Tough, reusable adhesive can glue a variety of materials

Researchers at the Department of Energy’s Oak Ridge National Laboratory have developed a reusable adhesive made from waste polymers that is stronger than commercial glues, works in wet and dry conditions, and bonds materials ranging from wood and glass to metal and plastics. Inspired by mussels’ sticking power, the adhesive uses reversible chemical crosslinkers that let it soften, release and be reused, unlike conventional single-use glues. The innovation could simplify manufacturing and repair by replacing the many specialized adhesives used today and may have significant economic impact in the global adhesives and sealants market, valued at about $87 billion and projected to reach nearly $119 billion by 2032.

“Most adhesives are made for one specific application,” said Anisur Rahman, a research and development staff member at ORNL who led a study with former ORNL postdoctoral researcher Mary Danielson, now a research assistant professor with the University of Tennessee-Oak Ridge Innovation Institute. “Our adhesive can be used for diverse applications, including structural or pressure-sensitive uses, and it performs reliably in both wet and dry environments,” he said. “None of the commercial adhesives can be used this way.” 

Beginning with common polymers from beverage bottles, fabric fibers and packaging films, the research team developed a process that saves materials, energy and money. “We took material destined for the landfill and turned it into something valuable,” Danielson said.

The researchers have applied for a patent for their versatile glue. 

How the reversible bonds work

Traditional structural adhesives rely on permanent crosslinks that make removal difficult. “You apply traditional adhesives once; you cannot reuse them,” Rahman said. 

“You basically have to rip an assembly apart to debond it,” Danielson added. “You’ve damaged both the part you’re glueing to and the part you’re glueing from. If you make a mistake when you’re gluing something and you allow it to cure, it’s done.“

In the ORNL adhesive, crosslinkers act like reversible attachments, akin to Velcro. Heating breaks dynamic chemical bonds in the polymer, allowing the adhesive to release without damaging surfaces. As the material cools, the bonds reform.

“If something is damaged or misapplied, you’re able to completely remove it and put it back on with full integrity,” Danielson said.

The team debonded and rebounded the adhesive more than 10 times with no loss in performance. 

“Normally in the marketplace, structural adhesives typically have shear strength — a measure of adhesion — in the 7- to 10-megapascal range,” Rahman said. “Our adhesives also stay well above that range but maintain reusability.”

The researchers can also retrieve the glue chemically, using an excess of amine molecules to break the adhesive into its monomer subunits. “We can recover all chemicals used in this adhesive,” Rahman said.

Mussel-inspired design

A polymer is a long chain or network made of monomers, or chemical subunits of one type. Using no solvents or catalysts, the scientists added amine, a nitrogen-containing chemical group, to the waste polymer and heated it to just below the polymer’s melting temperature. Under these mild conditions, the amine broke the polymer down into monomers that each contained four amine groups.

Next, to design the adhesive, ORNL researchers mimicked mussel foot proteins, which contain both hydrophilic and hydrophobic components that enable strong adhesion even in wet environments.

“We used a crosslinker, or hardener, that has both water-loving (hydrophilic) and water-hating (hydrophobic) components together in the same molecule,” Rahman said. “We mix the hardener and the monomer. It creates an adhesive resin that acts like a mussel foot protein.”

“For any glue that is a cross-linked network of two components, it takes time to complete the reaction between the two components,” Danielson said. “To repair boats, submarines and pipelines, our glue can be applied underwater using hand pressure until it sets.” 

Curing happens when a large four-armed monomer interacts with the crosslinking hardener. The monomer’s amine group reacts with the hardener’s acetoacetate group to produce a resin, or matrix with hydrophilic and hydrophobic characteristics. Whether the protein sticks or releases depends on the balance of those properties. 

“Our glue maintained strong adhesion across different environmental conditions, including seawater, extremely low temperature (100 degrees Celsius below zero), and both acidic and basic conditions,” Rahman said.

National lab capabilities enabled the achievement

Rahman conceived the concept of transforming deconstructed polymer waste into an adhesive. He and Danielson designed and led experiments and drafted the paper. Chuyi Pan, a summer intern from the University of Pennsylvania, assisted in synthesizing the adhesive. Tomonori Saito of ORNL and the University of Tennessee, Knoxville, reviewed and edited the manuscript drafts.

ORNL researchers performed vital characterizations. Bobby Sumpter simulated the energies with which the adhesive bound to different surface materials. Catalin Gainaru used rheology to characterize its stress and relaxation. Honghai Zhang and Vilmos Kertesz performed mass spectrometry to quantify different molecules. Zoriana Demchuk’s lifecycle analysis of the ORNL glue showed it was more energy-efficient to make than commercial adhesives. 

Toward strong and weak bonding applications

The team has also explored using this pioneering chemistry to advance vehicles. ORNL’s glue maintained strong adhesion between dissimilar substrates — a crucial requirement in automotive and aerospace applications, where joining composites to aluminum or steel presents notable challenges.

Next, the scientists aim to tune crosslinking to enable weaker, temporary bonds for removable labels, adhesive bandages, drug-delivery patches and other applications. 

ORNL’s versatile, high-performance glue is poised to make an impact that sticks in situations from the mundane to the extraordinary. Potential uses range from household items that require gentle removal, like press-on nails and price tags, to repairs in remote or extreme environments, including underwater or outer space — settings where specialty glues may be unavailable. 

The DOE Office of Science supported the research. The work used resources of the Center for Nanophase Materials Sciences, a DOE Office of Science user facility at ORNL.

For more information: Science Advances

Image: From left, Mary Danielson and Anisur Rahman, leaders of an ORNL project to invent a versatile reusable glue from polymer waste, examine its bonding performance. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Terahertz microscope reveals the motion of superconducting electrons

MIT physicists have used a new imaging technique to observe terahertz-frequency vibrations—described as quantum “jiggles”—inside a superconducting fluid for the first time, revealing behavior that was previously undetectable. By shining terahertz light, which probes matter differently than optical, infrared or X-ray wavelengths, the team captured inherent quantum motions within the material, opening a new window into the fundamental properties of superconductors.

Terahertz light is a form of energy that lies between microwaves and infrared radiation on the electromagnetic spectrum. It oscillates over a trillion times per second — just the right pace to match how atoms and electrons naturally vibrate inside materials. Ideally, this makes terahertz light the perfect tool to probe these motions.

But while the frequency is right, the wavelength — the distance over which the wave repeats in space — is not. Terahertz waves have wavelengths hundreds of microns long. Because the smallest spot that any kind of light can be focused into is limited by its wavelength, terahertz beams cannot be tightly confined. As a result, a focused terahertz beam is physically too large to interact effectively with microscopic samples, simply washing over these tiny structures without revealing fine detail.

The scientists report that they have developed a new terahertz microscope that compresses terahertz light down to microscopic dimensions. This pinpoint of terahertz light can resolve quantum details in materials that were previously inaccessible.

The team used the new microscope to send terahertz light into a sample of bismuth strontium calcium copper oxide, or BSCCO (pronounced “BIS-co”) — a material that superconducts at relatively high temperatures. With the terahertz scope, the team observed a frictionless “superfluid” of superconducting electrons that were collectively jiggling back and forth at terahertz frequencies within the BSCCO material.

“This new microscope now allows us to see a new mode of superconducting electrons that nobody has ever seen before,” says Nuh Gedik, the Donner Professor of Physics at MIT.

By using terahertz light to probe BSCCO and other superconductors, scientists can gain a better understanding of properties that could lead to long-coveted room-temperature superconductors. The new microscope can also help to identify materials that emit and receive terahertz radiation. Such materials could be the foundation of future wireless, terahertz-based communications, that could potentially transmit more data at faster rates compared to today’s microwave-based communications.

“There’s a huge push to take Wi-Fi or telecommunications to the next level, to terahertz frequencies,” says Alexander von Hoegen, a postdoc in MIT’s Materials Research Laboratory and lead author of the study. “If you have a terahertz microscope, you could study how terahertz light interacts with microscopically small devices that could serve as future antennas or receivers.”

In addition to Gedik and von Hoegen, the study’s MIT co-authors include Tommy Tai, Clifford Allington, Matthew Yeung, Jacob Pettine, Alexander Kossak, Byunghun Lee, and Geoffrey Beach, along with collaborators at Harvard University, the Max Planck Institute for the Structure and Dynamics of Matter, the Max Planck Institute for the Physics of Complex Systems and the Brookhaven National Lab.

Hitting a limit

Terahertz light is a promising yet largely untapped imaging tool. It occupies a unique spectral “sweet spot”: Like microwaves, radio waves, and visible light, terahertz radiation is nonionizing and therefore does not carry enough energy to cause harmful radiation effects, making it safe for use in humans and biological tissues. At the same time, much like X-rays, terahertz waves can penetrate a wide range of materials, including fabric, wood, cardboard, plastic, ceramics, and even thin brick walls.

Owing to these distinctive properties, terahertz light is being actively explored for applications in security screening, medical imaging, and wireless communications. In contrast, far less effort has been devoted to applying terahertz radiation to microscopy and the illumination of microscopic phenomena. The primary reason is a fundamental limitation shared by all forms of light: the diffraction limit, which restricts spatial resolution to roughly the wavelength of the radiation used.

With wavelengths on the order of hundreds of microns, terahertz radiation is far larger than atoms, molecules, and many other microscopic structures. As a result, its ability to directly resolve microscale features is fundamentally constrained.

“Our main motivation is this problem that, you might have a 10-micron sample, but your terahertz light has a 100-micron wavelength, so what you would mostly be measuring is air, or the vacuum around your sample,” von Hoegen explains. “You would be missing all these quantum phases that have characteristic fingerprints in the terahertz regime.”

Zooming in

The team found a way around the terahertz diffraction limit by using spintronic emitters — a recent technology that produces sharp pulses of terahertz light. Spintronic emitters are made from multiple ultrathin metallic layers. When a laser illuminates the multilayered structure, the light triggers a cascade of effects in the electrons within each layer, such that the structure ultimately emits a pulse of energy at terahertz frequencies.

By holding a sample close to the emitter, the team trapped the terahertz light before it had a chance to spread, essentially squeezing it into a space much smaller than its wavelength. In this regime, the light can bypass the diffraction limit to resolve features that were previously too small to see.

The MIT team adapted this technology to observe microscopic, quantum-scale phenomena. For their new study, the team developed a terahertz microscope using spintronic emitters interfaced with a Bragg mirror. This multilayered structure of reflective films successively filters out certain, undesired wavelengths of light while letting through others, protecting the sample from the “harmful” laser which triggers the terahertz emission.

As a demonstration, the team used the new microscope to image a small, atomically thin sample of BSCCO. They placed the sample very close to the terahertz source and imaged it at temperatures close to absolute zero — cold enough for the material to become a superconductor. To create the image, they scanned the laser beam, sending terahertz light through the sample and looking for the specific signatures left by the superconducting electrons.

“We see the terahertz field gets dramatically distorted, with little oscillations following the main pulse,” von Hoegen says. “That tells us that something in the sample is emitting terahertz light, after it got kicked by our initial terahertz pulse.”

With further analysis, the team concluded that the terahertz microscope was observing the natural, collective terahertz oscillations of superconducting electrons within the material.

“It’s this superconducting gel that we’re sort of seeing jiggle,” von Hoegen says.

This jiggling superfluid was expected, but never directly visualized until now. The team is now applying the microscope to other two-dimensional materials, where they hope to capture more terahertz phenomena.

“There are a lot of the fundamental excitations, like lattice vibrations and magnetic processes, and all these collective modes that happen at terahertz frequencies,” von Hoegen says. “We can now resonantly zoom in on these interesting physics with our terahertz microscope.”

For more information: Nature

Argonne National Laboratory’s new facility is giving researchers an inside look at irradiated nuclear materials

Argonne National Laboratory has opened its Activated Materials Lab, a radiological facility next to the Advanced Photon Source that lets scientists use an ultrabright X-ray beam to safely examine the inner structure of irradiated metals and nuclear fuels. The new capability allows researchers to see how radiation affects reactor materials in real time, supporting advances in reactor component design and helping utilities better plan maintenance and repair schedules.

Activated Materials Lab

Equipped with fume hoods, glove boxes, shielded containers, and approved sample containments, the new Activated Materials Lab can safely handle samples with higher radioactivity than previously allowed at the facility.

Staffed with a dedicated team that is responsible for receiving radioactive samples and transferring them safely to the Advanced Photon Source x-ray beamlines for measurements, the lab is able to reduce the turnaround time on user experiments while allowing researchers to focus on data collection and interpretation.

The Advanced Photon Source recently underwent a major upgrade project which enhanced its brightness over 100-fold and built nine new x-ray beamlines, including the High-Energy X-ray Microscope beamline at 20-ID which is adjacent to the Activated Materials Lab. With enhanced access to upgraded x-ray capabilities, researchers now have the ability to pursue a wider range of experiments. 

“By safely enabling higher activity samples at the Advanced Photon Source, this new capability allows for clearer views of how materials change during their time in a reactor, speeding progress toward safer, longer-lasting components,” said Brenden J. Heidrich, director of the U.S. Department of Energy’s Nuclear Science User Facilities program. 

Going Granular 

The new facility recently completed its first-ever user experiments examining the origins of stress corrosion cracks in irradiated materials.

Researchers looked at stainless steel parts that after decades of use were removed from a light-water reactor. The parts had developed microcracks due to a combination of irradiation, mechanical stress, and exposure to a corrosive environment.

Using a combination of three-dimensional x-ray techniques, researchers were able to examine the metal at the polycrystal grain level to see if they could find characteristics that correlated to cracking.

Finding the mechanisms that contribute to this behavior in alloys used in nuclear reactors will inform future material designs and long-term performance of existing reactors.

“This first experiment shows that the Activated Materials Laboratory can safely bridge irradiated samples to world-class X-ray tools, lowering barriers for the nuclear materials community,” said Xuan Zhang, Principal Materials Scientist at Argonne and facility lead of the AML. ​“By coordinating shipping, encapsulation, and safety reviews, AML helps users make the most of beam time and investigate questions that were out of reach before.”

“Access to higher-activity samples at beamline 1-ID, under strict controls, lets us probe grain-level behavior in materials that reflect real conditions,” said Jon Almer, Group Leader and Scientist, Materials Physics and Engineering Group, at Argonne. ​“Combining tomography with far-field and near-field high-energy diffraction microscopy gives a fuller picture that can inform models and materials design.”

The experiment was led by the University of Illinois Urbana–Champaign’s Professor James Stubbins, with collaborators at Oak Ridge National Laboratory, The University of Alabama, and Argonne National Laboratory, and was funded by the U.S. Department of Energy’s Nuclear Energy University Program and the Light Water Reactor Sustainability program. 

What’s Next?

The Activated Materials Lab stands ready to receive its next user experiment, adding its capacity to the list of innovative resources and capabilities the U. S. Department of Energy provides to researcher and industry to advance nuclear materials and technologies.

The Activated Materials Laboratory is supported by the Nuclear Science User Facilities (NSUF) program. The Advanced Photon Source is a Department of Energy (DOE) Office of Science user facility operated by Argonne National Laboratory.

For more information: NSUF

Electric field tunes vibrations to ease heat transfer

New research from the Department of Energy’s Oak Ridge National Laboratory found that applying an electric field to a ceramic material removes barriers to phonon transport, conducting heat almost three times more efficiently along the field direction, challenging conventional understanding about controlling heat flow in solid materials.

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How rough grinding makes stainless steel prone to corrosion

Stainless steel is widely known for its corrosion resistance. But when it is exposed to environments containing chloride ions, such as seawater, the risk of corrosion increases. Manufacturers typically grind the surface to smooth it. However, this finishing process reduces corrosion resistance even further. Researchers at Tohoku University in Japan have recently shed light on why this occurs.

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Stacking up: A new take on diamond electronics

Researchers at the U.S. Department of Energy’s Argonne National Laboratory have overcome a critical barrier in diamond-based electronics and microelectronics by using nanotechnology to integrate two-dimensional materials for efficient n-type doping, a breakthrough in electronic materials designed to operate in high-temperature environments and other harsh environments.

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Scientists achieve sub-second 3D printing using rotating light field

Researchers have developed a new sub-second volumetric 3D printing technique that eliminates the need to rotate the printed sample, a long-standing mechanical challenge in the field. The system, called Digital Incoherent Synthesis of Holographic Light Fields, or DISH, instead rotates the illumination using a high-speed periscope, allowing millimeter-scale structures to be printed in 0.6 seconds with about 19-micrometer resolution across a 1-centimeter depth range. The advance addresses a persistent trade-off in volumetric additive manufacturing between resolution, stability and printable volume.

Volumetric 3D printing has long hoped to fabricate entire objects simultaneously – rather than layer by layer. But established approaches, such as computed axial lithography, typically rotate the resin container during exposure.

Fast rotation introduces vibration and alignment errors. Slow rotation, meanwhile, requires highly viscous resins, often thousands of centipoise, to prevent features from drifting before polymerization completes.

When it comes to optics, higher resolution demands higher numerical aperture (NA) objectives. Yet higher NA optics come with a shallow depth of field.

The system used in the study has an intrinsic NA of 0.055 at 405 nm, with a native depth-of-field of roughly 0.4 mm. That’s far smaller than the centimetre-scale volumes desirable for practical manufacturing. 

DISH tackles both precision and scale at once.

In their method, instead of moving the resin container the researchers mounted a rotating periscope on a hollow stage to deliver synchronized angular illumination while keeping the sample stationary.

A 405 nm coherent laser is modulated by a Digital Micromirror Device operating at 17 kHz, projecting optimized binary patterns as the periscope rotates at speeds up to 10 revolutions per second.

The demonstrated sub-second fabrication corresponds to the specific exposure timing used in the reported experiments.

The team abandoned conventional ray-based approximations and implemented a wave-optics model that explicitly incorporates diffraction and refraction at the air–material interface.

A coarse-to-fine iterative optimization algorithm generates projection patterns that maintain intensity modulation well beyond the native focal plane.

An adaptive calibration scheme using two orthogonal cameras corrects single-pixel misalignments in the synthesized 3D light field, improving angular registration and exposure fidelity.

Performance tests show that DISH maintains approximately 19 μm feature fidelity across a 1 cm depth range, far exceeding the objective’s intrinsic 0.4 mm depth of field.

Relief-structure experiments demonstrated approximately 11 μm uniform linewidth across the full centimeter span, while the smallest independently resolved positive feature measured 12 μm.

Comparative tests against conventional back-projection approaches showed sharper edges and improved consistency, particularly in off-center regions where optical blur typically increases.

The single-sided illumination geometry does introduce a missing-cone trade-off that slightly affects axial resolution. The authors note that alternative periscope geometries could mitigate this limitation in future implementations.

One of the more practically significant findings is material compatibility. The system printed successfully in aqueous solutions of polyethylene glycol diacrylate with viscosities as low as 4.7 cP. Because polymerization completes within 0.6 seconds, gravitational drift occurs only after solidification.

By contrast, conventional volumetric systems often require viscosities between 6,000 and 10,000 cP to maintain positional stability during slower exposures.

The researchers also demonstrated printing in higher-viscosity resins and bio-derived hydrogels, including gelatin methacrylate (GelMA) and silk fibroin methacrylate (SilMA).

The single-sided geometry further enables in situ fabrication on fixed substrates and within confined environments such as petri dishes.

Integration with a fluidic channel allowed successive fabrication of multiple structures, pointing toward continuous production workflows.

The authors estimate voxel rates on the order of 1.25 × 108/second, calculated for a defined voxel size and build volume. They suggest that higher-power lasers and faster modulation hardware could further increase build rates.

Surface analysis indicates that inclined projection reduces the prominence of stripe-like speckle artefacts compared with perpendicular illumination systems.

However, the hologram optimization process currently requires substantial offline computation. The authors propose GPU acceleration or neural-network-based approaches as pathways to reduce processing time and enable more automated deployment.

By decoupling angular illumination from sample motion and synthesizing holographic light fields through wave-optics modeling, DISH demonstrates a way to extend effective depth performance without sacrificing resolution.

While industrial deployment remains prospective, the work outlines a credible pathway toward faster, continuous volumetric manufacturing using both acrylate-based systems and selected biomaterials.

Future efforts are likely to focus on accelerating hologram computation, refining optical geometries to address missing-cone effects, and scaling projection hardware.

For more information: Nature

Golden experiment reveals the invisible forces holding the universe together

Scientists in Sweden have developed a simple method to make the universe’s normally invisible binding forces visible by using gold flakes, salt water and light. The Chalmers University of Technology team created a platform that displays these tiny forces—often described as “nature’s invisible glue,” responsible for effects like dust sticking to surfaces or lizards climbing walls—as shifting colors. The technique offers a faster, more accessible way to study how matter organizes itself at the smallest scales.

Inside the lab, doctoral student Michaela Hošková holds up a small glass container filled with millions of micrometer-sized gold flakes suspended in a salt solution. Using a pipette, she places a droplet of the mixture onto a gold-coated glass plate positioned under an optical microscope. The flakes are immediately drawn toward the surface but stop just short of fully attaching, leaving nanometer-sized gaps between the flakes and the gold substrate.

These tiny liquid-filled gaps act like miniature light chambers. Light reflects back and forth inside them, producing visible colors. When the setup is illuminated with a halogen lamp and the reflected light is analyzed with a spectrometer, the different wavelengths become clear. On the connected monitor, flakes shimmer and shift in colors such as red and green against a golden yellow background.

“What we are seeing is how fundamental forces in nature interact with each other. Through these tiny cavities, we can now measure and study the forces we call ‘nature’s glue’ – what binds objects together at the smallest scales. We don’t need to intervene in what is happening, we just observe the natural movements of the flakes,” says Michaela Hošková, a doctoral student at the Department of Physics at Chalmers University of Technology.

By examining the light captured in the cavities, the team can analyze the balance between two competing forces – one that pulls the flakes together and one that keeps them apart. The attractive force, known as the Casimir effect, causes the gold flakes to move toward each other and toward the surface. The opposing electrostatic force develops in the salt solution and prevents the flakes from fully sticking. When these forces reach equilibrium, a process called self-assembly occurs, forming the cavities that make the measurements possible.

“Forces at the nanoscale affect how different materials or structures are assembled, but we still do not fully understand all the principles that govern this complex self-assembly. If we fully understood them, we could learn to control self-assembly at the nanoscale. At the same time, we can gain insights into how the same principles govern nature on much larger scales, even how galaxies form,” says Michaela Hošková.

The new platform builds on several years of research in Professor Timur Shegai’s group at the Department of Physics. Four years ago, the team showed that a pair of gold flakes could form a self assembled resonator. They have now expanded that discovery into a broader method for investigating fundamental forces.

In this system, the gold flakes function as tiny floating sensors. According to the researchers, the approach could be valuable across physics, chemistry, and materials science.

“The method allows us to study the charge of individual particles and the forces acting between them. Other methods for studying these forces often require sophisticated instruments which cannot provide information down to the particle level,” says research leader Timur Shegai.

The platform may also help scientists better understand how particles behave in liquids, including whether they remain stable or tend to clump together. That knowledge could improve how medicines move through the body, support the design of more effective biosensors, and contribute to better water filtration systems. It is also relevant for everyday products such as cosmetics, where preventing unwanted clumping is essential.

“The fact that the platform allows us to study fundamental forces and material properties shows its potential as a truly promising research platform,” says Timur Shegai.

In the laboratory, Hošková opens a small box containing a finished version of the device. Using tweezers, she places it into the microscope. Two thin glass plates enclose everything needed to examine nature’s invisible glue.

“What I find most exciting is that the measurement itself is so beautiful and easy. The method is simple and fast, based only on the movement of gold flakes and the interaction between light and matter,” says Michaela Hošková, zooming in on a gold flake whose colors immediately reveal the forces at work.

Gold flakes about 10 micrometers in size are placed in a salt solution, meaning water that contains free ions. When a droplet is added to a gold-coated glass surface, the flakes are drawn toward it, and nanometer-sized cavities (100-200 nanometers) form. This self assembly results from the balance between two forces: the Casimir force, a measurable quantum effect that pulls objects together, and the electrostatic force that arises between charged surfaces in a salt solution.

A halogen lamp shines light into the cavities, where it becomes trapped and reflected. An optical microscope and spectrometer then separate the light into its component wavelengths so the colors can be identified. By adjusting the salt concentration and observing how the flakes shift relative to the surface, researchers can measure the underlying forces. To prevent evaporation, the droplet containing the gold flakes is sealed and covered with a second glass plate.

The platform was developed at Chalmers’ Nanofabrication Laboratory, Myfab Chalmers, and at the Chalmers Materials Analysis Laboratory (CMAL).

For more information: PNAS

Image: Researchers at Chalmers University of Technology, Sweden, have discovered a quick and easy way to study the hidden forces that bind the smallest objects in the universe together. Using gold, salt water, and light, they have created a platform on which the forces can be seen through colors. Two thin glass plates hold everything needed to study nature’s invisible glue. Credit: Chalmers University of Technology | Mia Halleröd Palmgren

Research team shows nanoparticles adhere to quantum mechanics

Researchers at the University of Vienna have demonstrated quantum interference in sodium nanoparticles containing more than 7,000 atoms—and produced clusters of nearly 10,000—showing that even large metallic particles can exhibit quantum behavior. Lead author Sebastian Pedalino said the results challenge the assumption that such objects must behave classically, confirming that quantum mechanics remains valid at this scale without the need for alternative models. 

The team, led by Markus Arndt and Stefan Gerlich, achieved the breakthrough using sodium clusters exceeding 170,000 atomic mass units, larger than most proteins, marking a significant advance toward the long-sought goal of sustaining quantum superposition in increasingly complex systems.

The test was conducted using the multi-scale cluster interference experiment (MUSCLE). 

To observe interference, the team had to minimize environmental disturbances that would destroy coherence. They produced the sodium clusters under cryogenic conditions (77 K, the temperature of liquid nitrogen) to reduce thermal decoherence, and ran the interferometer in ultra-high vacuum (about 9 × 10⁻⁹ mbar, roughly 10⁻¹¹ of atmospheric pressure) to limit collisions with gas molecules.

The scientists produced sodium clusters containing roughly 5,000 to 10,000 atoms and sent them through a three-grating interferometer made from standing ultraviolet laser light. The first grating spatially confined the particles to build up coherence, and the second grating acted as a beam splitter for the clusters’ matter wave. Scanning the third grating revealed an interference fringe pattern, and the team showed the fringe visibility followed the quantum prediction rather than a classical shadow-pattern model

The study achieved a macroscopicity value of 15.5. This metric quantifies how strictly an experiment tests the limits of quantum theory. This study has a value 10 times higher than any previous experiment. 

This new level of sensitivity presents new opportunities for nanotechnology and materials science, opening new doors for measuring properties of nanoparticles and investigating the transition of matter from individual atoms to bulk metallic solids. 

For more information: University of Vienna

Image: MUSCLE at the University of Vienna, where quantum interference of massive nanoparticles was detected. To isolate vibrations, the experiment is mounted on a table weighing several tons that floats on an air cushion. Credit: S. Pedalino / Uni Wien

Building a sustainable metals infrastructure: NIST report highlights key strategies

NIST has released a report outlining strategies to build a more efficient, sustainable, and resilient U.S. metals processing infrastructure, emphasizing the need for improved standards for recycled content and stronger supply chains for critical materials. Covering the full lifecycle—from mining and alloy design to manufacturing, reuse, and recycling—the report highlights that addressing these challenges is essential for innovation, industrial competitiveness, and national security. The findings stem from a NIST workshop held in July 2024.

“The workshop brought together a diverse group of experts from industry, academia and the policy world to take on some of the biggest challenges in the metals processing space,” said NIST materials research engineer Andrew Iams, a co-author on the report. “Meeting these challenges requires a new approach in how to source, process, use and recycle metals.”

The report covers various topics related to metals manufacturing, from new technologies for extracting and processing bulk materials, like aluminum and steel, to developing new modeling and data tools to design advanced alloys.

The report highlights the importance of critical materials, including minerals containing lithium and cobalt that are key manufacturing elements for smartphones, batteries, semiconductors and medical devices, as well as superalloys used in military hardware and jet engines.

These materials can be challenging to obtain due to limited availability and the risk of supply chain disruptions. Industries can address these issues by diversifying their supply chains with new sources, identifying substitute materials, and improving recycling methods to enable greater recirculation of existing materials.

The report also highlights the need to improve standards for metals reuse and recycling. Better standards can make the separation of metals for recycling more efficient, reducing industry costs. New certification programs can help ensure that products made with recycled content meet performance standards, which could expand the market for recycled materials.

The report highlights five strategies that would help the industry tackle these and other challenges:

  • Advance measurement science for sustainable metals manufacturing, including new separation techniques for recycling.
  • Develop the technical basis to support standards development, including the data needed to create or improve performance-based standards for highly recycled metals, such as aluminum and steel.
  • Enhance data and modeling tools for addressing supply risks and designing products for improved recyclability.
  • Promote workforce development and education by establishing training programs and creating partnerships between universities, labs and industry.
  • Convene stakeholders to establish collaborations that foster knowledge-sharing and innovation.

The NIST workshop brought together manufacturers, technology companies, researchers and other experts from all stages of the metals processing chain. NIST has a long history of convening stakeholders across industrial sectors to solve shared problems through better technology and standards.

“We are always seeking ways to help industrial partners solve tough engineering or scientific problems,” Iams said. “Part of NIST’s mission is to help keep U.S. industry competitive. We can do that by identifying promising technologies and helping to move them out of the lab so they can be implemented on an industrial scale.”

For more information: Material Challenges in Developing a Sustainable Metal Processing Infrastructure – Workshop Report

Lehigh’s Blacksmithing Club to open its first dedicated lab

Lehigh University’s Blacksmithing Club is set to move into its first dedicated lab space in Whitaker Laboratory, marking a major step for its growing community of student metalworkers. The club, supported by professor Laura Moyer and partnerships with Historic Bethlehem Museums & Sites and Lehigh Heavy Forge, offers hands-on experience in both traditional and modern metalworking. Students have also practiced basic techniques at the historic 1750 Smithy, while The Loewy Institute continues to provide education in advanced metal-forming technology.

Moyer said the team had discussed building a lab for years but wasn’t sure how to achieve it financially or where it would be located. 

With student interest rising, Moyer said the idea for the blacksmithing club started to solidify two years ago when a student — now club president Josh Swavely, ‘26 — mentioned his passion for blacksmithing in one of Misiolek’s classes.

Under the guidance of Moyer and Misiolek, the group helped launch a one-credit blacksmithing elective offered each spring. Moyer said this course, now in its second year, is open to students of all majors, as is the club. 

Moyer also said they are hoping to grow the one-credit course that is currently offered for half a semester into a full-semester three-credit elective for students. 

“Within the course we are developing, the idea is to balance time between the laboratory and the lecture hall, between practice and theory,” Misiolek said. “It will be much more of an opportunity to design your own products, analyze the proposed processes, and learn while you are going through the process.”

Moyer said the new laboratory will allow students to complete every stage of the design cycle in one place — from heating and shaping metal at the forge to examining the microstructure of their finished work.

She said students will also be able to fabricate hooks, blades and small hardware entirely on campus supported by new ventilation hoods, anvils and space for larger tools.

Misiolek said the club’s growth has been driven by access to working forges across Bethlehem. Through its partnership with Historic Bethlehem Museums & Sites, students learn centuries-old techniques at the 1750 Smithy.

He also said their work with the Lehigh Heavy Forge in Bethlehem allows students to witness a modern industrial facility that provides advanced forgings to clients.

“We are connecting the historical aspects of forging technology with hands-on experience and current industrial practices,” Misiolek said. 

He also said beyond traditional forging skills, the new lab will support lessons on heat treatments, controlled cooling and other methods that help students compare how different variables affect the strength and performance of their designs.

Nick Rockwell, a researcher at the Loewy Institute, said community partnerships have been essential in turning the club’s ideas into practice. 

“With all the support we’ve received, it’s really helped us connect with Historic Bethlehem,” he said. 

He also said Mike Rex, the shop and laboratory operations supervisor, has been instrumental in building facilities for the lab and giving advice on how to best use the space.

The club also gives students opportunities beyond the classroom. Members have participated in the Forging Industry Educational and Research Foundation’s annual competition and secured funding from the nonprofit organization to support their work.

“We pride ourselves in our hands-on approach,” Moyer said. “There are a lot of materials science and engineering departments across the country, but many don’t allow undergraduate students into labs or use highly specialized equipment.”

For more information: Lehigh University

Image: Josh Swavely, ’26, is pictured forging a point in the soon to be blacksmithing lab. Swavely is the president of the Lehigh Blacksmithing Club. (Max Randall/B&W Staff)

Mapping the future: AI method to transform alloy properties prediction and design

Researchers at The Grainger College of Engineering have integrated their expertise in metals with advanced machine learning to create detailed spatial maps, enabling faster and more precise autonomous material design. Similar to how fingerprint technology captures intricate ridge and valley patterns for biometric identification, their approach leverages spatial mapping of fine details to revolutionize material engineering.

This evolution of recognition technology is mirrored in the field of materials science, where researchers seek new and efficient ways to fully characterize materials, accelerating the discovery of additional new materials. Much like human fingerprints, the performance of metal mixtures called alloys relies on the intricate spatial arrangement of microstructural features. Traditional methods reduce this complexity into a handful of averaged values, causing each alloy to lose its distinctive “fingerprint.”

 In a recent complement of papers from the lab of Jean-Charles Stinville, assistant professor of materials science and engineering, Illinois Grainger engineers have introduced new machine learning approaches for identifying alloy microstructures and predicting their properties rapidly. The Illinois researchers’ method will provide new avenues for faster and more efficient materials design. 

Microstructures are tiny structural features of metals that influence their strength and behavior. Scientists look to the microstructural properties of metals to assess their functionality. Metals used in propulsion devices like rockets and airplanes have special requirements. 

“We are sending these materials into increasingly extreme environments,” Stinville said. “They are exposed to intense environments; for instance, structural materials for space applications must be resistant to mechanical loading under extremely low or high temperatures. Conventional alloys don’t do as well in these conditions because their mechanical properties tend to degrade under these extreme environments. We want to find new ways to accelerate the identification of alloy chemistries and microstructures that can withstand these harsh conditions.”

The complete details of these microstructures, including small-scale influential variances called heterogeneities, cannot be easily captured by existing methods. Instead, Stinville and his colleagues used deep learning to analyze diffraction patterns, or the way electrons interact with metals. By encoding these interactions through a machine learning method onto a spatial latent representation, the researchers captured the full extent of an alloy’s microstructure and its heterogeneity — an approach Stinville calls Material Spatial Intelligence.

“Traditionally, we have used single descriptors or average values to guide data-based alloy design,” he said. “But spatial information from local measurements over a large field of view allows us to capture microstructure heterogeneity of the alloy. Using such spatial information in a data-based model provides significant improvement in prediction accuracy and enables alloy and microstructure design.” 

The initial model is a machine learning approach that successfully identified microstructures and material heterogeneity in unprecedented detail. In a second paper published in Scripta Materialia, Stinville further progressed the model towards the prediction of mechanical properties using the developed approach of material spatial intelligence. This method accelerates alloy property prediction by orders of magnitude and provides a rapid fundamental understanding of structure properties in metals. 

“I started my career as an experimentalist, where I developed tools that allowed us to collect large fields of view with very high resolution,” he said. “Then I went over to the numerical side to develop machine learning tools to actually use all this spatial information. As a metallurgist, I have an understanding that metals are controlled by local properties and their heterogeneities. My unique material scientist background really helped me in developing these novel models.”

By combining high-resolution digital image correlation with alloy microstructure characterization, Stinville examined tiny regions of metal surfaces and how they deformed at a small scale when loaded. Training a new model to recognize these deformation fingerprints allowed him to reliably predict important properties like strength, fatigue life, and ductility (the ability to extend without breaking). The model significantly decreases the time for testing, lessening the time needed to evaluate new alloys. This acceleration brings the field one step closer to intelligent alloy design.

Stinville envisions a future model that works backwards from a user’s desired properties to suggest a chemical composition and microstructure that best suits the given parameters. By integrating these approaches with his group’s advances in automated characterization, Stinville’s lab is setting the stage for fully autonomous alloy design, marking their next frontier.

But even as exciting advancements loom, Stinville still marvels at his field’s early beginnings. 

“This approach unites our field’s fundamental understanding of metals with new and efficient AI database tools,” he said. “We’re not just taking these new tools and leaving behind what we’ve already learned. We’re integrating the present with the past.” 

Mathieu Calvat, Chris Bean and Dhruv Anjaria significantly contributed to this research.

For more information: NPJ Computational Materials

Image: Electron backscatter diffraction (EBSD) maps of the investigated Inconel 718 alloys. Inverse pole figure (IPF) maps along the X direction (horizontal) are presented for a A wrought and fully recrystallized 718 alloy, and a B additively manufactured as-built 718 alloy

Study shows light can reshape atom-thin semiconductors for next-generation optical devices

Rice University researchers have discovered that light can induce a physical shift in the atomic lattice of transition metal dichalcogenides (TMDs), a class of atom-thin semiconductors. This tunable effect, observed in a Janus-type TMD, opens the door to technologies that use light instead of electricity, enabling faster, cooler computer chips, ultrasensitive sensors, and flexible optoelectronic devices.

“In nonlinear optics, light can be reshaped to create new colors, faster pulses or optical switches that turn signals on and off,” said Kunyan Zhang, a Rice doctoral alumna who is a first author on a study documenting the effect. “Two-dimensional materials, which are only a few atoms thick, make it possible to build these optical tools on a very small scale.”

TMDs are layered crystals made of a transition metal such as molybdenum and two layers of a chalcogen element like sulfur or selenium. Their combination of electrical conductivity, light absorption and mechanical flexibility has made them one of the most versatile classes of materials for next-generation electronics and optoelectronics.

Within this family, Janus materials stand out for their asymmetry — an idea reflected in their name. Like their mythological namesake, these materials have two different faces: The top and bottom atoms are made of different chemical species, creating an internal imbalance that gives the crystal a built-in electrical polarity, making it particularly sensitive to light and external forces.

“Our work explores how the structure of Janus materials affects their optical behavior and how light itself can generate a force in the materials,” Zhang said.

Using laser light of different colors, the team studied how a two-layer Janus TMD material — molybdenum sulfur selenide stacked on molybdenum disulfide — converts light through a process called second harmonic generation (SHG), in which the material emits light at twice the frequency of the incoming beam. They found that when the incoming light matched the material’s natural resonances, the doubled-frequency light pattern became distorted, signaling that the atoms inside were being displaced.

“We discovered that shining light on Janus molybdenum sulfur selenide and molybdenum disulfide creates tiny, directional forces inside the material, which show up as changes in its SHG pattern,” Zhang said. “Normally, the SHG signal forms a six-pointed ‘flower’ shape that mirrors the crystal’s symmetry. But when light pushes on the atoms, this symmetry breaks — the petals of the pattern shrink unevenly.”

The team traced the distortion to optostriction, a process in which the electromagnetic field of light itself exerts a mechanical push on atoms. In Janus materials, that push is amplified by strong coupling between the atomic layers, allowing even minute forces to produce measurable strain.

“Janus materials are ideal for this because their uneven composition creates an enhanced coupling between layers, which makes them more sensitive to light’s tiny forces — forces so small that it is difficult to measure directly, but we can detect them through changes in the SHG signal pattern,” Zhang said.

That sensitivity could make these materials useful far beyond the lab. Components that switch or route light using this principle could make optical chips faster and far more energy-efficient, since light-based circuits generate less heat than conventional electronics. The same responsiveness could be harnessed to create precise sensors capable of detecting the smallest vibrations or pressure changes or tunable light sources for advanced displays and imaging tools.

“Such active control could help design next-generation photonic chips, ultrasensitive detectors or quantum light sources ⎯ technologies that use light to carry and process information instead of relying on electricity,” said Shengxi Huang, an associate professor of electrical and computer engineering and materials science and nanoengineering at Rice and a corresponding author on the study. Huang is also a member of the Smalley-Curl Institute, the Rice Advanced Materials Institute and the Ken Kennedy Institute at Rice.

By showing how Janus TMDs’ built-in imbalance opens new ways to steer the flow of light, the study highlights how small structural features can unlock large technological potential.

For more information: ACS Nano

Image: Shengxi Huang is an associate professor of electrical and computer engineering and materials science and nanoengineering at Rice University. (Photo by Jeff Fitlow/Rice University)

Argonne-led Q-NEXT quantum center renewed for five years

The U.S. Department of Energy (DOE) has renewed funding for Q-NEXT, a National Quantum Information Science Research Center led by Argonne National Laboratory with SLAC National Accelerator Laboratory, for another five years. Backed by $125 million—$25 million in fiscal year 2026 and future funding subject to congressional approval—this investment ensures Q-NEXT will continue driving advancements in quantum information science and technology, reinforcing the United States’ leadership in this transformative field.

“Quantum information science is a cornerstone of the nation’s technological future, with the potential to transform industries including computing, healthcare and national security,” said inaugural Q-NEXT Director David Awschalom, who is also a senior scientist at Argonne, the director of quantum engineering at the University of Chicago Pritzker School of Molecular Engineering, and director of the Chicago Quantum Exchange. ​“Through our renewed mission, Q-NEXT will continue to push the boundaries of what is possible in quantum science, delivering the foundational knowledge, tools and technologies needed to ensure national leadership in this critical field.”

Awschalom has now assumed the role of chief science officer.

Q-NEXT’s mission is to unlock the future of quantum information by seamlessly integrating quantum and traditional information systems across optical networks. Building on its achievements over the past five years, the center will focus on demonstrating the potential of distributed quantum entanglement — a phenomenon where qubits, the fundamental unit of quantum information, remain connected even when separated by large distances.

“We’re building on the strong foundation we’ve laid over the past five years to take on our renewed mission — harnessing distributed entanglement to show what’s possible with scalable quantum platforms,” said Q-NEXT Director and Argonne scientist Martin Holt. ​“By uniting quantum technologies across optical networks, we will pave the way for systems capable of revolutionizing how we process, transmit and receive information.”

Q-NEXT brings together a strong network of partners: two DOE national laboratories, 11 leading universities and six tech companies. This cross-sector collaboration ensures that the center’s work is both innovative and practical, bridging the gap between scientific discovery and real-world application. Universities contribute expertise in quantum sensing and communication, while industry partners provide access to state-of-the-art prototypes and manufacturing capabilities. Together, Q-NEXT partners form a vibrant ecosystem that drives progress in quantum science.

“We envision quantum systems that work across chip-to-chip, lab-to-lab and city-to-city scales,” said Q-NEXT Deputy Director Jennifer Dionne, who is a professor of materials science and engineering and, by courtesy, of radiology at Stanford University and SLAC. ​“We’re excited to advance a shared set of hardware, software and protocols to make quantum networks and sensors efficient and practical.”

Q-NEXT’s renewed efforts will focus on three scientific goals:

  1. Communication — developing quantum communication networks that link devices across metropolitan areas. Q-NEXT aims to demonstrate algorithms that run across multiple remote, connected quantum processors.
  2. Sensing — using quantum entanglement to achieve unprecedented precision in sensing applications. Q-NEXT will demonstrate real-world uses, such as in medicine and navigation, as well as foundational scientific discoveries in gravitation and quantum mechanics where quantum entanglement gives a clear advantage in sensing and measurement.
  3. Materials — developing new approaches to integrating materials that can be scaled for industry use. Q-NEXT will tackle the most important challenges involved in merging distinct quantum materials systems with advanced functionality and integrating them into practical quantum devices.

Q-NEXT will pursue its goals through large-scale, team-based projects that combine materials science, device engineering and quantum physics theory. By leveraging world-class science facilities, Q-NEXT aims to deliver breakthroughs that will shape the future of quantum technology. These facilities include the Argonne and SLAC Quantum Foundries, the Stanford Synchrotron Radiation Lightsource and several DOE Office of Science user facilities: Argonne’s Advanced Photon Source and Center for Nanoscale Materials, the Argonne Leadership Computing Facility and SLAC’s Linac Coherent Light Source.

In addition to its scientific mission, Q-NEXT will continue to build the next generation of quantum scientists, engineers, technicians and other professionals. Through educational programs, internships and training opportunities such as the DOE Science Undergraduate Laboratory Internship and the Open Quantum Initiative Undergraduate Fellowship, the center is preparing students, early-career researchers and operations specialists to thrive in the rapidly growing quantum industry.

“By fostering a skilled workforce, Q-NEXT is ensuring that the U.S. remains at the forefront of quantum innovation,” Awschalom said.

Q-NEXT was established in 2020, and in its first five years, it led the establishment of the Argonne and SLAC Quantum Foundries. Together these two national facilities contribute to a robust supply chain of standardized materials and devices.

“We’ve built an active, cross-disciplinary collaboration that’s laid the groundwork for networked quantum information by intertwining computing, sensing and communication,” Holt said. ​“Over the next five years, we’ll strengthen these efforts, strategically coordinating with the other NQISRCs and the national quantum ecosystem to accelerate the arrival of transformative quantum technologies.”

Collaboration with industry has been essential to the center’s mission — turning cutting-edge research into innovations that can transform technology and strengthen the nation’s quantum leadership.

“IBM’s work with the National Quantum Information Science Research Centers, such as Q-NEXT led by Argonne, is vital to our mission to build the future of computing,” said Jay Gambetta, director of IBM Research and IBM Fellow. IBM is a Q-NEXT partner. ​“Together, we are exploring how efficient quantum networks can be created through optical links connected to IBM’s quantum networking units. This could deliver the fundamental technology needed to link the multiple, interconnected fault-tolerant quantum computers of the future over kilometer distances, allowing the convergence of quantum computation and communication within a future quantum computing internet that could revolutionize scientific and industrial discovery.”

Q-NEXT is one of five DOE National Quantum Information Science Research Centers renewed for another five years.

“Quantum technologies are driving innovations across society, and our laboratory is focusing on delivering science breakthroughs to accelerate these innovations,” said Argonne Director Paul Kearns. ​“With a renewed Q-NEXT, we will continue to play an integral role in the national quantum ecosystem through coordinated, complementary efforts with DOE’s other quantum research centers. We are committed to realizing the promise of quantum information to build a more connected world and shape a future of scientific progress that strengthens our nation’s security, prosperity and technological leadership.”

For more information: Argonne National Laboratory

Image: The DOE has renewed the Q-NEXT quantum center for five years with a $125 million investment. One of Q-NEXT’s major first-run accomplishments was the establishment of two national quantum foundries — one at Argonne (pictured) and one at SLAC. (Image by Argonne National Laboratory.)

New ORNL aluminum alloy to strengthen domestic auto supply chain

Over the next decade, large amounts of aluminum auto body scrap will enter salvage systems, but its impurities have traditionally limited reuse in critical automotive parts. Researchers at the Department of Energy’s Oak Ridge National Laboratory (ORNL) have addressed this challenge by developing RidgeAlloy, an innovative aluminum alloy that transforms low-value scrap into high-quality material for structural vehicle components. Produced by remelting and recasting post-consumer aluminum, RidgeAlloy meets strength, ductility, and crashworthiness standards, creating a sustainable domestic supply chain. This breakthrough supports DOE’s critical materials goals, as aluminum is essential for energy technologies that produce, transmit, store, and conserve energy.

“The team advanced from a paper concept to a successful, full-scale part demonstration of a new alloy in only 15 months,” said Allen Haynes, director of ORNL’s Light Metals Core Program. “That’s an unheard-of pace of innovation in developing complex structural alloys.” 

Aluminum-intensive vehicles entered the U.S. market around 2015, with Ford’s F-150 truck series among the first to be mass produced. By the early 2030s, many of these vehicles are projected to reach end-of-life, creating a surge of high-quality aluminum body sheet scrap — up to 350,000 tons annually in North America. Much of this sheet scrap is expected to be downcycled into low-grade castings or exported, which is a missed opportunity to use those resources as a source of high-quality domestic aluminum. 

“You can repurpose post-consumer aluminum into something non-structural like engine blocks,” said Alex Plotkowski, ORNL group leader of Computational Coupled Physics. “But it won’t have the properties needed for higher value, structurally sound body applications.” 

That’s because the vehicle shredding process introduces impurities, such as iron, from various parts, including fasteners like rivets. This makes the scrap chemistry too unpredictable and low performing for commercial automotive structural alloys. As a result, most lightweight parts are made using primary aluminum, which is produced from raw ore in an energy-intensive process. 

While primary aluminum is mostly imported, the U.S. has some of the world’s best infrastructure for vehicle shredding and aluminum scrap recovery. 

“Using remelted scrap instead of primary aluminum is estimated to result in up to 95% reduction in the energy needed for processing a part,” said Amit Shyam, leader of ORNL’s Alloy Behavior and Design Group. 

To make that possible, the team applied world-class scientific tools such as high-throughput computing, which involved more than two million calculations to predict the optimal alloy compositions with targeted properties, as well as materials characterization and neutron diffraction at ORNL’s Spallation Neutron Source, a DOE Office of Science user facility. These tools helped the researchers understand how specific impurities affect alloy behavior. Neutrons are uniquely suited for this kind of research because they can penetrate deep into dense metals without damaging the material, allowing scientists to observe internal structures and atomic-scale changes. 

After pinpointing the desired blend through rapid computational predictions and laboratory trials, the new alloy was tested in a real-world environment. PSW Group’s Trialco Aluminum in Chicago supplied recycled aluminum ingots, metal blocks ready for remelting, cast from mixed auto body sheet scrap and tailored to RidgeAlloy’s specifications. The ingots were shipped to Falcon Lakeside Manufacturing in Michigan, where they were successfully cast into automotive parts using high-pressure die-casting. 

“The part we chose was medium-sized and moderately complex,” Plotkowski said. “The ultimate goal is to eventually cast larger parts, perhaps even automotive giga-castings, but this is the first step.” 

The cast parts confirmed that RidgeAlloy, consisting of aluminum, magnesium, silicon, iron and manganese, had the combination of properties necessary for structural vehicle castings, even when made from recycled blends with higher iron and silicon content. It delivers strength, corrosion resistance and ductility, enabling the production of structural castings of underbodies, frame components and other critical parts from post-consumer aluminum scrap. This breakthrough offers the opportunity to reshape the value equation of how North American auto body sheet scrap is sorted and reused.

“This team figured out how to take full advantage of a national lab’s world-class suite of capabilities to rapidly fill a huge gap in our understanding of lightweight automotive materials,” Haynes said. 

By the early 2030s, RidgeAlloy could enable recycled structural castings at volumes equal to at least half of the annual primary aluminum production in the U.S. This would reduce energy use, cut costs and strengthen domestic supply chains.

“RidgeAlloy offers the first technology capable of recapturing the value of a fast-approaching and historically massive wave of domestic, high-quality recycled automotive aluminum sheet alloys,” Haynes said. “That’s the big picture supply chain impact our team aimed for.”

There is also potential for future applications in industrial machinery, agricultural equipment, aerospace, mobile power generation equipment, off-road vehicles such as snowmobiles, motorcycles, and marine vehicles including jet skis.

For more information: Oak Ridge National Laboratory

Image: This automotive part was manufactured from RidgeAlloy, a new structural alloy developed by researchers at ORNL. It was cast using metals recycled entirely from post-consumer aluminum auto body sheets. Credit: ORNL, U.S. Dept. of Energy

Scientists turn common semiconductor into a superconductor

Researchers have long sought to make semiconductors—essential for computer chips and solar cells—function as superconductors, which can carry electricity without resistance for faster, more efficient performance. This has been difficult because it requires a precise atomic structure that enables free electron movement. Now, an international team has achieved a breakthrough by creating a form of germanium that exhibits superconductivity, allowing electric currents to flow indefinitely without energy loss. This advancement could significantly enhance electronic and quantum devices while reducing power consumption.

“Establishing superconductivity in germanium, which is already widely used in computer chips and fiber optics, can potentially revolutionize scores of consumer products and industrial technologies,” explains Javad Shabani, a physicist at New York University and director of its Center of Quantum Information Physics and Quantum Institute.

Peter Jacobson, a physicist at the University of Queensland, adds that the findings could accelerate progress in building practical quantum systems. “These materials could underpin future quantum circuits, sensors, and low-power cryogenic electronics, all of which need clean interfaces between superconducting and semiconducting regions,” he says. “Germanium is already a workhorse material for advanced semiconductor technologies, so by showing it can also become superconducting under controlled growth conditions there’s now potential for scalable, foundry-ready quantum devices.”

Germanium and silicon, both group IV elements with diamond-like crystal structures, occupy a unique position between metals and insulators. Their versatility and durability make them central to modern manufacturing. To induce superconductivity in such elements, scientists must carefully alter their atomic structure to increase the number of electrons available for conduction. These electrons then pair up and move through the material without resistance — a process that is notoriously difficult to fine-tune on the atomic scale.

In the new study, researchers developed germanium films heavily infused with gallium, a softer element commonly used in electronics. This technique, known as “doping,” has long been used to modify a semiconductor’s electrical behavior. Normally, high levels of gallium destabilize the crystal, preventing superconductivity.

The team overcame this limitation using advanced X-ray methods to guide a refined process that encourages gallium atoms to take the place of germanium atoms in the crystal lattice. Although this substitution slightly distorts the crystal, it preserves its overall stability and allows it to carry current with zero resistance at 3.5 Kelvin (about -453 degrees Fahrenheit), confirming that it had become superconducting.

“Rather than ion implantation, molecular beam epitaxy was used to precisely incorporate gallium atoms into the germanium’s crystal lattice,” says Julian Steele, a physicist at the University of Queensland and a co-author of the study. “Using epitaxy — growing thin crystal layers — means we can finally achieve the structural precision needed to understand and control how superconductivity emerges in these materials.”

As Shabani notes, “This works because group IV elements don’t naturally superconduct under normal conditions, but modifying their crystal structure enables the formation of electron pairings that allow superconductivity.”

For more information: Nature Nanotechnology

Image: Josephson junction structures—quantum devices made of two superconductors and a thin non-superconducting barrier—using different forms of germanium (Ge): super-Ge (in gold), semiconducting Ge (in blue), and super-Ge on wafer-level scale. Millions of Josephson junction pixels (10 micrometer square) can be created with this new material stack on wafer scale. Inset shows crystalline form of Super-Ge on the same matrix of semiconductor Ge, a key for crystalline Josephson junction. Credit: Patrick Strohbeen/NYU

Checking the quality of materials just got easier with a new AI tool

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.

Physics-based machine learning could unlock better 3D-printed materials

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

New model reveals the hidden structure of everyday materials

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 create a paper-thin light that glows like the sun

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

Rice researchers create novel metamaterial that can potentially revolutionize implantable, ingestible devices

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).

US scientists bring quantum-level accuracy to molecular modeling, sharpen predictions

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.

AI-powered approach simplifies exploration of complex materials

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)