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
Scientists have created an ultra-thin, paper-like LED that emits a warm, sunlike glow by precisely blending red, yellow-green, and blue quantum dots to mimic natural sunlight. This breakthrough offers improved color accuracy and reduced eye strain, with potential applications in home lighting, electronic displays, and workspaces. Unlike traditional bulky bulbs, this slender design could transform how we light our environments while helping minimize sleep disruption from harsh artificial light.
“This work demonstrates the feasibility of ultra-thin, large-area quantum dot LEDs that closely match the solar spectrum,” says Xianghua Wang, a corresponding author of the study. “These devices could enable next-generation eye-friendly displays, adaptive indoor lighting, and even wavelength-tunable sources for horticulture or well-being applications.”
Many people prefer indoor lighting that feels natural and soothing. Earlier approaches achieved this effect with flexible LEDs that used red and yellow phosphorescent dyes to create a candle-like warmth. A newer alternative relies on quantum dots—tiny semiconductor particles that transform electrical energy into colored light. Some research teams have already used quantum dots to make white LEDs, but replicating the complete spectrum of sunlight has remained difficult, particularly in the yellow and green regions where sunlight is strongest. To address this challenge, Lei Chen and colleagues developed quantum dots that could recreate that balanced, sunlike glow in a thin, white quantum dot LED (QLED). Meanwhile, Wang’s group proposed an efficient conductive material design that could operate effectively at relatively low voltages.
The team began by synthesizing red, yellow-green, and blue quantum dots coated with zinc-sulfur shells. They determined the precise color ratio needed to match the spectrum of natural sunlight as closely as possible. Next, they assembled the QLED on an indium tin oxide glass substrate, layering conductive polymers, the quantum dot blend, metal oxide particles, and finally a top coating of aluminum or silver. The quantum dot layer measured only a few dozen nanometers in thickness—much thinner than standard color conversion layers—resulting in a white QLED with an overall profile comparable to wallpaper.
In initial tests, the thin QLED performed best under a 11.5-volt (V) power supply, giving off the maxmium bright, warm white light. The emitted light had more intensity in red wavelengths and less intensity in blue wavelengths, which is better for sleep and eye health, according to the researchers. Objects illuminated by the QLED should appear close to their true colors, scoring over 92% on the color rendering index.
In further experiments, the researchers made 26 white QLED devices, using the same quantum dots but different electrically conductive materials to optimize the operating voltage. These light sources required only 8 V to reach maximum light output, and about 80% exceeded the target brightness for computer monitors.
For more information: ACS Applied Materials
Image: A paper-thin device uses quantum dots, similar to those described in this work, to light up LEDs. Credit: Lin Zhou, Xianghua Wang
A team led by Lawrence Berkeley National Laboratory and George Washington University have confirmed that atoms in semiconductors will arrange themselves in distinctive localized patterns that change the material’s electronic behavior.
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Oxford Instruments’ Raman imaging team in England announced a refreshed core microscope line and a groundbreaking new spectrometer, Hexalight, which together with the witec360 microscope constitute the most advanced confocal Raman imaging system yet devised.
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A team led by Rice University’s Yong Lin Kong has developed a soft yet strong metamaterial capable of rapidly changing its size and shape through remote control, marking a major step forward for ingestible and implantable medical devices. Unlike natural materials, metamaterials derive their unique properties from their physical structure rather than chemical composition. Kong’s design combines exceptional deformability with structural stability—an unprecedented feat in soft materials—allowing it to withstand compressive loads over ten times its own weight and perform reliably in extreme temperatures and harsh chemical environments.
“We programmed multistability, i.e. the ability to exist in multiple stable states, into the soft structure by incorporating geometric features such as trapezoidal supporting segments and reinforced beams,” said Kong, assistant professor of mechanical engineering at Rice’s George R. Brown School of Engineering and Computing. “These elements create an energy barrier that locks the structure into its new shape even after the external actuation force is removed.”
The metamaterial’s soft architecture helps address critical medical safety concerns such as gastric ulcers, puncture injuries and inflammation that can occur from implantable and ingestible devices made of rigid components.
Kong and his team used 3D printing to create molds that form interconnected microarchitectures of tilted beams and supporting segments. This design allows for rapid switching between open (off) and closed (on) states, and the transformed configuration is maintained even after the magnetic field is removed. By combining many such unit cells as “building blocks,” they form a 3D structure that can not only transform its shape but can also produce complex peristaltic motions to move or to deliver fluids in a controlled manner when actuated with a magnetic field.
Importantly, the material continued to function reliably even after prolonged exposure to mechanical stress and acidic corrosion, conditions that mimic the harsh environment of the human stomach.
“The metamaterial makes it possible to remotely control the size and shape of devices inside the body. This could enable lifesaving capabilities such as precisely controlling where a device stays, delivering medication where it’s needed or applying targeted mechanical forces deep inside the body,” Kong said. “We are now leveraging this metamaterial to develop ingestible systems that may one day treat obesity in humans or improve the health of marine mammals, and we are collaborating with surgeons at the Texas Medical Center to design wireless fluidic control systems to address unmet clinical needs.”
The first author of this study was Kong’s first graduate student, Taylor Greenwood, who has since graduated and started a faculty position at Brigham Young University. Kong’s other graduate students Brian Elder and Jared Anklam, postdoctoral associates Jian Teng and Saebom Lee and other collaborators were involved in the study. This research was supported by the National Institutes of Health and the Office of Naval Research.
For more information: Science Advances
Image: The new metamaterial designed by Kong and his team at Rice can be controlled remotely to rapidly transform its size and shape (Photos and video by Jorge Vidal/Rice University).
Researchers at the University of Michigan have developed a breakthrough method that brings quantum-level precision to molecular modeling, offering deeper insights into chemical reactions and material properties. This advancement addresses the quantum many-body problem—how electrons interact to form chemical bonds and influence electrical behavior—which traditionally requires immense computational power and is limited to small molecules. By enhancing the efficiency of this simulation approach, the new method could extend quantum accuracy to larger, more complex systems, potentially reducing the heavy demand on national lab supercomputers.
Density functional theory, or DFT, makes quantum chemistry more manageable by focusing on electron densities rather than tracking every electron individually. This approach keeps computing demands much lower, allowing simulations of systems with hundreds of atoms. A major challenge, however, lies in the exchange-correlation (XC) functional, which governs how electrons interact according to quantum mechanics.
Until now, researchers have had to rely on approximations of the XC functional tailored to specific applications, limiting the theory’s overall accuracy. Improving this functional is key to making DFT an even more powerful tool for chemistry and materials science.
According to Vikram Gavini, a University of Michigan professor of mechanical engineering and the corresponding author of the study, researchers know that a universal functional exists that applies to all electron systems – whether in molecules, metals, or semiconductors – but its exact form remains unknown.
Hence, understanding this functional is crucial for improving DFT, which models electron interactions and underpins simulations in chemistry and materials science.
Given DFT’s central role in advancing both materials research and basic science, the US Department of Energy provided funding and supercomputer resources to support the University of Michigan team’s efforts to approach the universal exchange-correlation functional.
The researchers began by analyzing individual atoms and small molecules using quantum many-body theory. Then, instead of applying approximate functionals to predict electron behavior, they used machine learning to determine which XC functional would reproduce the electrons’ behavior as calculated by the more precise quantum many-body method.
Bikash Kanungo, a University of Michigan assistant research scientist in mechanical engineering and first author of the study, explains that an accurate exchange-correlation functional has broad applications because it is material-agnostic.
It is equally important for researchers developing better battery materials, designing new drugs, or building quantum computers. By improving this functional, scientists can make density functional theory more reliable and widely applicable, enabling more precise simulations across chemistry, materials science, and emerging technologies.
Thus, researchers can now use the XC functional discovered by the University of Michigan team or apply their approach to new systems, starting with light atoms and molecules and eventually extending to solids, paving the way for more accurate and efficient simulations across chemistry and materials science.
For more information: University of Michigan
Image: A 3D map of the quantum potential.
Researchers at Oak Ridge National Laboratory have introduced a powerful new method for investigating the atomic-level behavior of materials by combining Bayesian deep learning—a fusion of probability theory and neural networks—with advanced data analysis. This approach enables scientists to rapidly and accurately process complex datasets, allowing them to scan broader sample areas and identify regions with critical properties far more efficiently than traditional techniques.
“This method makes it possible to study a material’s properties with much greater efficiency,” said Ganesh Narasimha from ORNL. “Usually, we would need to scan a large region, and then several small regions, and perform spectroscopy, which is very time-consuming. Here, the AI algorithm takes control and does this process automatically and intelligently.”
The team demonstrated the system using europium zinc arsenide, a magnetic semimetal with distinctive electronic traits. With the aid of scanning tunneling microscopy, the researchers uncovered links between atomic-scale structures and their electronic responses.
Although the case study focused on europium zinc arsenide, the scientists emphasize that the method is broadly applicable to many different materials. The advance not only streamlines the discovery process but also strengthens national efforts in artificial intelligence and quantum science.
For more information: Nature
Image: A scanning tunneling microscope and machine learning algorithm autonomously search for atomic structures. This image shows a vacancy defect on europium zinc arsenide. (Image Credit: Ganesh Narasimha/ORNL, U.S. Dept. of Energy)
Scientists have directly imaged a rare atomic vibration known as a phason—a phenomenon long theorized but never observed—within a twisted two-dimensional material called tungsten diselenide. These ultra-thin materials, just a few atoms thick, can be stacked in unusual configurations that produce entirely new physical behaviors. Using a cutting-edge technique called electron ptychography, researchers captured the most precise images of individual atoms to date, revealing how phasons vary with atomic arrangement and opening new possibilities for future electronic technologies.
They are only a special case of a broader category of moiré phonons, the result of when two layers of 2D material are shifted slightly one from the other. That fold between the two creates a moiré superlattice—a larger periodic structure that gives rise to unique thermal and electronic behavior. Phasons are an extremely soft, low-frequency variety of these phonons.
Even though phonons and phasons are not seen, their effect is widespread. Heat is atomic vibration in itself, and an understanding of such patterns is required if one expects to have any control over heat transfer in electronics. Greater control could imply materials that cool faster or suck heat off sensitive parts.
You can’t just get rid of phasons; that’s the blessing and curse,” said Pinshane Huang, a professor of materials science and engineering at the University of Illinois at Urbana-Champaign and lead author of the paper. “They’ve always been hiding in plain sight, changing the properties of 2D moiré materials.”
Phasons had mystified scientists for decades. They were thought to belong in theoretical models only, but no one had concrete proof. Not until Huang teamed up with Yichao Zhang, a postdoctoral researcher of nanoscale heat conduction. Their task: to freeze the blur of atomic movement created by heat.
What our primary goal was, was to see heat when looking at an atom,” Huang explained. “What this does is get so fantastic spatial resolution that vibrations of the atoms have something to do with how fuzzy the atoms appear,” she said. “These are tiny movements, and we can basically look at one atom at a time and see its heat-related motion.”.
To do this, the team utilized electron ptychography, a new imaging method with a resolution of less than 15 picometers. That is about one-thousandth the width of an individual atom. It allowed the team to see the width, shape, and movement of atoms in twisted bilayer tungsten diselenide, or WSe₂.
“Back when I started out, we figured the highest resolution you could get was maybe a little bit less than an angstrom,” Huang said. “But once ptychography arrived on the scene, we were starting to see resolution numbers as low as 0.2 angstroms,” she continued. “That put us thinking, ‘Well, heat gets atoms jigged around about 0.05 angstroms,’ she said. “Now that we can actually see heat, it shows what a giant jump in resolution can do for what microscopes can do,” she continued.
Twisted structures are important because they alter the local atomic environment in strong and unexpected manners. When one layer is twisted against another in bilayer materials such as WSe₂, there is a mismatch in the grids of atoms. That mismatch creates new areas—some with atoms close together and others not so close.
When researchers mapped these helical regions, they found more vibrations in some areas. Vibrations were especially strong around solitons, where solitons are borders between different stacking modes. AA-stacked regions, in which atoms lie atop each other, also showed more intense vibrations.
By integrating electron ptychography with lattice dynamics and molecular dynamics simulations, the scientists made a robust conclusion. Phasons were discovered by them as the main culprit behind thermal vibration in low-angle twisted bilayers. This tells us that the moiré pattern is not just an illusion—it plays a pivotal role in determining how heat traverses through the material. These findings provide new doors to investigate and even control heat in 2D materials at the atomic level.
Phasons are tiny, but their potential effect could be huge. Controlling the skill of sensing and studying these vibrations could change the way electronics are designed in the future. “One potential application of this technique is creating materials that are better heat conductors,” said Zhang. “We can look at one atom and find a defect that keeps the material from cooling more efficiently,” he said.
This could result in better means of atomic-level thermal control,” he added. “To watch individual atoms and see how they react to heat will tell us some very important things,” he said.
Twisted 2D materials are already being researched for use in transistors, sensors, and quantum computing hardware. Adding heat behavior to that research means engineers can create entire devices at the atomic level. Such devices would be able to be made smaller, faster, and much more efficient than today’s electronics.
Perhaps most exciting is the manner in which this finding reconciles theory with direct observation. Phasons were previously hypothetical, known by equations and models alone. Now, they are observable in real time.
Using equipment like electron ptychography, even the slightest motion of the atoms can be watched and measured. Science no longer needs to speculate about what atoms do—it’s watching them in plain sight, one vibration at a time.
For more information: Science
Image: (left) atoms present in the 2D material. (right) photos of single atoms. (CREDIT: The Grainger College of Engineering)
Imagic Bildverarbeitung AG and the Materials Engineering Center Saarland (MECS) of Zürich and Saarbrücken, respectively, entered into a strategic partnership in September. The collaboration combines MECS’s expertise in materials science, which bridges the gap between research and industry, with Imagic’s leading software expertise in microscopy, image analysis, and image data management.
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Researchers from University of Oxford and Brookhaven National Laboratory have uncovered how exposure to hydrogen atoms dynamically alters the internal structure of stainless steel. Their findings reveal that hydrogen allows internal defects in steel to move in ways not normally possible, which can lead to unexpected failure.
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Silicon Valley may have led the digital revolution, but Illinois is emerging as a hub for quantum innovation, dubbed the “Quantum Prairie.” Anchored by institutions like the U.S. Department of Energy’s Argonne National Laboratory, the region is attracting both tech giants and startups focused on quantum information science. Researchers are now harnessing quantum physics—first discovered over a century ago—to revolutionize fields such as computing, medicine, and finance, using atomic-scale phenomena to build ultra-sensitive sensors and simulate complex physical systems.
“Building quantum information systems requires new materials, different computing architectures, specialized devices and software. You need to look at how quantum computers will communicate with one another and also how they will connect to classical computer systems,” said Argonne Distinguished Fellow Michael Norman, director of the Argonne Quantum Institute. “At Argonne, we’re doing all of it.”
Argonne is also a key part of two federal designations: the Bloch Quantum Tech Hub, which was designated by the U.S. Economic Development Administration, and Quantum Connected, a Midwest coalition that is a National Science Foundation Regional Innovation Engine (NSF Engines) Development Awardee and a semifinalist in the national NSF Engines competition. And Argonne Laboratory Director Paul Kearns is on the Board of Managers for the Illinois Quantum and Microelectronics Park (IQMP), an in-progress campus in Chicago for quantum technology and microelectronics innovation.
The allure of Illinois for quantum tech
Building on this foundation, Illinois has announced multiple quantum technology initiatives and partnerships in an effort to make Illinois a global quantum capital. In 2024, the state and IBM announced the National Quantum Algorithm Center, which will be located at the IQMP, and quantum computing company PsiQuantum has signed on as the anchor IQMP tenant.
IQMP Executive Director and CEO Harley Johnson, who is also a professor of mechanical science and engineering at the University of Illinois Urbana-Champaign, points to the state’s research bona fides: top schools such as his employer, along with the University of Chicago and Northwestern University; two DOE national labs, Argonne and Fermi National Accelerator Laboratory, which also head up two of the five DOE National Quantum Information Science Research Centers; and a partnership with the U.S. Department of Defense, part of the larger Quantum Benchmarking Initiative (QBI), to be a “national proving ground” for quantum. Argonne is leading several of the teams vetting quantum computing companies for the QBI.
“That’s an incredible base to build on,” Johnson said. “Then, when you add the state government, nonprofits, economic development organizations and the companies that are already here, there’s a strong ‘team Illinois’ environment that comes across to potential partners. They see that we’re pulling in the same direction.”
Preeti Chalsani, chief quantum officer at the public-private economic development partnership Illinois Economic Development Corporation, said that when she participates in conferences, she hears talk of other states that want to replicate Illinois’ success in kickstarting the quantum industry.
“It’s not accidental that quantum is strong here,” Chalsani said, pointing to the state’s storied, multidisciplinary research history and assets such as Argonne’s APS and the ALCF, with its Aurora exascale computer. “Chicago and Illinois have done a really good job making researchers aware of quantum as not just a research enterprise, but a whole industry with lots of opportunities to collaborate.”
An ecosystem for quantum
The breadth of Argonne’s work in quantum information science, combined with the field’s increasing relevance, led the laboratory to create the Argonne Quantum Institute in October 2023. The institute coordinates research efforts both internally at the lab and with external partners, such as IBM, Infleqtion, Intel, Quantinuum and JPMorgan Chase. A recent article in Nature showcases the work of the latter two companies with Argonne scientists. Several scientists at the UChicago Pritzker School of Molecular Engineering, which has a core focus on quantum information research, conduct their work jointly at Argonne. The two institutions share several quantum-focused research initiatives as well.
“The fact that Argonne invests in the Quantum Foundry as a way to atomically engineer and construct materials has taken us from relying on external sources to provide materials over periods of months to performing everything inhouse within a day,” Awschalom said.
Some of the scientists working at Argonne are entrepreneurs who are building quantum companies with support from the lab’s two-year fellowship program, Chain Reaction Innovations. Founders of startups including memQ and Super.tech are Chain Reaction Innovations alumni. Quantum accelerator Duality, based at the University of Chicago and with Argonne as a founding partner, also provides funding and expertise to accepted startups. Both programs help connect entrepreneurs to the region’s larger quantum community and industry base.
“A lot of the work of startups relies on connections to research labs,” Chalsani said. “It also depends on connections to larger companies who could be strategic partners that can help with their product development or go-to-market strategy, or customers who would adopt their technologies. This work really cannot be done in isolation.”
Building the quantum workforce
The key to a quantum future is a strong workforce. Argonne invests in the next generation of quantum scientists by hosting summer research experiences for undergraduates, who receive hands-on training at the Argonne Quantum Foundry. And Q-NEXT, for which Argonne is the lead laboratory, has supported more than 150 students and postdocs over the past five years.
These initiatives address the rapidly growing demand for professionals with quantum materials and computational expertise, reinforcing Argonne’s commitment to empowering future scientific leaders.
“Quantum science won’t advance without the next generation,” Norman said. “Giving students and early-career researchers real lab experience, good mentors and a solid foundation is how we’ll keep Argonne at the forefront of this field. Our efforts here have been enabled by both Chicago Quantum Exchange’s Open Quantum Initiative program and DOE’s Science Undergraduate Laboratory Internships.”
Even though quantum information science has been progressing steadily over the past decade, specialized graduate programs have only just begun to appear within the last five years or so. The University of Chicago introduced one of the first in the nation. The explosion of investment and innovation in quantum information science is creating demand for expertise.
“One of the biggest challenges in the field is not only the science and technology, it’s scaling the workforce at all levels to meet growing demand,” Awschalom said. “How do we create a sufficient supply of quantum engineers and technicians to innovate and build quantum technologies?”
The answer is not just people with advanced schooling. More than half of quantum technology jobs do not require a graduate degree.
“Several years ago, it was mostly research scientists with Ph.D.s or postdocs who were involved in quantum,” Chalsani said. “But as the industry has matured, it requires a whole range of other talent.” That includes equipment technicians, software engineers, HVAC installers and other key roles.
Some of the technologies this workforce will support are already here, such as quantum navigation sensors. Some, like quantum computing, are further off.
And others? We don’t know what they are yet.
“History has shown that whenever there are discontinuous changes in science and technology, the biggest impacts are the ones we’re not imagining in this conversation,” Awschalom said. “The trick is, will we be ready when they arrive? Because I guarantee you, they’re going to appear — they always do.”
Engineers at the University of Wisconsin–Madison have developed a versatile new electrolyte that advances the development of an initially anode-free sodium-ion battery—a promising alternative to lithium-ion batteries for electric vehicles and grid energy storage. Led by Assistant Professor Fang Liu and PhD students Qianli Xing and Ziqi Yang, the team is also using this electrolyte as a model system to explore how molecular manipulation can improve compatibility between different battery components, potentially paving the way for more efficient and energy-dense battery technologies.
Containing solvents and dissolved salts, the electrolyte is the liquid medium that touches all parts of the battery’s cells and, in its charging or discharging process, helps ions travel between the electrodes.
In a battery, the anode and cathode are different materials—for example, graphite, hard carbon sodium or lithium for the anode and a transition metal oxide like lithium nickel manganese cobalt oxide or sodium nickel iron manganese oxide for the cathode.
One of the challenges in developing next-generation batteries is that there’s not a one-size-fits-all electrolyte that performs effectively with both electrode material types. Conversely, when an electrolyte contains multiple solvent molecules, controlling their interactions and behavior is challenging.
Tweaking the electrolyte is a balancing act involving multiple factors, including how solvent molecules in the electrolyte form a “shell” around ions that could accelerate or impede the ions’ movement between anode and cathode—which ultimately affects battery charging and discharging, along with overall battery performance. “Using this model system, we are basically trying to understand whether we can present different molecules to different electrode surfaces—for example, an anode-stable solvent to the anode, and then a cathode-stable solvent to the cathode,” says Liu. “In this way, the electrolyte mixture would ideally behave like an anode-stable solvent at the anode, and like a cathode-stable solvent at the cathode.”
To create its new electrolyte, the team mixed two ether-based solvents, 2-methyltetrahydrofuran, or 2-MeTHF, which is more stable at the anode, and tetrahydrofuran, or THF, which is more stable at the cathode. Importantly, they found a way to rationalize electrolyte design: Solvents that dominate the first shell around positively charged ions that travel between electrodes are key to anode stability, while “free” or more weakly bonded solvents are important to the stability of the cathode side. “Through this electrolyte engineering work, we were trying to demystify what determines the stability of the anode and cathode separately, and how to present suitable molecules to both electrodes,” says Liu. “Qianli found out that the key factor is the population of solvents in the first solvation shell versus outside, and their location determines their presentation during the battery formation process.”
Computational testing, conducted by collaborator Reid Van Lehn, an associate professor of chemical and biological engineering at UW-Madison, and his student Jung Min Lee, played a significant role in the research as well. They used all-atom molecular dynamics simulations to predict the composition of solvent molecules near sodium ions and determine whether those ions “preferred” one solvent over the other. “Our results indeed found—in good agreement with experiments from the Liu group—that we could identify a single strongly interacting solvent (2-MeTHF) and a weakly interacting solvent (THF),” says Van Lehn. “We further used these calculations to relate this behavior to the relative strength of interactions of each type of solvent, providing molecular-scale insight that can be extended to even more complex mixtures to continue optimizing electrolyte design.”
The research lays the groundwork for the next steps in developing not simply sodium-metal batteries, but also other new alternatives to lithium-ion batteries. “Through this research, we start to understand that the solvent and anion interactions become really important,” says Liu. “We’re trying to expand our solvent library to manipulate these kinds of interactions, to see whether this kind of working principle can be applied to broader solvent libraries and different battery chemistries.”
Now, inspired by recent advances in machine learning, Bernardi and Luo have developed an AI-based technique that sifts through the high-order tensors that encode phonon interactions in a material and extracts only the crucial bits needed to complete the calculations that explain thermal transport.
“The calculations for four-phonon interactions are a nightmare,” Bernardi says. “For complex materials, this task would involve weekslong calculations. Now we can do them in 10 seconds.”
Bernardi explains more about the method:
“We use a machine learning technique called CANDECOMP/PARAFAC tensor decomposition, but we had to adapt it to satisfy the symmetry of this specific physical problem. We first set up a neural network and then run it on GPUs and ask: ‘What are the best functions to approximate the actual tensor that describes these phonon interactions?’ Once we fix the number of product terms we want to keep, the machine learning process returns the best functions to approximate the full tensor. We typically only need a few of these products, saving orders of magnitude in computational complexity compared to using the full tensor. This method allows us to learn the compressed form of phonon interactions, and we can still use these highly compressed tensors to compute all the observables of interest with the same accuracy.”
Bernardi adds that the new method is well suited for high-throughput screening of thermal physics and heat transport in large material databases, a major effort in the materials community. As for future work, he says, “My vision right now is to compress all different types of quantum interactions and high-order processes in materials with similar techniques. The key will be to bypass the formation of large tensors altogether and to learn the interactions directly in compressed form.”
Scientists and engineers at Johns Hopkins Applied Physics Laboratory (APL) are pioneering a new approach to materials science that leverages artificial intelligence and robotics to dramatically speed up the design, testing, and optimization of metal components critical to national defense. This initiative, called TETRA (Transforming Evaluation and Testing via Robotics and Acceleration), reimagines the traditional materials science framework — known as the tetrahedron — by integrating advanced automation and accelerated testing methods. The goal is to overcome current limitations in the defense industrial base, which struggles to meet demand for both legacy and advanced metallic components due to slow alloy qualification processes.
Funded by the Department of Defense’s Industrial Base Analysis and Sustainment Program, TETRA aims to revolutionize how materials are evaluated, enabling rapid deployment of high-performance alloys. According to Sal Nimer, assistant program manager for APL’s Science of Extreme and Multifunctional Materials program, this effort could significantly enhance the speed and efficiency of producing and qualifying materials, helping the DoD maintain existing systems while unlocking new capabilities.
“It takes a really long time, it’s really expensive and it’s inefficient,” Lambert said. “With the TETRA lab, we’re working to simultaneously explore all of the different composition and processing variants that influence properties and performance — or at least we aim to do this significantly more rapidly.”
Their approach leverages a method known as combinatorial synthesis to study a variety of chemical compositions. TETRA expands on the standard implementations, which are too limited in size and scale for the rigors of fielded equipment, Lambert explained.
“Materials perform quite differently when scaled up in size, so we are developing methods that focus on development and size scales of interest,” he said. “And traditional combinatorial synthesis often doesn’t account for critical effects of heat treatment and the hot work from forging and other production processes. Our approach will enable understanding and consideration for all of these effects as we develop new alloys and scalable processing approaches.”
TETRA is leveraging an additive manufacturing technique called blown-powder directed energy deposition, or DED. The process involves a laser melting metal powder as it’s fed into the build area, where it quickly solidifies. This allows for the creation, layer by layer, of dense metal structures, and chemical compositions can be varied in each sample. A single build plate can contain hundreds of alloys, printed into custom-designed 3D specimens, ready to be autonomously tested.
In addition to fabrication via additive manufacturing, the lab will feature a state-of-the-art melting furnace for ultrafast synthesis of custom castings from raw material, custom heat treatment furnaces and hot forging equipment for shaping material and modifying its microstructure, and robotic mechanical property measurement. This combination of capabilities will make TETRA an all-in-one materials research and development facility — the first of its kind.
These same tools for discovering new materials will also enable researchers to troubleshoot the manufacturing of legacy parts, Lambert said, helping to identify why a “surprisingly high” number of parts are rejected for poor properties, even when the root cause of these poor properties is not always clear. “One envisioned future use for the TETRA lab is to help diagnose those kinds of problems with existing parts, in addition to creating new ones,” he said.
“TETRA’s cutting-edge methods should integrate seamlessly with our ongoing work in AI-accelerated materials discovery,” Nimer said. “We envision creating an AI ‘co-engineer’ that works alongside human researchers, learning from materials development data to automatically recommend the next tests, or even creating a self-running lab that autonomously designs materials and tests them. We’re not there yet, but we hope we’re building the foundation to enable those instantiations in the future.”
Researchers at the University of Arizona in Tucson have developed a laser-based microscope that snaps images at attosecond — or a billionth of a billionth of a second — speed.
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A new perspective article from NIST and collaborators highlights a critical but often underrated piece of the semiconductor revolution: the “soft” polymer-based packaging materials that hold advanced chips together that are emerging as important factors for reliability, performance, and cost.
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Flaws in the pool deck are thought to be the preliminary cause of the devastating collapse of a condominium in Surfside, Florida, that killed 98. Recent test results by the National Institute of Standards and Technology (NIST), the agency leading the investigation of the 2021 tragedy, reinforces the theory that the failure started in the pool deck area rather than in the tower itself. NIST released new findings this week.
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Scientists in Japan have uncovered unusual quantum behavior in “heavy” electrons within the crystalline compound CeRhSn, which could one day support advances in quantum computing. These electrons appear to carry hundreds of times their normal mass—not due to their intrinsic properties, but because of strong interactions with other particles in the material that slow them down. Unlike typical metals, the electrons in CeRhSn enter a “non-Fermi liquid” state, moving collectively and entangled rather than individually. Remarkably, this state follows a universal energy dissipation rule known as Planckian scaling, linking the behavior to fundamental constants of nature.
n ordinary conductors like copper, electrons scatter in a way that can be calculated with standard physics. But at the edge of magnetism, superconductivity, or other collective phases, those rules break down. According to the researchers, CeRhSn sits right at this edge, making it a prime example of what physicists call “quantum criticality.”
The significance, according to the team, is that quantum critical materials may offer new routes for building quantum technologies. While most current quantum computers use superconducting circuits or trapped ions, heavy-electron compounds could provide an alternative platform where information is stored in the collective motion of electrons.
Dr. Shin-ichi Kimura of The University of Osaka, who led the research, said, “Our findings demonstrate that heavy fermions in this quantum critical state are indeed entangled, and this entanglement is controlled by the Planckian time. This direct observation is a significant step towards understanding the complex interplay between quantum entanglement and heavy fermion behavior.”
To probe CeRhSn, the team grew single crystals of the material in a controlled furnace and then polished them for study. They shined polarized light along different crystal directions and recorded how the electrons responded across a wide range of energies.
The experiments showed a distinct directional difference. In the plane where the cerium atoms form a kagome-like pattern—a triangular lattice with inherent frustration—the electrons followed Planckian scaling below about 80 Kelvin, or -193°C. Along the vertical axis, however, the electrons did not follow the same rule. The researchers interpret this anisotropy, or direction dependence, as evidence that the geometry of the lattice strongly shapes how the electrons behave.
While the findings demonstrate that heavy electrons can follow universal scaling laws, they do not yet provide a recipe for building a quantum computer. According to the study, the scaling behavior appeared only along one direction in the crystal, underscoring the material’s complexity.
The researchers also note that different experimental probes sometimes yield conflicting results. For example, while optical conductivity measurements suggested Planckian behavior, other measurements such as heat capacity report different values. Reconciling these differences will require further experiments.
Quantum computing today is built on platforms that manipulate single quantum states and properly managing entanglement. Although there is work to do, the study points to a different possibility: harnessing the collective entanglement of many strongly interacting electrons. While speculative, the researchers argue that observing Planckian scaling in heavy-electron systems adds weight to this idea.
The researchers suggest that CeRhSn may represent a new class of quantum critical material, distinct from compounds where magnetism dominates. They propose studying other materials with similar lattice structures to see if the same directional scaling appears. Pressure, chemical substitution, or magnetic fields could also be used to test how far the Planckian regime extends.
If the phenomenon proves robust, scientists report they could eventually try to design materials where the collective state of heavy electrons can be stabilized and controlled. Such systems might support qubits that are less sensitive to noise than those in existing technologies.
Northwestern University chemists have developed a new plastic upcycling process that could revolutionize recycling by significantly reducing or even eliminating the need to pre-sort mixed plastic waste. Using an inexpensive nickel-based catalyst, the method selectively breaks down polyolefin plastics—such as polyethylenes and polypropylenes, which make up nearly two-thirds of global plastic use—allowing industrial users to efficiently process large volumes of unsorted waste.
When the catalyst breaks down polyolefins, the low-value solid plastics transform into liquid oils and waxes, which can be upcycled into higher-value products, including lubricants, fuels and candles. Not only can it be used multiple times, but the new catalyst can also break down plastics contaminated with polyvinyl chloride (PVC), a toxic polymer that notoriously makes plastics “unrecyclable.”
“One of the biggest hurdles in plastic recycling has always been the necessity of meticulously sorting plastic waste by type,” said Northwestern’s Tobin Marks, the study’s senior author. “Our new catalyst could bypass this costly and labor-intensive step for common polyolefin plastics, making recycling more efficient, practical and economically viable than current strategies.”
“When people think of plastic, they likely are thinking about polyolefins,” said Northwestern’s Yosi Kratish, a co-corresponding author on the paper. “Basically, almost everything in your refrigerator is polyolefin based — squeeze bottles for condiments and salad dressings, milk jugs, plastic wrap, trash bags, disposable utensils, juice cartons and much more. These plastics have a very short lifetime, so they are mostly single-use. If we don’t have an efficient way to recycle them, then they end up in landfills and in the environment, where they linger for decades before degrading into harmful microplastics.”
A world-renowned catalysis expert, Marks is the Vladimir N. Ipatieff Professor of Catalytic Chemistry at Northwestern’s Weinberg College of Arts and Sciences and a professor of chemical and biological engineering at Northwestern’s McCormick School of Engineering. He is also a faculty affiliate at the Paula M. Trienens Institute for Sustainability and Energy. Kratish is a research assistant professor in Marks’ group, and an affiliated faculty member at the Trienens Institute. Qingheng Lai, a research associate in Marks’ group, is the study’s first author. Marks, Kratish and Lai co-led the study with Jeffrey Miller, a professor of chemical engineering at Purdue University; Michael Wasielewski, Clare Hamilton Hall Professor of Chemistry at Weinberg; and Takeshi Kobayashi a research scientist at Ames National Laboratory.
From yogurt cups and snack wrappers to shampoo bottles and medical masks, most people interact with polyolefin plastics multiple times throughout the day. Because of its versatility, polyolefins are the most used plastic in the world. By some estimates, industry produces more than 220 million tons of polyolefin products globally each year. Yet, according to a 2023 report in the journal Nature, recycling rates for polyolefin plastics are alarmingly low, ranging from less than 1% to 10% worldwide.
The main reason for this disappointing recycling rate is polyolefin’s sturdy, stubborn composition. It contains small molecules linked together with carbon-carbon bonds, which are famously difficult to break.
“When we design catalysts, we target weak spots,” Kratish said. “But polyolefins don’t have any weak links. Every bond is incredibly strong and chemically unreactive.”
Currently, only a few, less-than-ideal processes exist that can recycle polyolefin. It can be shredded into flakes, which are then melted and downcycled to form low-quality plastic pellets. But because different types of plastics have different properties and melting points, the process requires workers to scrupulously separate various types of plastics. Even small amounts of other plastics, food residue or non-plastic materials can compromise an entire batch. And those compromised batches go straight into the landfill.
Another option involves heating plastics to incredibly high temperatures, reaching 400 to 700 degrees Celsius. Although this process degrades polyolefin plastics into a useful mixture of gases and liquids, it’s extremely energy intensive.
“Everything can be burned, of course,” Kratish said. “If you apply enough energy, you can convert anything to carbon dioxide and water. But we wanted to find an elegant way to add the minimum amount of energy to derive the maximum value product.”
To uncover that elegant solution, Marks, Kratish and their team looked to hydrogenolysis, a process that uses hydrogen gas and a catalyst to break down polyolefin plastics into smaller, useful hydrocarbons. While hydrogenolysis approaches already exist, they typically require extremely high temperatures and expensive catalysts made from noble metals like platinum and palladium.
“The polyolefin production scale is huge, but the global noble metal reserves are very limited,” Lai said. “We cannot use the entire metal supply for chemistry. And, even if we did, there still would not be enough to address the plastic problem. That’s why we’re interested in Earth-abundant metals.”
For its polyolefin recycling catalyst, the Northwestern team pinpointed cationic nickel, which is synthesized from an abundant, inexpensive and commercially available nickel compound. While other nickel nanoparticle-based catalysts have multiple reaction sites, the team designed a single-site molecular catalyst.
The single-site design enables the catalyst to act like a highly specialized scalpel — preferentially cutting carbon-carbon bonds — rather than a less controlled blunt instrument that indiscriminately breaks down the plastic’s entire structure. As a result, the catalyst allows for the selective breakdown of branched polyolefins (such as isotactic polypropylene) when they are mixed with unbranched polyolefins — effectively separating them chemically.
“Compared to other nickel-based catalysts, our process uses a single-site catalyst that operates at a temperature 100 degrees lower and at half the hydrogen gas pressure,” Kratish said. “We also use 10 times less catalyst loading, and our activity is 10 times greater. So, we are winning across all categories.”
With its single, precisely defined and isolated active site, the nickel-based catalyst possesses unprecedented activity and stability. The catalyst is so thermally and chemically stable, in fact, that it maintains control even when exposed to contaminants like PVC. Used in pipes, flooring and medical devices, PVC is visually similar to other types of plastics but significantly less stable upon heating. Upon decomposition, PVC releases hydrogen chloride gas, a highly corrosive byproduct that typically deactivates catalysts and disrupts the recycling process.
Amazingly, not only did Northwestern’s catalyst withstand PVC contamination, PVC actually accelerated its activity. Even when the total weight of the waste mixture is made up of 25% PVC, the scientists found their catalyst still worked with improved performance. This unexpected result suggests the team’s method might overcome one of the biggest hurdles in mixed plastic recycling — breaking down waste currently deemed “unrecyclable” due to PVC contamination. The catalyst also can be regenerated over multiple cycles through a simple treatment with inexpensive alkylaluminium.
“Adding PVC to a recycling mixture has always been forbidden,” Kratish said. “But apparently, it makes our process even better. That is crazy. It’s definitely not something anybody expected.”
For more information: Nature Chemistry
Professor Yoon Hyo-jae’s research team at Korea University has discovered that rubber plant leaves can naturally generate electricity through a phenomenon called the ionic Seebeck effect, without any additional processing. This effect occurs when moisture and ions within the plant tissues move in response to temperature differences, creating voltage. Notably, partially drying the leaves forms a conductive surface layer that significantly enhances this energy conversion. This breakthrough reveals that plants, traditionally known for photosynthesis and gas exchange, can also function as high-performance thermoelectric devices, outperforming many artificial materials.
The researchers confirmed that the ionic Seebeck effect is exhibited not only in dried leaves but also in living leaves, enabling electricity generation. They also discovered that when electrodes were attached to living leaves and exposed to light, a stable voltage was repeatedly generated and that this energy conversion process did not affect the leaf’s physiological functions.
Kang Hun-gu, the first author of the article, said, “The fact that leaves can serve as ‘living thermoelectric devices’ that generate electricity by receiving heat is a new plant function that has not been observed until now. Our study well demonstrates a convergence research paradigm that interconnects chemistry, biology, and energy science.”
This research holds significant value from a sustainability point of view because the results could enable the utilization of plants in their natural state. Furthermore, since changes in plant health can be detected in real time, the results of this study are expected to have wide applications in environmental and agricultural fields, such as climate change response and plant growth monitoring.
This study was supported by the National Research Foundation of Korea.
For more information: Korea University
Image: △ (a), (b) Photographs of a Ficus elastica leaf used in the experiments, and a conceptual diagram of a device for measuring thermopower performance.
(c), (d) Results of measuring the thermovoltage observed from the Ficus elastica leaf.
Stretchable electronics are increasingly used in devices like smartphones, smartwatches, curved displays, and wearable sensors, but they face a trade-off between flexibility and electrical performance, as stretchable materials like elastomers typically underperform compared to rigid ones like metals or semiconductors. To address this, researchers have turned to origami and kirigami—Japanese techniques of folding and cutting paper—to enable stretchability in non-stretchable electronic materials. Origami creates bendable structures with hinges suitable for mounting rigid components, while kirigami uses slits to allow full structural deformation, making it ideal for large-area designs but less compatible with rigid parts.
In a groundbreaking study, Professor Eiji Iwase and Mr. Nagi Nakamura from the Department of Applied Mechanics and Aerospace Engineering at Waseda University, Japan, developed an innovative hybrid technique using kiri-origami structures.
“In this study, we have proposed a kiri-origami structure that incorporates both folding and cutting lines, combining the strengths of origami and kirigami while canceling out their weaknesses,” explains Iwase. “This structure enables large-number-of-unit, large area electronic devices, allowing rigid electronic components to be folded by stretching.”
The proposed kiri-origami design features a mutual orthogonal cutting line pattern. In this pattern, triangular joint panels consisting of two folding lines act as hinges and connect two square panels formed by the cutting lines. When stretched from a flat state, the square panels rise and rotate. This opens slits between the panels, ultimately resulting in a Z-shape around the hinges. This structure allows simultaneous mounting of rigid components and stretching to a target shape, while also supporting large-area and large-number-of-unit structures.
In ideal kiri-origami structures, called rigid-origami structures, the panels do not deform, and the hinges rotate frictionlessly. However, for a real stretchable electronic substrate, panel deformation and elastic repulsive forces cannot be overlooked, giving rise to an “elastic origami model.” To investigate these effects, the researchers tested the deformation of a rectangular elastic origami model using a simple stretching method, where the sample is clamped and stretched uniaxially. They observed that the elastic model deformed differently from the rigid model. They found that this difference occurs due to two factors: first, the clamping edges in the rigid model are free edges, while they are fixed in the elastic model. Second, the entire structure distorts while stretching due to non-uniform tension.
To mitigate these effects, the researchers developed a new folding method that introduces buffer structures. The buffer structures are trapezoidal extensions that connect all the edges of the kiri-origami structure to the clamps. The width of the shorter edge of the buffer structures is equal to the initial width of the kiri-origami structure, while the larger edge is set to the target stretched width of the rigid model. When a tensile force is applied, they extend and behave like springs. As a result, the entire structure stretches in two directions, matching the deformation of the rigid model while maintaining uniform tension.
The researchers demonstrated this technique by fabricating a stretchable display with more than 500 hinges and 145 LEDs. All hinges could fold up simultaneously, and the device’s performance was maintained before and after folding.
“Our approach makes it possible to develop stretchable electronic devices that can accommodate complex shapes and do not compromise on performance, including next-generation high-performance wearable sensors, curved displays, and flexible sensors and actuators for human assistance robots,” remarks Iwase.
This kiri-origami technique thus offers a scalable, structurally engineered solution for integrating high-performance electronic materials into flexible, stretchable devices—paving the way for innovative applications in electronics, healthcare, and robotics.
For more information: npj Flexible Electronics
Image: Kiri-origami structures combine the benefits of both origami and kirigami, incorporating their advantages while canceling their disadvantages, enabling the development of high-performance, stretchable, large-number-of-unit electronic devices.
