Researchers at Oak Ridge National Laboratory, Oak Ridge, Tenn., modified a commercial atomic force microscope with artificial intelligence to assemble and detect patterns in bismuth ferrite and analyze defects at the materials’ surface, advancing the understanding of these materials and enabling innovative data storage and computation methods.
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Researchers at Manipal Institute of Technology, India, developed a machine learning-based approach to detect faults in ball bearings through acoustic signals, proving that this innovative methodology could revolutionize predictive maintenance strategies.
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The Industrial Metrology Business Unit of Nikon Corporation, Japan, has enhanced its long-trusted AutoMeasure software—used for over 30 years with Nikon’s NEXIV video measuring machines—by adding automated edge finding to make precise dimensional measurement programming even more efficient.
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Intertek, UK, a leading Total Quality Assurance provider to industries worldwide, announced the opening of a new Intertek Caleb Brett laboratory in Naples, Italy, strengthening its presence in this important region and addressing a growing demand for reliable, high-quality testing services in crude oil, refined fuels, and emerging biofuels.
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Tescan, Czech Republic, announces that Carlyle, together with the other shareholders, has entered into a definitive agreement with Shimadzu Corporation, Japan, for the sale of the company, resulting in a change in Tescan’s shareholding structure.
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Researchers at Lawrence Livermore National Laboratory (LLNL) and their collaborators conducted experiments with gold to learn more about the unexpected structures and properties it would adopt under high pressure. The results, which show gold switching structure at 10 million times the Earth’s atmospheric pressure, are essential for planetary modeling and fusion science.
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New research shows that diamond—although known for being the best natural heat conductor on Earth—at the atomic scale it can briefly trap heat in unexpected ways. The findings could influence how scientists design diamond-based quantum technologies, including ultra-precise sensors and future quantum computers.
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3D-Micromac AG, Germany, introduced microPREP L, a laser-based sample prep system that supports up to whole 12-inch wafers and system-level boards, enabling fast, precise, reproducible, and non-destructive material removal for semiconductor failure analysis and quality assurance.
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A research team led by National Institute for Materials Science, Japan, has, for the first time, produced nanoscale images of two key features in an ultra-thin material: twist domains (areas where one atomic layer is slightly rotated relative to another) and polarities (differences in atomic orientation) by combining scanning transmission electron microscopy with artificial intelligence.
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By studying the theoretical limits of how light can be used to perform computation, Cornell researchers have uncovered new insights and strategies for designing energy-efficient optical computing systems.
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A research team from Tokyo University of Science, Japan, has developed an automated artificial intelligence-based approach for analyzing X-ray absorption spectroscopy data to establish a clear and objective link between a material’s spectral data and its underlying material properties.
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Materials scientists at the University of Minnesota Twin Cities have found a way to create and control tiny internal “flaws” inside ultra-thin materials known as extended defects, that could give next-generation nanomaterials entirely new properties, opening the door to advances in nanotechnology.
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Hitachi High-Tech Corporation, Japan, has launched the Ultrahigh-Resolution Scanning Electron Microscope SU9600, which allows for highly accurate and precise observation of substances down to the sub-nano level.
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Photothermal Spectroscopy Corp., Santa Barbara, Calif., released stRAMos, a next-generation Raman microscope based on photothermal detection of Stimulated Raman scattering that delivers unmatched speed, sensitivity, spatial resolution, and multimodal imaging capabilities for life science and materials research.
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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:
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 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)
Designing hypersonic aircraft that travel at five to seven times the speed of sound is a major challenge because their materials must be lightweight yet capable of withstanding extreme heat and pressure. To address this, researchers at Embry-Riddle Aeronautical University, in collaboration with the U.S. Department of Energy’s Argonne National Laboratory, are developing a device that simulates the intense thermal and mechanical stresses of hypersonic flight. Paired with the ultrabright X-rays of Argonne’s Advanced Photon Source, this system will allow scientists to observe real-time changes in these materials under flight-like conditions.
“Recreating the environment of hypersonic flight can be complicated,” said Seetha Raghavan, professor of aerospace engineering and a co-principal investigator on the project. “There are so many factors and no perfect way to test them all. High enthalpy wind tunnels that can simulate the wind speed use a lot of energy resources and are limited in access.” (Enthalpy refers to the heat content of a system at constant pressure.)
The research team’s goal is an alternative that replicates hypersonic flight conditions using fewer energy resources and uses APS X-rays to capture detailed data. The APS is in the final stages of an upgrade that increased the brightness of its X-ray beams by up to 500 times. It is now the brightest synchrotron X-ray facility in the world, and according to Raghavan, the enhanced capabilities of the upgraded APS are crucial to this project.
“When you are talking about hypersonics, you’re talking about high speeds and fast changes, and response time is critical,” she said. “You can only get that kind of time resolution with enough flux (or brightness of the beam), and the upgraded APS is able to help with that.
“Additionally, the materials we’ll be testing are the thinnest that can be used, and at the upgraded APS you can focus the beam down to a small enough size to capture the data we need,” she said.
Victoria Cooley, an APS beamline scientist who worked with the Embry-Riddle team at beamline 1-ID, touted both the upgraded X-ray beam and the improved experiment station.
“It’s an exciting time for our beamline,” she said. “Brighter X-rays allow us to probe deep into materials with a higher-resolution beam and map very thin samples like these. At the same time, we have installed faster, more sensitive detectors to capture chemical or crystallographic changes occurring incredibly quickly. These two pieces come together to make world-changing projects such as this one possible.”
Uncovering materials that can withstand the conditions of hypersonic flight and are not prohibitively expensive to produce is key to unlocking their many applications. Durable hypersonic materials could be used for military and civilian aircraft, as well as cargo delivery vehicles.
The hypersonic materials project is supported by a $1.4 million contract from the U.S. Department of War Joint Hypersonics Transition Office through the University Consortium for Applied Hypersonics. The Embry-Riddle team’s principal investigator is William Engblom, professor of aerospace engineering, and Mark Ricklick, associate professor of aerospace engineering, is a co-principal investigator.
For more information: Embry-Riddle
Image: The Embry-Riddle research team at Beamline 1-ID, with Argonne scientist Victoria Cooley (back row, right). (Image by Mark Lopez/Argonne National Laboratory.)
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
Microscopy images are indispensable in nanomaterials science. Yet scientists now fear that generative AI is diluting the significance of these images by polluting the pool with fake, AI-generated photos that are indistinguishable from the real ones. Even seasoned researchers find it increasingly difficult to distinguish between real microscopy images of nanomaterials and those created by AI as shown in a new study.
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Researchers from the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) have discovered a new way to produce high-entropy alloys (HEAs), at near-room temperatures. Their technique gives users much more control of the alloy’s crystal structure and overall morphology compared with existing methods, opening the door for a new paradigm of custom-made HEAs.
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Sandia National Labs researchers have created a new dataset on the rates and types of rooftop photovoltaic connector failures and published the first large-scale investigation of harvested PV connectors, drawing from a dataset of 6276 connectors from residential rooftop solar systems across the United States.
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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)
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:
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.)
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
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
Shimadzu Corporation, Japan, launched the inspeXio 7000 microfocus X-ray CT system, a high-end industrial model that builds on the high-resolution, high-contrast imaging capabilities of its predecessor while offering improved image quality through new scanning modes and a larger maximum sample capacity.
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Researchers at the Massachusetts Institute of Technology (MIT), Cambridge, have developed a new way to probe inside an atom’s nucleus, using the atom’s own electrons as “messengers” within a molecule.
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Scientists at Lawrence Livermore National Laboratory (LLNL) now have a new technique in their toolkit for nuclear forensics examinations, which is the art and science of extracting information about the provenance and history of nuclear materials.
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Advancing technologies like batteries, electronics, and pharmaceuticals relies on discovering and verifying new materials, a process traditionally slowed by costly, time-consuming quality checks using specialized instruments. While AI has accelerated material discovery by identifying promising candidates from vast databases, MIT engineers have now developed a new AI tool that streamlines the verification process, potentially reducing delays and costs in materials-driven industries.
In a new study the researchers present “SpectroGen,” a generative AI tool that turbocharges scanning capabilities by serving as a virtual spectrometer. The tool takes in “spectra,” or measurements of a material in one scanning modality, such as infrared, and generates what that material’s spectra would look like if it were scanned in an entirely different modality, such as X-ray. The AI-generated spectral results match, with 99 percent accuracy, the results obtained from physically scanning the material with the new instrument.
Certain spectroscopic modalities reveal specific properties in a material: Infrared reveals a material’s molecular groups, while X-ray diffraction visualizes the material’s crystal structures, and Raman scattering illuminates a material’s molecular vibrations. Each of these properties is essential in gauging a material’s quality and typically requires tedious workflows on multiple expensive and distinct instruments to measure.
With SpectroGen, the researchers envision that a diversity of measurements can be made using a single and cheaper physical scope. For instance, a manufacturing line could carry out quality control of materials by scanning them with a single infrared camera. Those infrared spectra could then be fed into SpectroGen to automatically generate the material’s X-ray spectra, without the factory having to house and operate a separate, often more expensive X-ray-scanning laboratory.
The new AI tool generates spectra in less than one minute, a thousand times faster compared to traditional approaches that can take several hours to days to measure and validate.
“We think that you don’t have to do the physical measurements in all the modalities you need, but perhaps just in a single, simple, and cheap modality,” says study lead Loza Tadesse, assistant professor of mechanical engineering at MIT. “Then you can use SpectroGen to generate the rest. And this could improve productivity, efficiency, and quality of manufacturing.”
The study was led by Tadesse, with former MIT postdoc Yanmin Zhu serving as first author.
Tadesse’s interdisciplinary group at MIT pioneers technologies that advance human and planetary health, developing innovations for applications ranging from rapid disease diagnostics to sustainable agriculture.
“Diagnosing diseases, and material analysis in general, usually involves scanning samples and collecting spectra in different modalities, with different instruments that are bulky and expensive and that you might not all find in one lab,” Tadesse says. “So, we were brainstorming about how to miniaturize all this equipment and how to streamline the experimental pipeline.”
Zhu noted the increasing use of generative AI tools for discovering new materials and drug candidates, and wondered whether AI could also be harnessed to generate spectral data. In other words, could AI act as a virtual spectrometer?
A spectroscope probes a material’s properties by sending light of a certain wavelength into the material. That light causes molecular bonds in the material to vibrate in ways that scatter the light back out to the scope, where the light is recorded as a pattern of waves, or spectra, that can then be read as a signature of the material’s structure.
For AI to generate spectral data, the conventional approach would involve training an algorithm to recognize connections between physical atoms and features in a material, and the spectra they produce. Given the complexity of molecular structures within just one material, Tadesse says such an approach can quickly become intractable.
“Doing this even for just one material is impossible,” she says. “So, we thought, is there another way to interpret spectra?”
The team found an answer with math. They realized that a spectral pattern, which is a sequence of waveforms, can be represented mathematically. For instance, a spectrum that contains a series of bell curves is known as a “Gaussian” distribution, which is associated with a certain mathematical expression, compared to a series of narrower waves, known as a “Lorentzian” distribution, that is described by a separate, distinct algorithm. And as it turns out, for most materials infrared spectra characteristically contain more Lorentzian waveforms, while Raman spectra are more Gaussian, and X-ray spectra is a mix of the two.
Tadesse and Zhu worked this mathematical interpretation of spectral data into an algorithm that they then incorporated into a generative AI model.
“It’s a physics-savvy generative AI that understands what spectra are,” Tadesse says. “And the key novelty is, we interpreted spectra not as how it comes about from chemicals and bonds, but that it is actually math — curves and graphs, which an AI tool can understand and interpret.”
The team demonstrated their SpectroGen AI tool on a large, publicly available dataset of over 6,000 mineral samples. Each sample includes information on the mineral’s properties, such as its elemental composition and crystal structure. Many samples in the dataset also include spectral data in different modalities, such as X-ray, Raman, and infrared. Of these samples, the team fed several hundred to SpectroGen, in a process that trained the AI tool, also known as a neural network, to learn correlations between a mineral’s different spectral modalities. This training enabled SpectroGen to take in spectra of a material in one modality, such as in infrared, and generate what a spectra in a totally different modality, such as X-ray, should look like.
Once they trained the AI tool, the researchers fed SpectroGen spectra from a mineral in the dataset that was not included in the training process. They asked the tool to generate a spectra in a different modality, based on this “new” spectra. The AI-generated spectra, they found, was a close match to the mineral’s real spectra, which was originally recorded by a physical instrument. The researchers carried out similar tests with a number of other minerals and found that the AI tool quickly generated spectra, with 99 percent correlation.
“We can feed spectral data into the network and can get another totally different kind of spectral data, with very high accuracy, in less than a minute,” Zhu says.
The team says that SpectroGen can generate spectra for any type of mineral. In a manufacturing setting, for instance, mineral-based materials that are used to make semiconductors and battery technologies could first be quickly scanned by an infrared laser. The spectra from this infrared scanning could be fed into SpectroGen, which would then generate a spectra in X-ray, which operators or a multiagent AI platform can check to assess the material’s quality.
“I think of it as having an agent or co-pilot, supporting researchers, technicians, pipelines and industry,” Tadesse says. “We plan to customize this for different industries’ needs.”
The team is exploring ways to adapt the AI tool for disease diagnostics, and for agricultural monitoring through an upcoming project funded by Google. Tadesse is also advancing the technology to the field through a new startup and envisions making SpectroGen available for a wide range of sectors, from pharmaceuticals to semiconductors to defense.
For more information: Matter
Image: The circle with the chip symbolizes SpectroGen, with the connecting threads depicting the process of generating a material’s spectrum.
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
