Researchers at the Massachusetts Institute of Technology (MIT), Cambridge, have developed a new way to probe inside an atom’s nucleus, using the atom’s own electrons as “messengers” within a molecule.
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Scientists at Lawrence Livermore National Laboratory (LLNL) now have a new technique in their toolkit for nuclear forensics examinations, which is the art and science of extracting information about the provenance and history of nuclear materials.
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Advancing technologies like batteries, electronics, and pharmaceuticals relies on discovering and verifying new materials, a process traditionally slowed by costly, time-consuming quality checks using specialized instruments. While AI has accelerated material discovery by identifying promising candidates from vast databases, MIT engineers have now developed a new AI tool that streamlines the verification process, potentially reducing delays and costs in materials-driven industries.
In a new study the researchers present “SpectroGen,” a generative AI tool that turbocharges scanning capabilities by serving as a virtual spectrometer. The tool takes in “spectra,” or measurements of a material in one scanning modality, such as infrared, and generates what that material’s spectra would look like if it were scanned in an entirely different modality, such as X-ray. The AI-generated spectral results match, with 99 percent accuracy, the results obtained from physically scanning the material with the new instrument.
Certain spectroscopic modalities reveal specific properties in a material: Infrared reveals a material’s molecular groups, while X-ray diffraction visualizes the material’s crystal structures, and Raman scattering illuminates a material’s molecular vibrations. Each of these properties is essential in gauging a material’s quality and typically requires tedious workflows on multiple expensive and distinct instruments to measure.
With SpectroGen, the researchers envision that a diversity of measurements can be made using a single and cheaper physical scope. For instance, a manufacturing line could carry out quality control of materials by scanning them with a single infrared camera. Those infrared spectra could then be fed into SpectroGen to automatically generate the material’s X-ray spectra, without the factory having to house and operate a separate, often more expensive X-ray-scanning laboratory.
The new AI tool generates spectra in less than one minute, a thousand times faster compared to traditional approaches that can take several hours to days to measure and validate.
“We think that you don’t have to do the physical measurements in all the modalities you need, but perhaps just in a single, simple, and cheap modality,” says study lead Loza Tadesse, assistant professor of mechanical engineering at MIT. “Then you can use SpectroGen to generate the rest. And this could improve productivity, efficiency, and quality of manufacturing.”
The study was led by Tadesse, with former MIT postdoc Yanmin Zhu serving as first author.
Tadesse’s interdisciplinary group at MIT pioneers technologies that advance human and planetary health, developing innovations for applications ranging from rapid disease diagnostics to sustainable agriculture.
“Diagnosing diseases, and material analysis in general, usually involves scanning samples and collecting spectra in different modalities, with different instruments that are bulky and expensive and that you might not all find in one lab,” Tadesse says. “So, we were brainstorming about how to miniaturize all this equipment and how to streamline the experimental pipeline.”
Zhu noted the increasing use of generative AI tools for discovering new materials and drug candidates, and wondered whether AI could also be harnessed to generate spectral data. In other words, could AI act as a virtual spectrometer?
A spectroscope probes a material’s properties by sending light of a certain wavelength into the material. That light causes molecular bonds in the material to vibrate in ways that scatter the light back out to the scope, where the light is recorded as a pattern of waves, or spectra, that can then be read as a signature of the material’s structure.
For AI to generate spectral data, the conventional approach would involve training an algorithm to recognize connections between physical atoms and features in a material, and the spectra they produce. Given the complexity of molecular structures within just one material, Tadesse says such an approach can quickly become intractable.
“Doing this even for just one material is impossible,” she says. “So, we thought, is there another way to interpret spectra?”
The team found an answer with math. They realized that a spectral pattern, which is a sequence of waveforms, can be represented mathematically. For instance, a spectrum that contains a series of bell curves is known as a “Gaussian” distribution, which is associated with a certain mathematical expression, compared to a series of narrower waves, known as a “Lorentzian” distribution, that is described by a separate, distinct algorithm. And as it turns out, for most materials infrared spectra characteristically contain more Lorentzian waveforms, while Raman spectra are more Gaussian, and X-ray spectra is a mix of the two.
Tadesse and Zhu worked this mathematical interpretation of spectral data into an algorithm that they then incorporated into a generative AI model.
“It’s a physics-savvy generative AI that understands what spectra are,” Tadesse says. “And the key novelty is, we interpreted spectra not as how it comes about from chemicals and bonds, but that it is actually math — curves and graphs, which an AI tool can understand and interpret.”
The team demonstrated their SpectroGen AI tool on a large, publicly available dataset of over 6,000 mineral samples. Each sample includes information on the mineral’s properties, such as its elemental composition and crystal structure. Many samples in the dataset also include spectral data in different modalities, such as X-ray, Raman, and infrared. Of these samples, the team fed several hundred to SpectroGen, in a process that trained the AI tool, also known as a neural network, to learn correlations between a mineral’s different spectral modalities. This training enabled SpectroGen to take in spectra of a material in one modality, such as in infrared, and generate what a spectra in a totally different modality, such as X-ray, should look like.
Once they trained the AI tool, the researchers fed SpectroGen spectra from a mineral in the dataset that was not included in the training process. They asked the tool to generate a spectra in a different modality, based on this “new” spectra. The AI-generated spectra, they found, was a close match to the mineral’s real spectra, which was originally recorded by a physical instrument. The researchers carried out similar tests with a number of other minerals and found that the AI tool quickly generated spectra, with 99 percent correlation.
“We can feed spectral data into the network and can get another totally different kind of spectral data, with very high accuracy, in less than a minute,” Zhu says.
The team says that SpectroGen can generate spectra for any type of mineral. In a manufacturing setting, for instance, mineral-based materials that are used to make semiconductors and battery technologies could first be quickly scanned by an infrared laser. The spectra from this infrared scanning could be fed into SpectroGen, which would then generate a spectra in X-ray, which operators or a multiagent AI platform can check to assess the material’s quality.
“I think of it as having an agent or co-pilot, supporting researchers, technicians, pipelines and industry,” Tadesse says. “We plan to customize this for different industries’ needs.”
The team is exploring ways to adapt the AI tool for disease diagnostics, and for agricultural monitoring through an upcoming project funded by Google. Tadesse is also advancing the technology to the field through a new startup and envisions making SpectroGen available for a wide range of sectors, from pharmaceuticals to semiconductors to defense.
For more information: Matter
Image: The circle with the chip symbolizes SpectroGen, with the connecting threads depicting the process of generating a material’s spectrum.
An NSF-funded initiative led by Lehigh University’s Parisa Khodabakhshi is advancing machine learning models that integrate physical laws to accelerate the design of high-performance alloys for aerospace, automotive, and healthcare applications. This effort complements additive manufacturing—also known as 3D printing—which constructs objects layer by layer using materials like metals, polymers, and biomaterials, offering a powerful combination for next-generation engineering solutions.
“This layer-by-layer approach allows for the fabrication of parts with complex geometries that are often difficult, or even impossible, to achieve with conventional manufacturing methods,” says Parisa Khodabakhshi, an assistant professor of Mechanical Engineering and Mechanics at Lehigh University’s P.C. Rossin College of Engineering and Applied Science. “However, the thermomechanical properties of the final additively manufactured parts are influenced by a large number of process parameters, making design optimization particularly challenging.”
Establishing the map between variations in process parameters and the final part’s properties requires several simulations across a wide range of length scales, making the task computationally expensive. “The computational demands of performing all the necessary simulations make it impractical,” says Khodabakhshi. As a result, manufacturers often resort to trial-and-error methods to achieve desired thermal or mechanical properties in the end product.
“However, you cannot fully explore the entire design space that way to find the optimal design, which is why we’re currently not able to utilize the full potential of additive manufacturing.”
Khodabakhshi recently received a three-year, $350,000 grant from the National Science Foundation to develop a computationally efficient model that accurately predicts how additive manufacturing process parameters influence the solidification microstructure, which in turn determines the properties of the final part. Specifically, Khodabakhshi will develop a physics-based, data-driven reduced-order model for predicting microstructure evolution in binary alloy solidification (or when a mixture of two metals changes from liquid to solid).
“For example, say I want a part that has specific thermal properties,” she says. “I don’t know what my process parameters should be to achieve those properties. The simulations that link given process parameters to the resulting solidification microstructure, and consequently the final properties of the built part, are highly nonlinear. We refer to this simulation as the forward map. From there, I can construct the inverse map, which connects desired properties back to the process parameters.” The NSF project focuses on developing a computationally efficient model for the process-structure (PS) relationship.
The ultimate goal is to optimize the manufacturing of additively manufactured parts, which are especially useful in the aerospace, automotive, and healthcare industries. Fields in which confidence in manufacturing is paramount.
Her team’s approach uses a scientific machine learning framework that blends data-driven machine learning algorithms with physical laws.
“As engineers, we don’t want to just train a black-box algorithm,” says Khodabakhshi. “We want to embed physics into the problem to satisfy the governing equations of the physical phenomena so that we’re confident about the output that we receive from the from the algorithm. That’s the difference between conventional machine learning and scientific machine learning.”
For more information: Lehigh University
Image: Parisa Khodabakhshi, assistant professor, mechanical engineering and mechanics
Scientists are working to better understand how components in mixed materials like concrete or underground rock are distributed, which could lead to stronger materials and safer storage of substances like carbon dioxide or nuclear waste. A key tool in this effort is the Poisson model, which randomly divides space using flat surfaces to simulate how materials mix. Although useful in fields like radiation transport, the model lacked precise formulas to describe relationships between multiple points—limiting its accuracy and application in complex systems until recent breakthroughs addressed this gap.
In a new study, Stanford researchers introduced a clever math method that helps reveal what a material is made of, just by knowing details from one random spot. They used a well-known statistical model, called the Poisson model, which randomly divides space, to study materials such as sand and concrete. This new approach enables scientists to understand the tiny structure of these materials with very high accuracy, which could aid in designing stronger and more reliable materials.
Lead study author Alec Shelley said, “With this study, we’ve solved the famous Poisson model for heterogenous materials.”
“Our result could have a broad impact on several areas of science, because heterogenous materials are common and their models almost never have exact solutions.”
The tiny structure inside materials affects how strong, durable, and useful they are. Thanks to the new research, scientists can now understand these structures more precisely.
“What Alec has succeeded in doing in this study is quite remarkable,” said Daniel Tartakovsky, a professor of energy science and engineering. “Using his approach, you could design a composite material to your specifications and obtain certain properties based on the proper mixture of components.”
Shelley and Tartakovsky plan to use their new math method to predict what different materials are made of. Their model can reveal a long list of important properties that depend on a material’s tiny inner structure, such as hardness and elasticity, tensile strength (how much it can stretch before breaking), electrical and heat conductivity, how fast one substance moves through another, magnetic behavior, and how much light passes through.
Concrete has tiny air pockets inside it. If engineers can accurately model these spaces, they could use materials like fly ash, slag, or biochar to fill them in. This would reduce the amount of cement needed, helping to lower carbon dioxide emissions from cement production, while also making the concrete stronger and more affordable.
Additional applications include modeling fractured and porous media, a central challenge in groundwater management, as well as in nuclear waste disposal, geothermal energy, and carbon sequestration.
“These systems are complex and difficult to model,” said Tartakovsky. “However, the Poisson model’s multipoint functions that we solve in this study offer a new tool for understanding and predicting their behavior.”
In this way, as a microstructural model, the Poisson model can accurately simulate a wide range of heterogenous materials, including everything from the appearance and distribution of ice fragments on a frozen lake to the marbling in a juicy steak.
Shelley shared a neat way to understand the Poisson model. Imagine taking a blank piece of paper and randomly drawing lines across it to create different sections. Then, color each section however you want, it’s like creating a colorful mosaic! The new research takes this idea a step further by picturing another piece of paper laid over that mosaic.
If you poke a hole in the top sheet, you see one color below. That small peek gives a clue about the whole pattern. By making more holes and using a math method called multipoint correlations, you can predict the full design more accurately each time. This approach mirrors how scientists study heterogeneous materials, such as concrete or rock, by using small samples to understand the bigger picture.
“It’s like we’ve created the perfect Battleship player for guessing colors in this model,” Shelley said.
To handle the intricate math involved in the Poisson model’s multipoint correlations, Shelley took a hands-on approach. He began by sketching ideas in a notebook to help picture the problem. Figuring out two points was simple, but things got complicated fast, by the time he worked on three points, he had to deal with 128 different terms.
By the time he reached the four-point scenarios, the complexity was overwhelming, pushing him to turn to computer simulations. It was a necessary shift that saved him from spending months buried in calculations. on manual work.
According to Shelley, the seemingly painstaking work was anything but. “I love math, and I was a math double major in undergrad, so I had the knowledge to go in and try this problem out,” he said.
For more information: Physical Review Letters
Scientists have created an ultra-thin, paper-like LED that emits a warm, sunlike glow by precisely blending red, yellow-green, and blue quantum dots to mimic natural sunlight. This breakthrough offers improved color accuracy and reduced eye strain, with potential applications in home lighting, electronic displays, and workspaces. Unlike traditional bulky bulbs, this slender design could transform how we light our environments while helping minimize sleep disruption from harsh artificial light.
“This work demonstrates the feasibility of ultra-thin, large-area quantum dot LEDs that closely match the solar spectrum,” says Xianghua Wang, a corresponding author of the study. “These devices could enable next-generation eye-friendly displays, adaptive indoor lighting, and even wavelength-tunable sources for horticulture or well-being applications.”
Many people prefer indoor lighting that feels natural and soothing. Earlier approaches achieved this effect with flexible LEDs that used red and yellow phosphorescent dyes to create a candle-like warmth. A newer alternative relies on quantum dots—tiny semiconductor particles that transform electrical energy into colored light. Some research teams have already used quantum dots to make white LEDs, but replicating the complete spectrum of sunlight has remained difficult, particularly in the yellow and green regions where sunlight is strongest. To address this challenge, Lei Chen and colleagues developed quantum dots that could recreate that balanced, sunlike glow in a thin, white quantum dot LED (QLED). Meanwhile, Wang’s group proposed an efficient conductive material design that could operate effectively at relatively low voltages.
The team began by synthesizing red, yellow-green, and blue quantum dots coated with zinc-sulfur shells. They determined the precise color ratio needed to match the spectrum of natural sunlight as closely as possible. Next, they assembled the QLED on an indium tin oxide glass substrate, layering conductive polymers, the quantum dot blend, metal oxide particles, and finally a top coating of aluminum or silver. The quantum dot layer measured only a few dozen nanometers in thickness—much thinner than standard color conversion layers—resulting in a white QLED with an overall profile comparable to wallpaper.
In initial tests, the thin QLED performed best under a 11.5-volt (V) power supply, giving off the maxmium bright, warm white light. The emitted light had more intensity in red wavelengths and less intensity in blue wavelengths, which is better for sleep and eye health, according to the researchers. Objects illuminated by the QLED should appear close to their true colors, scoring over 92% on the color rendering index.
In further experiments, the researchers made 26 white QLED devices, using the same quantum dots but different electrically conductive materials to optimize the operating voltage. These light sources required only 8 V to reach maximum light output, and about 80% exceeded the target brightness for computer monitors.
For more information: ACS Applied Materials
Image: A paper-thin device uses quantum dots, similar to those described in this work, to light up LEDs. Credit: Lin Zhou, Xianghua Wang
A team led by Lawrence Berkeley National Laboratory and George Washington University have confirmed that atoms in semiconductors will arrange themselves in distinctive localized patterns that change the material’s electronic behavior.
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For the first time, scientists have created a fully functional memory chip only a few atoms thick and integrated it into conventional chips, an advance from Fudan University in China that could pave the way for more powerful and energy-efficient electronic devices.
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Oxford Instruments’ Raman imaging team in England announced a refreshed core microscope line and a groundbreaking new spectrometer, Hexalight, which together with the witec360 microscope constitute the most advanced confocal Raman imaging system yet devised.
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Siemens Digital Industries Software, Plano, Texas, introduced Tessent AnalogTest software – an innovative solution that reduces pattern generation time for analog circuit tests from months to days, enabling testing of analog circuitry in integrated circuits up to 100 times faster than traditional manual methods.
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Tektronix, Beaverton, Ore., announced the 7 Series DPO oscilloscope, the first in a new generation of ultra-high-performance instruments, purpose-built for engineering teams and researchers pushing the boundaries of high-speed communications, high-energy physics, AI, and quantum computing.
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Chinese researchers at Hefei University of Technology have created a paper-thin LED that gives off a warm, sun-like glow and could light up the next generation of phone and computer screens and other light sources.
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A team led by Rice University’s Yong Lin Kong has developed a soft yet strong metamaterial capable of rapidly changing its size and shape through remote control, marking a major step forward for ingestible and implantable medical devices. Unlike natural materials, metamaterials derive their unique properties from their physical structure rather than chemical composition. Kong’s design combines exceptional deformability with structural stability—an unprecedented feat in soft materials—allowing it to withstand compressive loads over ten times its own weight and perform reliably in extreme temperatures and harsh chemical environments.
“We programmed multistability, i.e. the ability to exist in multiple stable states, into the soft structure by incorporating geometric features such as trapezoidal supporting segments and reinforced beams,” said Kong, assistant professor of mechanical engineering at Rice’s George R. Brown School of Engineering and Computing. “These elements create an energy barrier that locks the structure into its new shape even after the external actuation force is removed.”
The metamaterial’s soft architecture helps address critical medical safety concerns such as gastric ulcers, puncture injuries and inflammation that can occur from implantable and ingestible devices made of rigid components.
Kong and his team used 3D printing to create molds that form interconnected microarchitectures of tilted beams and supporting segments. This design allows for rapid switching between open (off) and closed (on) states, and the transformed configuration is maintained even after the magnetic field is removed. By combining many such unit cells as “building blocks,” they form a 3D structure that can not only transform its shape but can also produce complex peristaltic motions to move or to deliver fluids in a controlled manner when actuated with a magnetic field.
Importantly, the material continued to function reliably even after prolonged exposure to mechanical stress and acidic corrosion, conditions that mimic the harsh environment of the human stomach.
“The metamaterial makes it possible to remotely control the size and shape of devices inside the body. This could enable lifesaving capabilities such as precisely controlling where a device stays, delivering medication where it’s needed or applying targeted mechanical forces deep inside the body,” Kong said. “We are now leveraging this metamaterial to develop ingestible systems that may one day treat obesity in humans or improve the health of marine mammals, and we are collaborating with surgeons at the Texas Medical Center to design wireless fluidic control systems to address unmet clinical needs.”
The first author of this study was Kong’s first graduate student, Taylor Greenwood, who has since graduated and started a faculty position at Brigham Young University. Kong’s other graduate students Brian Elder and Jared Anklam, postdoctoral associates Jian Teng and Saebom Lee and other collaborators were involved in the study. This research was supported by the National Institutes of Health and the Office of Naval Research.
For more information: Science Advances
Image: The new metamaterial designed by Kong and his team at Rice can be controlled remotely to rapidly transform its size and shape (Photos and video by Jorge Vidal/Rice University).
Researchers at the University of Michigan have developed a breakthrough method that brings quantum-level precision to molecular modeling, offering deeper insights into chemical reactions and material properties. This advancement addresses the quantum many-body problem—how electrons interact to form chemical bonds and influence electrical behavior—which traditionally requires immense computational power and is limited to small molecules. By enhancing the efficiency of this simulation approach, the new method could extend quantum accuracy to larger, more complex systems, potentially reducing the heavy demand on national lab supercomputers.
Density functional theory, or DFT, makes quantum chemistry more manageable by focusing on electron densities rather than tracking every electron individually. This approach keeps computing demands much lower, allowing simulations of systems with hundreds of atoms. A major challenge, however, lies in the exchange-correlation (XC) functional, which governs how electrons interact according to quantum mechanics.
Until now, researchers have had to rely on approximations of the XC functional tailored to specific applications, limiting the theory’s overall accuracy. Improving this functional is key to making DFT an even more powerful tool for chemistry and materials science.
According to Vikram Gavini, a University of Michigan professor of mechanical engineering and the corresponding author of the study, researchers know that a universal functional exists that applies to all electron systems – whether in molecules, metals, or semiconductors – but its exact form remains unknown.
Hence, understanding this functional is crucial for improving DFT, which models electron interactions and underpins simulations in chemistry and materials science.
Given DFT’s central role in advancing both materials research and basic science, the US Department of Energy provided funding and supercomputer resources to support the University of Michigan team’s efforts to approach the universal exchange-correlation functional.
The researchers began by analyzing individual atoms and small molecules using quantum many-body theory. Then, instead of applying approximate functionals to predict electron behavior, they used machine learning to determine which XC functional would reproduce the electrons’ behavior as calculated by the more precise quantum many-body method.
Bikash Kanungo, a University of Michigan assistant research scientist in mechanical engineering and first author of the study, explains that an accurate exchange-correlation functional has broad applications because it is material-agnostic.
It is equally important for researchers developing better battery materials, designing new drugs, or building quantum computers. By improving this functional, scientists can make density functional theory more reliable and widely applicable, enabling more precise simulations across chemistry, materials science, and emerging technologies.
Thus, researchers can now use the XC functional discovered by the University of Michigan team or apply their approach to new systems, starting with light atoms and molecules and eventually extending to solids, paving the way for more accurate and efficient simulations across chemistry and materials science.
For more information: University of Michigan
Image: A 3D map of the quantum potential.
Researchers at Oak Ridge National Laboratory have introduced a powerful new method for investigating the atomic-level behavior of materials by combining Bayesian deep learning—a fusion of probability theory and neural networks—with advanced data analysis. This approach enables scientists to rapidly and accurately process complex datasets, allowing them to scan broader sample areas and identify regions with critical properties far more efficiently than traditional techniques.
“This method makes it possible to study a material’s properties with much greater efficiency,” said Ganesh Narasimha from ORNL. “Usually, we would need to scan a large region, and then several small regions, and perform spectroscopy, which is very time-consuming. Here, the AI algorithm takes control and does this process automatically and intelligently.”
The team demonstrated the system using europium zinc arsenide, a magnetic semimetal with distinctive electronic traits. With the aid of scanning tunneling microscopy, the researchers uncovered links between atomic-scale structures and their electronic responses.
Although the case study focused on europium zinc arsenide, the scientists emphasize that the method is broadly applicable to many different materials. The advance not only streamlines the discovery process but also strengthens national efforts in artificial intelligence and quantum science.
For more information: Nature
Image: A scanning tunneling microscope and machine learning algorithm autonomously search for atomic structures. This image shows a vacancy defect on europium zinc arsenide. (Image Credit: Ganesh Narasimha/ORNL, U.S. Dept. of Energy)
Scientists have directly imaged a rare atomic vibration known as a phason—a phenomenon long theorized but never observed—within a twisted two-dimensional material called tungsten diselenide. These ultra-thin materials, just a few atoms thick, can be stacked in unusual configurations that produce entirely new physical behaviors. Using a cutting-edge technique called electron ptychography, researchers captured the most precise images of individual atoms to date, revealing how phasons vary with atomic arrangement and opening new possibilities for future electronic technologies.
They are only a special case of a broader category of moiré phonons, the result of when two layers of 2D material are shifted slightly one from the other. That fold between the two creates a moiré superlattice—a larger periodic structure that gives rise to unique thermal and electronic behavior. Phasons are an extremely soft, low-frequency variety of these phonons.
Even though phonons and phasons are not seen, their effect is widespread. Heat is atomic vibration in itself, and an understanding of such patterns is required if one expects to have any control over heat transfer in electronics. Greater control could imply materials that cool faster or suck heat off sensitive parts.
You can’t just get rid of phasons; that’s the blessing and curse,” said Pinshane Huang, a professor of materials science and engineering at the University of Illinois at Urbana-Champaign and lead author of the paper. “They’ve always been hiding in plain sight, changing the properties of 2D moiré materials.”
Phasons had mystified scientists for decades. They were thought to belong in theoretical models only, but no one had concrete proof. Not until Huang teamed up with Yichao Zhang, a postdoctoral researcher of nanoscale heat conduction. Their task: to freeze the blur of atomic movement created by heat.
What our primary goal was, was to see heat when looking at an atom,” Huang explained. “What this does is get so fantastic spatial resolution that vibrations of the atoms have something to do with how fuzzy the atoms appear,” she said. “These are tiny movements, and we can basically look at one atom at a time and see its heat-related motion.”.
To do this, the team utilized electron ptychography, a new imaging method with a resolution of less than 15 picometers. That is about one-thousandth the width of an individual atom. It allowed the team to see the width, shape, and movement of atoms in twisted bilayer tungsten diselenide, or WSe₂.
“Back when I started out, we figured the highest resolution you could get was maybe a little bit less than an angstrom,” Huang said. “But once ptychography arrived on the scene, we were starting to see resolution numbers as low as 0.2 angstroms,” she continued. “That put us thinking, ‘Well, heat gets atoms jigged around about 0.05 angstroms,’ she said. “Now that we can actually see heat, it shows what a giant jump in resolution can do for what microscopes can do,” she continued.
Twisted structures are important because they alter the local atomic environment in strong and unexpected manners. When one layer is twisted against another in bilayer materials such as WSe₂, there is a mismatch in the grids of atoms. That mismatch creates new areas—some with atoms close together and others not so close.
When researchers mapped these helical regions, they found more vibrations in some areas. Vibrations were especially strong around solitons, where solitons are borders between different stacking modes. AA-stacked regions, in which atoms lie atop each other, also showed more intense vibrations.
By integrating electron ptychography with lattice dynamics and molecular dynamics simulations, the scientists made a robust conclusion. Phasons were discovered by them as the main culprit behind thermal vibration in low-angle twisted bilayers. This tells us that the moiré pattern is not just an illusion—it plays a pivotal role in determining how heat traverses through the material. These findings provide new doors to investigate and even control heat in 2D materials at the atomic level.
Phasons are tiny, but their potential effect could be huge. Controlling the skill of sensing and studying these vibrations could change the way electronics are designed in the future. “One potential application of this technique is creating materials that are better heat conductors,” said Zhang. “We can look at one atom and find a defect that keeps the material from cooling more efficiently,” he said.
This could result in better means of atomic-level thermal control,” he added. “To watch individual atoms and see how they react to heat will tell us some very important things,” he said.
Twisted 2D materials are already being researched for use in transistors, sensors, and quantum computing hardware. Adding heat behavior to that research means engineers can create entire devices at the atomic level. Such devices would be able to be made smaller, faster, and much more efficient than today’s electronics.
Perhaps most exciting is the manner in which this finding reconciles theory with direct observation. Phasons were previously hypothetical, known by equations and models alone. Now, they are observable in real time.
Using equipment like electron ptychography, even the slightest motion of the atoms can be watched and measured. Science no longer needs to speculate about what atoms do—it’s watching them in plain sight, one vibration at a time.
For more information: Science
Image: (left) atoms present in the 2D material. (right) photos of single atoms. (CREDIT: The Grainger College of Engineering)
Furukawa Electric Co., Ltd., Tokyo, Japan announced that it has been named to the 2024 Supplier Engagement Assessment (SEA) Leaderboard by CDP, an international environmental nonprofit organization. The recognition places the company among the highest-ranked corporations worldwide for its initiatives addressing climate change across the supply chain and for its transparent environmental disclosures.
The CDP supplier engagement assessment evaluates corporate actions that promote emissions reduction and collaboration throughout the value chain. This marks the fifth time Furukawa Electric has earned a place on the leaderboard, reflecting sustained progress in environmental management and reporting.
Aligned with its “Furukawa Electric Group Vision 2030,” the company has advanced its ESG-driven management strategy to enhance long-term corporate value. Under its Environmental Vision 2050—first established in 2021 and updated in March 2025—Furukawa Electric is pursuing initiatives that contribute to a carbon-free society. The company also received SBTi 1.5°C certification in July 2023 for its science-based emissions reduction targets.
Furukawa Electric stated that the recognition underscores its commitment to tracking and reducing greenhouse gas emissions throughout its supply chain. Future plans include expanding the use of renewable energy sources such as hydroelectric and solar power and continuing energy efficiency improvements across offices and manufacturing plants. The company reaffirmed its goal of achieving net-zero greenhouse gas emissions across its entire value chain by 2050.
Johnson Matthey, London, England announced plans to open its first hydrogen internal combustion engine (H₂ICE) testing facility at its existing site in Gothenburg, Sweden. The upgraded center will expand the company’s heavy-duty vehicle testing capabilities, reflecting growing industry and regulatory momentum toward cleaner mobility technologies. The facility is expected to become operational in autumn 2025.
Building on Johnson Matthey’s previous hydrogen combustion research, the new installation will allow full engine testing for the first time. It will assess catalyst performance within broader aftertreatment and control systems, supporting the development of efficient hydrogen-powered mobility solutions.
The project follows the successful completion of Project Brunel, a collaboration launched in 2021 with Cummins, PHINIA, and Zircotec, which delivered key advances in H₂ICE engine performance and durability.
The upgraded Gothenburg site will feature its own hydrogen supply and storage system rated at up to 500 bar, hydrogen flow meters and analyzers, control and safety systems, adapted exhaust measuring instruments, and enhanced fire and gas detection. It will support engines up to 600 kW (800 hp).
Tauseef Salma, chief technology officer of Johnson Matthey Clean Air, said the expansion underscores the company’s commitment to advancing hydrogen mobility as part of global decarbonization efforts. Salma noted that hydrogen engines offer a mature and complementary solution to battery electric vehicles in achieving emissions reduction goals across Europe and beyond.
Johnson Matthey also recently joined the Global Hydrogen Mobility Alliance as a founding member. The coalition, comprising more than 30 major companies including BMW, Toyota, Hyundai, Air Liquide, and Linde, aims to accelerate adoption of hydrogen technologies in Europe’s transport sector.
Cirtec Medical, Brooklyn Park, Minnesota announced a collaborative approach with Ilika, Southampton, United Kingdom, combining application-specific integrated circuit (ASIC) technology with solid-state battery (SSB) innovation to power the next generation of active implantable medical devices (AIMDs). The integration aims to enable compact, rechargeable, and efficient implants for applications such as neuromodulation and cardiac sensing.
The joint solution addresses long-standing design challenges in AIMDs, including limited energy storage capacity, wireless power transfer efficiency, and stable memory retention during low-power or pulsed operating cycles.
Ilika’s Stereax M300 solid-state battery delivers high energy density in a miniaturized footprint, reducing recharge times and extending implant life to minimize the need for replacement surgeries. Cirtec’s ASIC platform complements this by managing power regulation, memory integrity, and startup control, even under variable energy conditions.
By combining these technologies, Ilika and Cirtec offer a scalable and reliable energy architecture for medical device developers, supporting safer, longer-lasting, and more sustainable implant designs that enhance patient outcomes while accelerating product development for medtech innovators.
Norman Noble, Cleveland, Ohio announced the installation of a high-performance 3D optical metrology system to enhance final inspection of complex medical implants, including contoured stents and heart valve frames. The new system is designed to measure challenging geometries and reflective surfaces with exceptional speed and precision.
Featuring a pivoting illumination head, the system captures detailed, non-contact surface measurements on parts with small diameters and steep angles. The technology improves Norman Noble’s ability to inspect critical-to-function features and maintain stringent quality standards for its OEM customers.
Annie Wolfe, director of quality, explained that the automated visual inspection platform provides high-resolution data and greater measurement accuracy, supporting the company’s ongoing commitment to precision manufacturing and quality assurance.
The upgraded inspection capabilities allow engineering and quality teams to validate intricate surface profiles with increased repeatability, resulting in faster development cycles, reduced production risk, and improved consistency across parts. Norman Noble stated that this investment underscores its leadership in advanced inspection technologies for medical implant manufacturing.
The effects of various subcooling and tempering cycles on martempered L3 steel were investigated with changes in specimen length tracked throughout the treatments. All length changes were measured relative to the original, as-martempered state.
The study included four treatment protocols, each maintaining a total tempering time of 10 hours at 120 °C (250 °F), though distributed differently across cycles:
For clarity, the length changes from Treatment D were omitted from the associated figure (Fig. 3), though its net expansion was found to be comparable to that of Treatment A.
Results indicated that the first two refrigeration cycles following martempering were effective in reducing retained austenite in L2 and L3 steels. However, additional cycles beyond the initial two offered no significant benefit. Regardless of cycle structure, maintaining a cumulative tempering time of 10 hours at 120 °C appeared to be a critical factor for dimensional stability.
Two particularly effective strategies combining tempering and subcooling were highlighted:
These approaches suggest that combining initial subcooling with sufficient cumulative tempering can effectively stabilize the microstructure and dimensions of martempered L3 steel.
Wisconsin Oven Corporation, East Troy, Wisconsin announced the shipment of a custom electrically heated batch oven to a major space exploration company. The system will be used to stress relieve titanium components and is equipped with a powered load/unload table to support automated processing.
Designed for a maximum operating temperature of 1,250°F, the oven delivers temperature uniformity of ±15°F at set points ranging from 350°F to 1,200°F. Uniformity was verified through a nine-point temperature profile prior to delivery. The unit features a top-down airflow configuration for consistent heat distribution across the entire load.
The oven can process loads up to 1,200 pounds per cycle. Titanium parts are positioned on a high-strength grid and moved into the 7-foot wide by 10-foot long by 3-foot high work chamber using an automated pusher/extractor system. Post-process cooling is achieved by six high-speed fans that direct ambient air upward over the parts.
The control system includes an Allen-Bradley CompactLogix PLC and industrial PC with a 24-inch monitor. Operators can configure up to 50 custom recipes with 20 programmable steps each and access batch control, material handling, and real-time monitoring from a single interface. Additional features include guaranteed soak control, automatic thermocouple selection, detailed batch reports, and an integrated UPS battery backup.
Wisconsin Oven noted that the system meets AMS 2750G, Class 3, Instrumentation Type A standards. Vice president of sales Mike Grande stated that the oven’s precision temperature control and airflow design enable high-quality processing for demanding aerospace applications.
Constellium, Paris, France announced the extension of its long-term agreement with Embraer to supply advanced aluminum materials, including its proprietary aluminum-lithium alloy, Airware. The renewed partnership will continue to support Embraer’s Commercial Aviation, Executive Jets, and Defense & Security divisions.
Under the agreement, Constellium will provide high-performance aluminum products designed to meet the aerospace industry’s demand for lightweight, durable materials in critical structural applications.
Philippe Hoffmann, president of Constellium’s aerospace and transportation business unit, stated that the contract reinforces the company’s commitment to supporting Embraer’s growth and aligns with its broader strategy to meet increasing global demand for advanced materials in aerospace programs.
Roberto Chaves, executive vice president of global procurement and supply chain at Embraer, noted that the continued partnership is built on a foundation of quality, reliability, and collaboration, and supports efforts to strengthen the supply chain while advancing sustainable growth.
Harper International, Buffalo, New York announced that it has been selected by Fujian Fuwei Advanced Material Co., Ltd. to supply a commercial-scale carbonization line for carbon fiber manufacturing. The equipment is scheduled for delivery to Fujian Fuwei’s facility in Yong’an City, Fujian Province in February 2026.
The turn-key thermal processing system includes three-meter-wide oxidation ovens, low- and high-temperature furnace systems, surface treatment systems, waste gas abatement, and integrated controls and auxiliary operations. The custom-engineered line is designed to enhance fiber quality through improved oxidation rates, greater temperature and velocity uniformity, and precise reaction control.
This order forms part of the first phase of Fujian Fuwei’s high-end materials investment project, valued at 2.3 billion yuan (approximately $320 million), which targets an annual carbon fiber production capacity of 4,000 metric tons. The long-term plan spans 1,142 acres and envisions scaling to 50,000 metric tons per year.
Harper noted that its technology and on-site support services are designed to accelerate commissioning and reduce time-to-market for new production lines. Company vice president Paul Elwell stated that Harper’s carbonization systems remain a preferred choice for manufacturers seeking to reduce commercialization risk while achieving advanced fiber quality.
Solar Atmospheres, Hermitage, Pennsylvania announced the start of installation for a new titanium drop bottom water quench furnace at its Western PA facility. The furnace is engineered for high-performance heat treatment of titanium components, with a maximum operating temperature of 1850°F ±10°F.
Designed to process loads up to 7,500 pounds, the furnace chamber measures 14 feet in length, 54 inches in width, and 48 inches in height. Workloads will be rapidly transferred into a 7,000-gallon recirculated water quench tank within seconds, ensuring uniform metallurgical properties critical to aerospace and industrial specifications.
The installation represents a strategic investment in expanding the company’s titanium solution treating capabilities. Solar Atmospheres stated that the project reinforces its commitment to delivering precision thermal processing solutions tailored to customer demands in high-performance sectors.
Imagic Bildverarbeitung AG and the Materials Engineering Center Saarland (MECS) of Zürich and Saarbrücken, respectively, entered into a strategic partnership in September. The collaboration combines MECS’s expertise in materials science, which bridges the gap between research and industry, with Imagic’s leading software expertise in microscopy, image analysis, and image data management.
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Researchers from University of Oxford and Brookhaven National Laboratory have uncovered how exposure to hydrogen atoms dynamically alters the internal structure of stainless steel. Their findings reveal that hydrogen allows internal defects in steel to move in ways not normally possible, which can lead to unexpected failure.
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Silicon Valley may have led the digital revolution, but Illinois is emerging as a hub for quantum innovation, dubbed the “Quantum Prairie.” Anchored by institutions like the U.S. Department of Energy’s Argonne National Laboratory, the region is attracting both tech giants and startups focused on quantum information science. Researchers are now harnessing quantum physics—first discovered over a century ago—to revolutionize fields such as computing, medicine, and finance, using atomic-scale phenomena to build ultra-sensitive sensors and simulate complex physical systems.
“Building quantum information systems requires new materials, different computing architectures, specialized devices and software. You need to look at how quantum computers will communicate with one another and also how they will connect to classical computer systems,” said Argonne Distinguished Fellow Michael Norman, director of the Argonne Quantum Institute. “At Argonne, we’re doing all of it.”
Argonne is also a key part of two federal designations: the Bloch Quantum Tech Hub, which was designated by the U.S. Economic Development Administration, and Quantum Connected, a Midwest coalition that is a National Science Foundation Regional Innovation Engine (NSF Engines) Development Awardee and a semifinalist in the national NSF Engines competition. And Argonne Laboratory Director Paul Kearns is on the Board of Managers for the Illinois Quantum and Microelectronics Park (IQMP), an in-progress campus in Chicago for quantum technology and microelectronics innovation.
The allure of Illinois for quantum tech
Building on this foundation, Illinois has announced multiple quantum technology initiatives and partnerships in an effort to make Illinois a global quantum capital. In 2024, the state and IBM announced the National Quantum Algorithm Center, which will be located at the IQMP, and quantum computing company PsiQuantum has signed on as the anchor IQMP tenant.
IQMP Executive Director and CEO Harley Johnson, who is also a professor of mechanical science and engineering at the University of Illinois Urbana-Champaign, points to the state’s research bona fides: top schools such as his employer, along with the University of Chicago and Northwestern University; two DOE national labs, Argonne and Fermi National Accelerator Laboratory, which also head up two of the five DOE National Quantum Information Science Research Centers; and a partnership with the U.S. Department of Defense, part of the larger Quantum Benchmarking Initiative (QBI), to be a “national proving ground” for quantum. Argonne is leading several of the teams vetting quantum computing companies for the QBI.
“That’s an incredible base to build on,” Johnson said. “Then, when you add the state government, nonprofits, economic development organizations and the companies that are already here, there’s a strong ‘team Illinois’ environment that comes across to potential partners. They see that we’re pulling in the same direction.”
Preeti Chalsani, chief quantum officer at the public-private economic development partnership Illinois Economic Development Corporation, said that when she participates in conferences, she hears talk of other states that want to replicate Illinois’ success in kickstarting the quantum industry.
“It’s not accidental that quantum is strong here,” Chalsani said, pointing to the state’s storied, multidisciplinary research history and assets such as Argonne’s APS and the ALCF, with its Aurora exascale computer. “Chicago and Illinois have done a really good job making researchers aware of quantum as not just a research enterprise, but a whole industry with lots of opportunities to collaborate.”
An ecosystem for quantum
The breadth of Argonne’s work in quantum information science, combined with the field’s increasing relevance, led the laboratory to create the Argonne Quantum Institute in October 2023. The institute coordinates research efforts both internally at the lab and with external partners, such as IBM, Infleqtion, Intel, Quantinuum and JPMorgan Chase. A recent article in Nature showcases the work of the latter two companies with Argonne scientists. Several scientists at the UChicago Pritzker School of Molecular Engineering, which has a core focus on quantum information research, conduct their work jointly at Argonne. The two institutions share several quantum-focused research initiatives as well.
“The fact that Argonne invests in the Quantum Foundry as a way to atomically engineer and construct materials has taken us from relying on external sources to provide materials over periods of months to performing everything inhouse within a day,” Awschalom said.
Some of the scientists working at Argonne are entrepreneurs who are building quantum companies with support from the lab’s two-year fellowship program, Chain Reaction Innovations. Founders of startups including memQ and Super.tech are Chain Reaction Innovations alumni. Quantum accelerator Duality, based at the University of Chicago and with Argonne as a founding partner, also provides funding and expertise to accepted startups. Both programs help connect entrepreneurs to the region’s larger quantum community and industry base.
“A lot of the work of startups relies on connections to research labs,” Chalsani said. “It also depends on connections to larger companies who could be strategic partners that can help with their product development or go-to-market strategy, or customers who would adopt their technologies. This work really cannot be done in isolation.”
Building the quantum workforce
The key to a quantum future is a strong workforce. Argonne invests in the next generation of quantum scientists by hosting summer research experiences for undergraduates, who receive hands-on training at the Argonne Quantum Foundry. And Q-NEXT, for which Argonne is the lead laboratory, has supported more than 150 students and postdocs over the past five years.
These initiatives address the rapidly growing demand for professionals with quantum materials and computational expertise, reinforcing Argonne’s commitment to empowering future scientific leaders.
“Quantum science won’t advance without the next generation,” Norman said. “Giving students and early-career researchers real lab experience, good mentors and a solid foundation is how we’ll keep Argonne at the forefront of this field. Our efforts here have been enabled by both Chicago Quantum Exchange’s Open Quantum Initiative program and DOE’s Science Undergraduate Laboratory Internships.”
Even though quantum information science has been progressing steadily over the past decade, specialized graduate programs have only just begun to appear within the last five years or so. The University of Chicago introduced one of the first in the nation. The explosion of investment and innovation in quantum information science is creating demand for expertise.
“One of the biggest challenges in the field is not only the science and technology, it’s scaling the workforce at all levels to meet growing demand,” Awschalom said. “How do we create a sufficient supply of quantum engineers and technicians to innovate and build quantum technologies?”
The answer is not just people with advanced schooling. More than half of quantum technology jobs do not require a graduate degree.
“Several years ago, it was mostly research scientists with Ph.D.s or postdocs who were involved in quantum,” Chalsani said. “But as the industry has matured, it requires a whole range of other talent.” That includes equipment technicians, software engineers, HVAC installers and other key roles.
Some of the technologies this workforce will support are already here, such as quantum navigation sensors. Some, like quantum computing, are further off.
And others? We don’t know what they are yet.
“History has shown that whenever there are discontinuous changes in science and technology, the biggest impacts are the ones we’re not imagining in this conversation,” Awschalom said. “The trick is, will we be ready when they arrive? Because I guarantee you, they’re going to appear — they always do.”
Engineers at the University of Wisconsin–Madison have developed a versatile new electrolyte that advances the development of an initially anode-free sodium-ion battery—a promising alternative to lithium-ion batteries for electric vehicles and grid energy storage. Led by Assistant Professor Fang Liu and PhD students Qianli Xing and Ziqi Yang, the team is also using this electrolyte as a model system to explore how molecular manipulation can improve compatibility between different battery components, potentially paving the way for more efficient and energy-dense battery technologies.
Containing solvents and dissolved salts, the electrolyte is the liquid medium that touches all parts of the battery’s cells and, in its charging or discharging process, helps ions travel between the electrodes.
In a battery, the anode and cathode are different materials—for example, graphite, hard carbon sodium or lithium for the anode and a transition metal oxide like lithium nickel manganese cobalt oxide or sodium nickel iron manganese oxide for the cathode.
One of the challenges in developing next-generation batteries is that there’s not a one-size-fits-all electrolyte that performs effectively with both electrode material types. Conversely, when an electrolyte contains multiple solvent molecules, controlling their interactions and behavior is challenging.
Tweaking the electrolyte is a balancing act involving multiple factors, including how solvent molecules in the electrolyte form a “shell” around ions that could accelerate or impede the ions’ movement between anode and cathode—which ultimately affects battery charging and discharging, along with overall battery performance. “Using this model system, we are basically trying to understand whether we can present different molecules to different electrode surfaces—for example, an anode-stable solvent to the anode, and then a cathode-stable solvent to the cathode,” says Liu. “In this way, the electrolyte mixture would ideally behave like an anode-stable solvent at the anode, and like a cathode-stable solvent at the cathode.”
To create its new electrolyte, the team mixed two ether-based solvents, 2-methyltetrahydrofuran, or 2-MeTHF, which is more stable at the anode, and tetrahydrofuran, or THF, which is more stable at the cathode. Importantly, they found a way to rationalize electrolyte design: Solvents that dominate the first shell around positively charged ions that travel between electrodes are key to anode stability, while “free” or more weakly bonded solvents are important to the stability of the cathode side. “Through this electrolyte engineering work, we were trying to demystify what determines the stability of the anode and cathode separately, and how to present suitable molecules to both electrodes,” says Liu. “Qianli found out that the key factor is the population of solvents in the first solvation shell versus outside, and their location determines their presentation during the battery formation process.”
Computational testing, conducted by collaborator Reid Van Lehn, an associate professor of chemical and biological engineering at UW-Madison, and his student Jung Min Lee, played a significant role in the research as well. They used all-atom molecular dynamics simulations to predict the composition of solvent molecules near sodium ions and determine whether those ions “preferred” one solvent over the other. “Our results indeed found—in good agreement with experiments from the Liu group—that we could identify a single strongly interacting solvent (2-MeTHF) and a weakly interacting solvent (THF),” says Van Lehn. “We further used these calculations to relate this behavior to the relative strength of interactions of each type of solvent, providing molecular-scale insight that can be extended to even more complex mixtures to continue optimizing electrolyte design.”
The research lays the groundwork for the next steps in developing not simply sodium-metal batteries, but also other new alternatives to lithium-ion batteries. “Through this research, we start to understand that the solvent and anion interactions become really important,” says Liu. “We’re trying to expand our solvent library to manipulate these kinds of interactions, to see whether this kind of working principle can be applied to broader solvent libraries and different battery chemistries.”
Now, inspired by recent advances in machine learning, Bernardi and Luo have developed an AI-based technique that sifts through the high-order tensors that encode phonon interactions in a material and extracts only the crucial bits needed to complete the calculations that explain thermal transport.
“The calculations for four-phonon interactions are a nightmare,” Bernardi says. “For complex materials, this task would involve weekslong calculations. Now we can do them in 10 seconds.”
Bernardi explains more about the method:
“We use a machine learning technique called CANDECOMP/PARAFAC tensor decomposition, but we had to adapt it to satisfy the symmetry of this specific physical problem. We first set up a neural network and then run it on GPUs and ask: ‘What are the best functions to approximate the actual tensor that describes these phonon interactions?’ Once we fix the number of product terms we want to keep, the machine learning process returns the best functions to approximate the full tensor. We typically only need a few of these products, saving orders of magnitude in computational complexity compared to using the full tensor. This method allows us to learn the compressed form of phonon interactions, and we can still use these highly compressed tensors to compute all the observables of interest with the same accuracy.”
Bernardi adds that the new method is well suited for high-throughput screening of thermal physics and heat transport in large material databases, a major effort in the materials community. As for future work, he says, “My vision right now is to compress all different types of quantum interactions and high-order processes in materials with similar techniques. The key will be to bypass the formation of large tensors altogether and to learn the interactions directly in compressed form.”
