Scientists create a paper-thin light that glows like the sun

Scientists have created an ultra-thin, paper-like LED that emits a warm, sunlike glow by precisely blending red, yellow-green, and blue quantum dots to mimic natural sunlight. This breakthrough offers improved color accuracy and reduced eye strain, with potential applications in home lighting, electronic displays, and workspaces. Unlike traditional bulky bulbs, this slender design could transform how we light our environments while helping minimize sleep disruption from harsh artificial light.

“This work demonstrates the feasibility of ultra-thin, large-area quantum dot LEDs that closely match the solar spectrum,” says Xianghua Wang, a corresponding author of the study. “These devices could enable next-generation eye-friendly displays, adaptive indoor lighting, and even wavelength-tunable sources for horticulture or well-being applications.”

Many people prefer indoor lighting that feels natural and soothing. Earlier approaches achieved this effect with flexible LEDs that used red and yellow phosphorescent dyes to create a candle-like warmth. A newer alternative relies on quantum dots—tiny semiconductor particles that transform electrical energy into colored light. Some research teams have already used quantum dots to make white LEDs, but replicating the complete spectrum of sunlight has remained difficult, particularly in the yellow and green regions where sunlight is strongest. To address this challenge, Lei Chen and colleagues developed quantum dots that could recreate that balanced, sunlike glow in a thin, white quantum dot LED (QLED). Meanwhile, Wang’s group proposed an efficient conductive material design that could operate effectively at relatively low voltages.

The team began by synthesizing red, yellow-green, and blue quantum dots coated with zinc-sulfur shells. They determined the precise color ratio needed to match the spectrum of natural sunlight as closely as possible. Next, they assembled the QLED on an indium tin oxide glass substrate, layering conductive polymers, the quantum dot blend, metal oxide particles, and finally a top coating of aluminum or silver. The quantum dot layer measured only a few dozen nanometers in thickness—much thinner than standard color conversion layers—resulting in a white QLED with an overall profile comparable to wallpaper.

In initial tests, the thin QLED performed best under a 11.5-volt (V) power supply, giving off the maxmium bright, warm white light. The emitted light had more intensity in red wavelengths and less intensity in blue wavelengths, which is better for sleep and eye health, according to the researchers. Objects illuminated by the QLED should appear close to their true colors, scoring over 92% on the color rendering index.

In further experiments, the researchers made 26 white QLED devices, using the same quantum dots but different electrically conductive materials to optimize the operating voltage. These light sources required only 8 V to reach maximum light output, and about 80% exceeded the target brightness for computer monitors.

For more information: ACS Applied Materials

Image: A paper-thin device uses quantum dots, similar to those described in this work, to light up LEDs. Credit: Lin Zhou, Xianghua Wang

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

A team led by Rice University’s Yong Lin Kong has developed a soft yet strong metamaterial capable of rapidly changing its size and shape through remote control, marking a major step forward for ingestible and implantable medical devices. Unlike natural materials, metamaterials derive their unique properties from their physical structure rather than chemical composition. Kong’s design combines exceptional deformability with structural stability—an unprecedented feat in soft materials—allowing it to withstand compressive loads over ten times its own weight and perform reliably in extreme temperatures and harsh chemical environments.

“We programmed multistability, i.e. the ability to exist in multiple stable states, into the soft structure by incorporating geometric features such as trapezoidal supporting segments and reinforced beams,” said Kong, assistant professor of mechanical engineering at Rice’s George R. Brown School of Engineering and Computing. “These elements create an energy barrier that locks the structure into its new shape even after the external actuation force is removed.”

The metamaterial’s soft architecture helps address critical medical safety concerns such as gastric ulcers, puncture injuries and inflammation that can occur from implantable and ingestible devices made of rigid components.

Kong and his team used 3D printing to create molds that form interconnected microarchitectures of tilted beams and supporting segments. This design allows for rapid switching between open (off) and closed (on) states, and the transformed configuration is maintained even after the magnetic field is removed. By combining many such unit cells as “building blocks,” they form a 3D structure that can not only transform its shape but can also produce complex peristaltic motions to move or to deliver fluids in a controlled manner when actuated with a magnetic field.

Importantly, the material continued to function reliably even after prolonged exposure to mechanical stress and acidic corrosion, conditions that mimic the harsh environment of the human stomach.

“The metamaterial makes it possible to remotely control the size and shape of devices inside the body. This could enable lifesaving capabilities such as precisely controlling where a device stays, delivering medication where it’s needed or applying targeted mechanical forces deep inside the body,” Kong said. “We are now leveraging this metamaterial to develop ingestible systems that may one day treat obesity in humans or improve the health of marine mammals, and we are collaborating with surgeons at the Texas Medical Center to design wireless fluidic control systems to address unmet clinical needs.”

The first author of this study was Kong’s first graduate student, Taylor Greenwood, who has since graduated and started a faculty position at Brigham Young University. Kong’s other graduate students Brian Elder and Jared Anklam, postdoctoral associates Jian Teng and Saebom Lee and other collaborators were involved in the study. This research was supported by the National Institutes of Health and the Office of Naval Research.

For more information: Science Advances

Image: The new metamaterial designed by Kong and his team at Rice can be controlled remotely to rapidly transform its size and shape (Photos and video by Jorge Vidal/Rice University).

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

Researchers at the University of Michigan have developed a breakthrough method that brings quantum-level precision to molecular modeling, offering deeper insights into chemical reactions and material properties. This advancement addresses the quantum many-body problem—how electrons interact to form chemical bonds and influence electrical behavior—which traditionally requires immense computational power and is limited to small molecules. By enhancing the efficiency of this simulation approach, the new method could extend quantum accuracy to larger, more complex systems, potentially reducing the heavy demand on national lab supercomputers.

Density functional theory, or DFT, makes quantum chemistry more manageable by focusing on electron densities rather than tracking every electron individually. This approach keeps computing demands much lower, allowing simulations of systems with hundreds of atoms. A major challenge, however, lies in the exchange-correlation (XC) functional, which governs how electrons interact according to quantum mechanics. 

Until now, researchers have had to rely on approximations of the XC functional tailored to specific applications, limiting the theory’s overall accuracy. Improving this functional is key to making DFT an even more powerful tool for chemistry and materials science.

According to Vikram Gavini, a University of Michigan professor of mechanical engineering and the corresponding author of the study, researchers know that a universal functional exists that applies to all electron systems – whether in molecules, metals, or semiconductors – but its exact form remains unknown.

Hence, understanding this functional is crucial for improving DFT, which models electron interactions and underpins simulations in chemistry and materials science.

Given DFT’s central role in advancing both materials research and basic science, the US Department of Energy provided funding and supercomputer resources to support the University of Michigan team’s efforts to approach the universal exchange-correlation functional. 

The researchers began by analyzing individual atoms and small molecules using quantum many-body theory. Then, instead of applying approximate functionals to predict electron behavior, they used machine learning to determine which XC functional would reproduce the electrons’ behavior as calculated by the more precise quantum many-body method.

Bikash Kanungo, a University of Michigan assistant research scientist in mechanical engineering and first author of the study, explains that an accurate exchange-correlation functional has broad applications because it is material-agnostic.

It is equally important for researchers developing better battery materials, designing new drugs, or building quantum computers. By improving this functional, scientists can make density functional theory more reliable and widely applicable, enabling more precise simulations across chemistry, materials science, and emerging technologies.

Thus, researchers can now use the XC functional discovered by the University of Michigan team or apply their approach to new systems, starting with light atoms and molecules and eventually extending to solids, paving the way for more accurate and efficient simulations across chemistry and materials science.

For more information: University of Michigan

Image: A 3D map of the quantum potential.

AI-powered approach simplifies exploration of complex materials

Researchers at Oak Ridge National Laboratory have introduced a powerful new method for investigating the atomic-level behavior of materials by combining Bayesian deep learning—a fusion of probability theory and neural networks—with advanced data analysis. This approach enables scientists to rapidly and accurately process complex datasets, allowing them to scan broader sample areas and identify regions with critical properties far more efficiently than traditional techniques.

“This method makes it possible to study a material’s properties with much greater efficiency,” said Ganesh Narasimha from ORNL. “Usually, we would need to scan a large region, and then several small regions, and perform spectroscopy, which is very time-consuming. Here, the AI algorithm takes control and does this process automatically and intelligently.”

The team demonstrated the system using europium zinc arsenide, a magnetic semimetal with distinctive electronic traits. With the aid of scanning tunneling microscopy, the researchers uncovered links between atomic-scale structures and their electronic responses.

Although the case study focused on europium zinc arsenide, the scientists emphasize that the method is broadly applicable to many different materials. The advance not only streamlines the discovery process but also strengthens national efforts in artificial intelligence and quantum science.

For more information: Nature

Image: A scanning tunneling microscope and machine learning algorithm autonomously search for atomic structures. This image shows a vacancy defect on europium zinc arsenide. (Image Credit: Ganesh Narasimha/ORNL, U.S. Dept. of Energy)

Breakthrough phason discovery in twisted 2D materials transforms quantum computing

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)

How Argonne is helping to expand the Quantum Prairie

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.

“The pace of scientific discovery is truly remarkable, and the levels of global engagement have dramatically increased,” said David Awschalom, the Quantum Information Science and Technology group leader at Argonne, professor at the University of Chicago and director of Q-NEXT, a DOE National Quantum Information Science Research Center led by Argonne. “Quantum science is rapidly becoming a technology that will have both economic and security implications.”
Argonne is accelerating quantum information science in multiple ways, including creating materials for qubits, which are units of quantum data, and helping to build testbeds for quantum technology such as ARQNET, InterQnet, the Chicago Quantum Network and the Chicago Quantum Computing Testbed. At Argonne, the upgraded Advanced Photon Source (APS), the Center for Nanoscale Materials and the Argonne Leadership Computing Facility (ALCF) — all DOE Office of Science user facilities — have enabled such breakthroughs. In 2023, the Argonne Quantum Foundry was launched to develop scalable semiconductor quantum 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.”

But the lab’s homegrown capabilities are only part of its role as a key player in a growing Midwestern quantum ecosystem. Through Q-NEXT, Argonne spearheads collaboration on quantum technologies. It is a founding member of both the Chicago Quantum Exchange, a University of Chicago-based community of more than 50 corporate, international, nonprofit and regional partners that launched and nurtured the Illinois-Wisconsin-Indiana quantum ecosystem, and Duality, the nation’s first quantum startup accelerator.

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 institute organizes quantum information research across four themes: computing and simulations; matter and materials; communications and networking; and quantum detecting and sensing. Recently, Argonne researchers developed an approach for controlling the collective magnetic properties of atoms in real time, which could be useful in quantum computers. Others are building qubits known as color centers at the Argonne Quantum Foundry.

“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.”

For more information: Argonne National Laboratory
Image: Argonne researchers are developing technologies and protocols to enable scalable, long-distance quantum communication. (Image by Argonne National Laboratory.)

Enabling an electric future, researchers create electrode-agnostic electrolyte

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.

Typically, batteries are made up of two electrodes—an anode (negative side) and a cathode (positive side)—as well as a liquid electrolyte. In this case, the “initially anode-free” aspect of the battery means its physical anode forms internally upon the battery’s first charge—making it simpler, less expensive and more energy-dense.

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.”

For more information Nature Communications
Image: PhD student Qianli Xing. Photos: Joel Hallberg

New AI technique unravels quantum atomic vibrations in materials

Caltech researchers have developed a machine learning method that significantly accelerates quantum calculations related to atomic vibrations, or phonons, which influence key material properties like heat transport and phase transitions. Led by Professor Marco Bernardi and graduate student Yao Luo, the team built on their earlier work using singular value decomposition (SVD) to simplify complex mathematical models of electron-phonon interactions. Their new AI-based approach could eventually be applied to all quantum interactions, offering a powerful tool for understanding how particles behave in materials from first principles.

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.

Using current state-of-the-art techniques, a supercomputer takes hours or days to calculate the interactions between three or four phonons in a material. The new method enables computers to complete the same thermal transport and phonon dynamics calculations 1,000 to 10,000 times faster, all while maintaining accuracy.

“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.”

For more information: Physical Review Letters
Image: Inspired by recent advances in machine learning, Caltech scientists 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.

An accelerated paradigm for developing mission-critical materials

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.

“When developing materials for defense needs, it’s not just about the composition of the alloy or system — it’s also about how you shape, treat and refine it,” said Morgan Trexler, who leads the research program area in APL’s Research and Exploratory Development Mission Area. “TETRA has potential to be game-changing because it allows us to simultaneously consider every variable that impacts performance, which until now, has been painstaking and time-consuming, sometimes taking months to achieve what TETRA can accomplish in just a matter of days.”
In materials science, processing, structure and properties are dynamically interrelated, with changes in one necessarily affecting the others. However, conventional processes lock scientists into procedures that force them to assess each factor serially, explained Paul Lambert, TETRA co-lead. Scientists typically produce a large ingot of material with a uniform chemical composition, cut it into pieces, place those in a furnace, machine each into a test specimen and then subject each specimen to analysis to test for properties of interest. This sequence is then iteratively repeated for each change made to the material.

“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.

Eventually, the TETRA team envisions bringing in existing APL capabilities that employ artificial intelligence to discover novel materials for extreme environments.

“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.”

Image: A rendering of the TETRA lab demonstrates how the effort will develop novel capabilities and streamline processes to increase the speed of production for designing, testing and optimizing metal components. Credit: Johns Hopkins APL

NIST finds Florida condo collapse started in pool deck

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 report heavy electrons could open a path to a new type of quantum computer

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.

For more information: npj Quantum Materials

A simple metal could solve the world’s plastic recycling problem

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

KU research group discovers the principles of in vivo thermopower generation

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.

Novel kiri-origami structures enable high-performance stretchable electronics

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.

MoonRanger’s instruments to gather data during 2029 lunar mission

NASA has tapped a lunar rover built at Carnegie Mellon University, Pittsburgh, to advance our understanding of water on the moon as it autonomously explores near the lunar south pole. MoonRanger will be among the payloads aboard a 2029 mission to the moon. The rover will carry a neutron spectrometer to study the lunar soil for traces of hydrogen, a good indicator of the presence of water, and demonstrate new levels of autonomous navigation on the moon.

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Accelerating materials design with high-throughput experiments and data science

Advances in machine learning and computational modeling are transforming materials design by allowing researchers to predict and discover new materials more efficiently than traditional methods. At the forefront of this innovation is Professor Toshiaki Taniike of the Japan Advanced Institute of Science and Technology (JAIST), who leads the Laboratory on Materials Informatics. His team integrates high-throughput experiments, data science, and simulations to accelerate the development of materials like catalysts, polymers, nanocomposites, and nanomaterials such as MOFs and graphene. Driven by a lifelong passion for science, Taniike is focused on solving real-world problems through smarter, data-driven approaches to materials discovery.

“I studied chemical engineering to learn the basics of process design. But I realized I needed a deeper understanding of how chemical reactions actually work. That led me to quantum physics and simulations. However, simulations cannot fully capture the complexity of real reactions, so I returned to experiments, focusing on catalysis, because catalysts drive about 80% of chemical processes,” says Prof. Taniike, sharing how he voyaged through this field. Over time, he learned that discovery often comes through trial and error, so now, he combines high-throughput experiments with data science and machine learning to make this process faster and smarter.

Traditionally, discovering new materials or chemical reactions was mainly pursued as a trial-and-error experiment. Researchers would use what they already knew to take a calculated guess about which features or descriptors matter most when designing a new material. For example, they might think that surface area or crystal structure will affect catalyst performance and then test that idea. This worked well for improving things we already understand, but its utility is limited for discovering truly new reactions or materials because you cannot guess what you do not know.

What Prof. Taniike’s lab does differently is to combine high-throughput experimentation with machine learning techniques like automatic feature engineering. Instead of relying on human intuition to choose descriptors, they let the machine automatically generate and test thousands or even millions of possible descriptors to find the ones that really matter. They then run experiments in parallel to test these ideas, which makes the process 10 to 1000 times faster than doing it manually. Moving beyond the traditional trial-and-error approach, this lets them discover reactions or materials that were impossible to find before.

One such example is the oxidative coupling of methane (OCM), which is sometimes called a “dream reaction.” It aims to convert methane — the most abundant hydrocarbon feedstock — directly into ethylene, which is extremely valuable for producing plastics and chemicals. This is very challenging because methane is such a stable molecule. Usually, when you try to activate it, it just burns completely to CO2. The idea behind OCM is to carefully control this process so that instead of complete combustion, you stop the reaction at the ethylene stage. This could help reduce CO2 emissions from the chemical sector. Published in ACS Catalysis in 2020, Prof. Taniike’s study presents a high-throughput system for the OCM, generating a large, consistent dataset that enables automated performance evaluation, insightful data visualization, and accurate C₂ yield prediction through nonlinear machine learning.

Another study published in Communications Chemistry presents a combination of automatic feature engineering with the above high-throughput system, to streamline the discovery of high-performing catalysts for the OCM through automated descriptor design.

The team is also moving toward discovering entirely new kinds of reactions that could someday transform fields like energy, carbon recycling, or sustainable chemical manufacturing.

For more information: Japan Advanced Institute of Science and Technology

Image: Professor TANIIKE Toshiaki from JAIST.

Scientists give robots a sense of touch with fabric that mimics human skin

Robots often struggle with tasks requiring a delicate sense of touch, such as grasping objects without dropping or crushing them, despite advancements in sensors and cameras. A team at the University at Buffalo (UB) is developing a new electronic textile (E-textile) that mimics the way human nerves detect pressure and slippage during gripping. This cost-effective technology could significantly improve robotic tactile sensitivity, enabling machines to handle objects more precisely and safely.

“The applications are very exciting …,” says Jun Liu, assistant professor in the Department of Mechanical and Aerospace Engineering, School of Engineering and Applied Sciences. “The technology could be used in manufacturing tasks like assembling products and packaging them — basically any situation where humans and robots collaborate. It could also help improve robotic surgery tools and prosthetic limbs.”

Liu, also a core faculty member of UB’s RENEW Institute, is the study’s corresponding author. Additional authors include Ehsan Esfahani, associate professor of mechanical and aerospace engineering, several UB students and a former UB PhD student from Liu’s group who is now a postdoctoral scholar at the University of Chicago.

“Our sensor functions like human skin — it’s flexible, highly sensitive and uniquely capable of detecting not just pressure, but also subtle slip and movement of objects,” says Vashin Gautham, a PhD candidate in the Liu research group and first author of the study. “It’s like giving machines a real sense of touch and grip, and this breakthrough could transform how robots, prosthetics and human-machine interaction systems interact with the world around them.”

Researchers integrated the sensing system onto a pair of 3D-printed robotic fingers that are mounted to a compliant robotic gripper developed by Esfahani’s group.

“The integration of this sensor allows the robotic gripper to detect slippage and dynamically adjust its compliance and grip force, enabling in-hand manipulation tasks that were previously difficult to achieve,” says Esfahani.

For example, when researchers tried to pull a copper weight from the fingers, the gripper sensed this and immediately tightened its grip.

“This sensor is the missing component that brings robotic hands one step closer to functioning like a human hand,” Esfahani adds. The slight movement of the object causes friction between the two materials, which in turn generates direct-current (DC) electricity — a phenomenon known as the tribovoltaic effect.

Researchers measured the sensing system’s response time and found it comparable to human capabilities. For example, it took the system from .76 milliseconds to 38 milliseconds to respond, depending on the experiment. Human touch receptors typically react between 1 and 50 milliseconds.

“The system is incredibly fast, and well within the biological benchmarks set forth by human performance,” says Liu. “We found that the stronger or faster the slip, the stronger the response is from the sensor. This is fortuitous because it makes it easier to build control algorithms to enable the robot to act with precision.”

The research team is planning additional testing of the sensing system, including integrating a form of artificial intelligence known as reinforcement learning that could further improve the robot’s dexterity.

For more information: Nature Communications

Image: From left: Ehsan Esfahani, Vashin Gautham and Jun Liu pose with the compliant robotic gripper outfitted with an electronic textile sensor. Photo: Meredith Forrest Kulwicki