) transistors—a material of significant interest for flexible and transparent electronics. The dataset spanned a parameter space roughly 1,000 times broader than previous studies, encompassing six key variables: defect density, electron trap states, and carrier mobility among them. Astonishingly, the TNN inferred all six parameters from a single transistor current-voltage curve in less than one millisecond, achieving near-perfect accuracy across the range.
Beyond simulations, real-world validation was paramount. The researchers fabricated a series of a-IGZO transistors under five distinct processing conditions in their laboratories. When subjected to the TNN analysis, the model reproduced the measured device characteristics with exceptional fidelity, requiring no parameter tuning or iterative refinement. This immediate, high-precision performance marks a quantum leap compared to traditional simulation-dependent approaches that demand repetitive calculations, often spanning hours or days.
Assistant Professor Keisuke Ide, one of the principal investigators, highlighted the transformative implications of this speedup: “Our tandem neural network achieves a computational acceleration exceeding six orders of magnitude relative to conventional device simulation methods. This means what once took days can now be done almost instantaneously, radically changing the landscape of semiconductor diagnostics and research.”
The implications of this advancement extend well beyond academic curiosity. In industrial manufacturing, instantaneous and ultra-accurate assessment of transistor material properties can serve as an integral part of quality control, allowing defects and performance variants to be detected and addressed on the production line in real-time. This rapid feedback loop could markedly reduce waste and improve yield in semiconductor fabrication facilities.
Moreover, the TNN framework paves the way for autonomous laboratories where artificial intelligence designs, executes, and interprets experiments with minimal human oversight. By swiftly and reliably unraveling complex material-property relationships, AI-driven research tools can accelerate innovation cycles, optimizing device designs and material formulations in ways previously unattainable due to computational bottlenecks.
Interestingly, the tandem neural network architecture is not conceptually limited to semiconductors alone. The researchers underscore its potential applicability across a broad spectrum of inverse problems characterized by multivaluedness, spanning materials science, chemistry, optics, and possibly even biological systems where multiple underlying parameter sets may produce indistinguishable observational data.
The Institute of Science Tokyo, newly established in 2024 through the merger of Tokyo Medical and Dental University and Tokyo Institute of Technology, serves as a nexus for such cutting-edge interdisciplinary research. This project exemplifies the institute’s mission to advance scientific understanding and technology in ways that create tangible value for society.
As the demands for faster, smaller, and more efficient semiconductor components intensify, novel computational tools that can disentangle the complex interplay of material parameters swiftly and accurately become indispensable. The tandem neural network approach represents a critical milestone on this journey, ushering in a new era where machine learning not only supplements but fundamentally transforms physical sciences research.
This rise of AI-powered physics inference is poised to fuel accelerated material discovery, optimized manufacturing, and ultimately, enhanced electronic devices that power tomorrow’s technological landscape. With this tandem neural network, researchers have cracked open the door to solving multivalued inverse problems at blistering speeds, a feat that was once considered beyond reach.
Subject of Research: Semiconductor material characterization using machine learning
Article Title: Tandem Neural Network Rapidly Solves Multivalued Inverse Problems: Application to Oxide-Semiconductor Characterization
News Publication Date: 27-May-2026
Web References: https://doi.org/10.1002/aisy.70437
Image Credits: Institute of Science Tokyo
Artificial intelligence, machine learning, semiconductor analysis, inverse problems, multivaluedness, twin neural networks, material characterization, oxide semiconductors, transistor diagnostics, amorphous indium-gallium-zinc oxide, computational speedup, autonomous laboratories
Astronomers have found that interstellar comet 3I/ATLAS contains an unusually large amount of methanol. Every interstellar object that passes through our solar system offers a rare chance to sample material forged around another star. Now, new observations of comet 3I/ATLAS using the Atacama Large Millimeter/submillimeter Array (ALMA) have revealed a chemical surprise: the comet is [...]
Gold’s pristine shine may depend on an until now hidden atomic rearrangement at its surface.
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.
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