UC Irvine scientist takes a lesson from ultrahard, wear-resistant mollusk teeth

Researchers from UC Irvine and Japan’s Okayama and Toho universities have conducted a groundbreaking study on chitons—algae-eating mollusks known for their exceptionally hard, wear-resistant, and magnetic teeth. The study reveals how iron-binding proteins called RTMP1 are precisely delivered through microscopic tubules called microvilli during tooth formation. This tightly controlled process results in a durable dental structure that supports the chitons’ constant scraping of rocky surfaces, and the findings are inspiring new approaches to designing advanced materials for various technological applications.

“Chiton teeth, which consist of both magnetite nanorods and organic material, are not only harder and stiffer than human tooth enamel, but also harder than high-carbon steels, stainless steel, and even zirconium oxide and aluminum oxide – advanced engineered ceramics made at high temperatures,” said co-author David Kisailus, UC Irvine professor of materials science and engineering. “Chiton grow new teeth every few days that are superior to materials used in industrial cutting tools, grinding media, dental implants, surgical implants and protective coatings, yet they are made at room temperature and with nanoscale precision. We can learn a lot from these biological designs and processes.”

There are more than 900 different chiton species worldwide, mostly dwelling within intertidal coastal regions. They can be found in places like Crystal Cove and Laguna Beach near the UC Irvine campus, but Kisailus said the ones investigated in this study are much larger and live in Northwest coastal areas of the United States and off the coast of Hokkaido, Japan. The research team learned that the RTMP1 proteins exist in chitons at disparate locations around the world, which suggests “some convergent biological design in controlling iron oxide deposition,” according to Kisailus.

He said that when he and his collaborators began, they were not aware of how and when these iron-binding proteins were conveyed into the chiton teeth. But by using a combination of advanced materials and molecular biological analyses, they discovered that these specialized proteins that were initially found within tissues surrounding immature, nonmineralized teeth were directed through nanostructured tubules into each tooth.

Once inside, the proteins bind to preassembled scaffolds of chitin nanofibers, the structural biopolymer that controls the architecture of the magnetite nanorods in the teeth. Concurrently, iron stored in ferritin, another protein found in the tissue outside the teeth, is released into each tooth, where it binds to the RTMP1, leading to the precise deposition of nanoscale iron oxide, which continues to grow during the tooth maturation into highly aligned magnetite nanorods that ultimately yield the ultrahard teeth.

Kisailus said this project has improved humanity’s understanding of cellular iron metabolism while providing insight into the synthesis of next-generation advanced materials.

“The fact that these organisms form new sets of teeth every few days not only enables us to study the mechanisms of precise, nanoscale mineral formation within the teeth, but also presents us with new opportunities toward the spatially and temporally controlled synthesis of other materials for a broad range of applications, such as batteries, fuel cell catalysts and semiconductors,” he said. “This includes new approaches toward additive manufacturing – 3D printing – and synthesis methods that are far more environmentally friendly and sustainable.”

Setting this study apart, according to Kisailus, was the blending of state-of-the-art materials science techniques, including ultra-high-resolution electron microscopy, X-ray analysis and spectroscopy, with biological methods such as immunofluorescence, gene expression tracking and RNA interference to reveal the full molecular choreography of chiton tooth formation.

“By combining biological and materials science approaches through wonderful, global efforts, we’ve uncovered how one of the hardest and strongest biological materials on Earth is built from the ground up,” Kisailus said.

His collaborators on this project were Michiko Nemoto, Koki Okada, Haruka Akamine, Yuki Odagaki, Yuka Narahara, Kiori Obuse, Hisao Moriya and Akira Satoh of Okayama University and Kenji Okoshi of Toho University.

For more information: University of California, Irvine

Image: David Kisailus, UC Irvine professor of materials science and engineering, shown here in his laboratory with aquarium tanks inhabited by the marine mollusks. Steve Zylius / UC Irvine

Simple algorithm paired with standard imaging tool could predict failure in lithium metal batteries

Researchers at UC San Diego have developed a straightforward yet effective technique using scanning electron microscopy to evaluate lithium metal battery performance, potentially speeding up the creation of safer, longer-lasting, and more energy-dense batteries for electric vehicles and large-scale energy storage. Lithium metal batteries can store twice the energy of current lithium-ion batteries, which could significantly extend the range of electric cars and the battery life of devices. However, achieving this requires controlling how lithium deposits during charging; uniform deposits lead to longer battery life, while uneven deposits form dangerous dendrites that can cause short circuits and battery failure.

Historically, researchers have largely determined the uniformity of lithium deposits by visually assessing microscope images. This practice has led to inconsistent analyses between labs, which has made it difficult to compare results across studies.

“What one battery group may define as uniform might be different from another group’s definition,” said study first author Jenny Nicolas, a materials science and engineering Ph.D. candidate at the UC San Diego Jacobs School of Engineering. “The battery literature also uses so many different qualitative words to describe lithium morphology — words like chunky, mossy, whisker-like and globular, for example. We saw a need to create a common language to define and measure lithium uniformity.”

To do so, Nicolas and colleagues — led by Ping Liu, professor in the Aiiso Yufeng Li Family Department of Chemical and Nano Engineering at the UC San Diego Jacobs School of Engineering — developed a simple algorithm that analyzes how evenly lithium is spread across scanning electron microscopy (SEM) images. The researchers used SEM because it offers detailed images of battery electrodes by capturing 3D surface features as 2D grayscale images — it is also a widely used technique in battery research.

To use their method, the team first takes SEM images of battery electrodes and converts them to black and white pixels. The white pixels represent the topmost lithium deposits in the sample and black pixels represent either the substrate or inactive lithium. The images are divided into multiple regions, and the algorithm counts the number of white pixels in each, then calculates a metric called the index of dispersion (ID).

“The index of dispersion is a measure of lithium uniformity,” Nicolas explained. “The closer it is to zero, the more uniform the lithium deposits. A higher value means less uniformity and more clustering of lithium particles in certain areas.”

The team first validated the method on 2,048 synthetic SEM images with known particle size distributions. The ID measurements aligned with the ground-truth distributions, which confirmed the method’s accuracy. The team then applied the method to real electrode images to analyze how lithium morphology changes over time under different cycling conditions. They found that as batteries cycled, the ID increased — indicating more uneven lithium deposits. Meanwhile, the energy required for lithium to deposit increased — a sign of degradation. In addition, the researchers found that local peaks and dips in the ID consistently appeared just before cells failed. Such peaks and dips could serve as an early warning sign of short circuits.

A big advantage of this method is that it is accessible. Battery researchers already use SEM imaging as part of their studies, Nicolas noted, and they can use the simple algorithm presented here to calculate the ID from the data they already collect.

“Our tool can be employed as a low-hanging fruit for researchers to take their analysis to the next level by utilizing image analysis to its fullest potential,” she said.

For more information: Proceedings of the National Academy of Sciences

Image: Scanning electron microscopy (SEM) images are already a common staple of battery research. Now, they can be paired with a simple algorithm to enable better prediction of lithium metal battery performance and failure. Credit: Jenny Nicolas et al.

A smarter approach to designing metamaterials

Lightweight cellular materials are essential to the performance of many industrial products, but defects during fabrication can compromise their effectiveness. To address this, a UC Berkeley-led team has developed GraphMetaMat, an AI-powered framework that uses deep learning to efficiently design 3D truss metamaterials with exceptional mechanical and acoustic properties, while reducing their vulnerability to manufacturing flaws and improving their overall usability.

“Until now, most of the work done in AI and materials design has been in the theoretical and computational domain, where they give you the design that performs well under ideal conditions,” said Xiaoyu (Rayne) Zheng, associate professor of materials science and engineering and the study’s principal investigator. “GraphMetaMat shows that AI can give you a realistic design tailored for a specific manufacturing method, like 3D printing, and optimized to withstand various manufacturing related defects. It sets the stage for the automatic design of manufacturable, defect-tolerant materials with on-demand functionalities.”

While advances in data-driven design and additive manufacturing have significantly accelerated the development of truss metamaterials, Zheng explained that existing inverse design approaches have inherent limitations. They can generate metamaterials with target linear properties, such as elasticity, but struggle to capture more complex nonlinear behaviors, such as energy absorption, needed for items like car bumpers and protective athletic gear.

“Design methods like topology optimization or an intuition-guided iterative approach are good at predicting simple responses,” said Zheng. “But for many real-world problems, these approaches cannot efficiently design materials with the required functionality, manufacturability and tolerance to defects introduced during manufacturing.”

Recently, researchers considered using graph neural networks for metamaterials design, since this has proved to be a powerful tool in drug discovery. But there was little to no training data available for designing metamaterials.

Zheng and his fellow researchers solved this problem by integrating multiple deep learning techniques — reinforcement learning, imitation learning, a surrogate model, and Monte Carlo tree search — into GraphMetaMat.

“Users can create metamaterial designs, represented as graphs, entirely from scratch based on custom inputs — such as a desired stress–strain curve or specific vibration attenuation gaps where mechanical waves are blocked at certain frequencies,” said Marco Maurizi, postdoctoral researcher in the Department of Materials Science and Engineering and lead author of the study. “Our AI system then iteratively adds graph nodes and edges to define the material’s geometry and topology.”

Most importantly, according to Zheng, GraphMetaMat can also integrate engineering constraints into the graphs — including manufacturing and defect constraints.

“GraphMetaMat has the unique ability to account for fabrication-induced imperfections,” he said. “This innovation is a game-changer because it ensures that the generated metamaterials will not fail if they develop a small defect during manufacturing.”

In their proof of concept, the researchers used GraphMetaMat to design lightweight truss metamaterials optimized for energy absorption and vibration mitigation at various frequencies. For each use case, the generated metamaterial consistently outperformed traditional materials, including polymeric foams and phononic crystals.

“Based on our findings, GraphMetaMat has the potential to redefine the design paradigm,” said Zheng. “This opens the door to exciting new possibilities in creating realistic, high-performance metamaterials.”

This work was conducted in collaboration with UCLA researchers led by Wei Wang and Yizhou Sun, and Penn State University researchers led by Yun Jing. In addition, co-lead authors of this study include Derek Xu of UCLA and Yu-Tong Wang of Penn State University.

For more information: Nature Machine Intelligence

Image: GraphMetaMat, an inverse design framework, enables users to create metamaterial designs, represented as graphs, entirely from scratch based on custom inputs. Its AI system then iteratively adds graph nodes and edges to define the material’s geometry and topology and integrates manufacturing and defect constraints. (Illustration courtesy of the researchers)

Self-driving lab to automate the discovery of novel alloys

Pure metals like aluminum and titanium often lack the ideal combination of properties needed for advanced applications, prompting researchers to develop custom alloys by blending various elements. To accelerate this traditionally slow and complex process, scientists at Lawrence Livermore National Laboratory, in collaboration with Cornell University, are creating the APEX platform—an automated system for 3D printing, processing, and analyzing alloy samples—to dramatically reduce alloy development time from years to months.

Funded by LLNL’s Laboratory Directed Research and Development program, this platform will combine industry-matured robotics and automation technologies with cutting-edge machine learning capabilities, turning a traditionally tedious and repetitive research process into a fully automated cycle.

“Our end goal is to make APEX the first self-driving laboratory for alloy discovery at LLNL, capable of working around the clock to collect experimental data and autonomously design, build and test novel alloys,” said Mason Sage, APEX principal investigator and LLNL robotics engineer.

Researching new alloys is complex for several reasons, but primarily because the design space expands exponentially with each added processing or compositional variable.

“Imagine you’re baking cookies and want to create the perfect recipe, so you decide to experiment with three ingredients: sugar, butter and flour,” Sage said. “If you test each ingredient with 10 different amounts, you’d need to bake 1,000 cookies to try every possible combination (10 sugar variations × 10 butter variations × 10 flour variations). Now, imagine you have 20 or 30 ingredients to test, suddenly, the number of cookies you’d need to bake becomes astronomically high — somewhere around 100 quintillion combinations, or 100 with eighteen zeros following it.

“This is the challenge before us. In theory, there hasn’t been enough time since the universe began to test every possible combination — even if we conducted an experiment every second.”

To navigate this enormous and complex parameter space, APEX leverages robotics and machine learning to accelerate sample fabrication, preparation and inspection by intelligently exploring different combinations.

“Our goal is to use machine learning to analyze the information we have collected from all our processes, learn from past experiments and design new experiments,” Sage said.

After selecting an alloy to explore, the APEX platform builds the metal sample layer-by-layer using an additive manufacturing method called directed-energy deposition, a type of 3D printing that uses a high-power laser to melt and fuse metal powders onto a substrate.

Then, to prepare a printed sample to be microstructurally and mechanically characterized, its surface must undergo grinding and polishing. Traditionally, these steps can take two to three days to complete by hand, but APEX is expected to simultaneously process multiple samples at a time, producing dozens of samples a day.

Once a sample is prepped, APEX can evaluate its properties through a series of characterization tests, including examining its surface under a microscope, indenting it with a diamond to measure hardness and crushing it to assess its response to compression.

Finding ways to automate these labor-intensive steps has been a central challenge for the APEX team, one that is critical to the project’s success.

“Trying to not only determine what is really important to our research process but also how to turn that into something you can control a robot to do has been an interesting undertaking,” said LLNL research scientist Michael Juhasz.

For example, when a researcher grinds a sample, they do so by feel, tuning into the vibrational patterns of the machine and sample to ensure they are applying the right amount of pressure. This left the team wondering: How do you relay this highly developed skill to a robotic system? One creative solution the team came up with was to strap a vibration sensor to the machine to capture these “feelings” numerically.

“The sensor produces data in real time, allowing us to put numbers and parameters to the different vibrations, which can guide APEX when it’s grinding a sample,” said LLNL research scientist Alex Baker. “This project is really trying to understand where the automation complexity meets the material science complexity and then translating between the two.”

From sample fabrication to grinding and polishing to characterization, APEX works to collect data at every phase, generating a full history for each sample that passes through the platform. In the future, APEX aims to integrate this data with the Materials Acceleration Platform (MAP), combining physics-based models with machine learning to help design and improve the next round of APEX-produced samples.

The MAP is a framework for integrating computer models that uses algorithms to design optimal materials under different constraints (limitations related to how material properties interact and impact one another) in an effort to find an ideal composition that embodies all desired properties. This is particularly important because optimizing materials often involves trade-offs, where improving one property can sometimes lead to compromises in another. MAP helps strike a balance to achieve the best possible combination of properties.

While one model might excel at predicting a material’s hardness, another might be better suited for predicting its ductility.

“MAP connects all of these separate, siloed models and weaves them together to help us predict new materials that we haven’t tested yet,” said LLNL computational scientist Brandon Bocklund. “The vision of the project is to really close the loop of materials development and acceleration — letting the system learn and update the models autonomously.”

Currently in the early stages of its development, APEX is being trained to work with stainless steel alloys as the research team proves its feasibility.

“We chose stainless steel because it is very a well characterized alloy,” Juhasz said. “This makes it the ideal control, allowing us to ground what APEX is doing and perfect the system before diving deeper into our materials development research.”

The plan is to make APEX as extensible as possible by its anticipated completion in 2027, allowing researchers to adapt the platform to accommodate a variety of unique scientific challenges and incorporate new characterization or diagnostic techniques as needed.

This one-of-a-kind project exemplifies LLNL’s multidisciplinary approach to pushing the boundaries of scientific discovery, demonstrating that when experts from different fields come together, no problem is too “unsolvable.”

 “It’s been great seeing the synergies between deep scientific knowledge of the materials experts meshing with the technical expertise of the engineers,” Sage said.

Baker echoed this sentiment, “There have been many instances where we each could see the subject matter expertise the other side of the project was bringing.”

For more information: Lawrence Livermore National Laboratory

An experimental quantum chip may yield more robust qubits

Microsoft’s newly unveiled prototype chip, Majorana 1, has emerged as a strong contender in the race to build practical quantum computers. Unlike traditional computers, quantum machines promise to tackle complex problems—such as simulating vast molecular interactions for drug discovery—at unprecedented speeds. What sets Microsoft’s approach apart is its use of a rare state of matter, making it one of the most unconventional and potentially more stable methods among current quantum technologies, which often rely on manipulating atoms with lasers or magnetic fields.

The chip is the most advanced attempt yet to harness the properties of a strange type of particle that was first predicted to exist in the 1930s by Italian theoretical physicist Ettore Majorana, for whom Microsoft’s chip is named. Majorana’s particle, called a Majorana fermion, remained entirely theoretical for more than seventy years. That is, until researchers (many of them supported by the U.S. National Science Foundation) realized that thin-layered arrangements of certain solid materials might tease out the behavior of the elusive particles and perhaps even put them to use. One of those researchers was theoretical physicist Chetan Nayak.  

In 2000, Nayak was at UCLA, where he received an NSF Faculty Early Career Development (NSF CAREER) grant to explore the physical attributes and theoretical potential of systems that might be made with newly discovered materials now known as topological materials (more on those later). Nayak joined Microsoft in 2005, where he now leads the company’s effort to develop a functional quantum computer by physically implementing some of his NSF-funded theories.  

“NSF was really important in supporting my career in its early stages, without a doubt,” says Nayak. “NSF was there at the start.” 

Microsoft is one of many companies racing to produce a useful quantum computer. A powerful quantum computer could potentially help scientists find new superconducting materials, new medicines and other valuable substances hidden within the practically infinite variety of nature.  

“Genuinely transformative new technologies require entirely new concepts,” says Daryl Hess, program director in the NSF Division of Materials Research. Hess oversaw Nayak’s NSF CAREER grant from 2000 to 2006. “We can’t go down that road without bold new ideas thoughtfully woven together through theory and experiment.”

“We’re basically doing stuff at the one-qubit level,” says Nayak of the currently modest ability of the Majorana 1 to potentially control qubits, the basic unit of information in a quantum computer.  

“That’s pretty basic, but it’s a whole new type of qubit. It’s a whole new way of controlling qubits and doing computations.” 

Quantum mechanics was a young field when Ettore Majorana proposed the existence of a new particle in a 1937 paper. It details his mathematical exploration of equations created by British physicist Paul Dirac, which describe the properties of subatomic particles. From Majorana’s mathematical manipulations, out popped a strange theoretical particle that could be coursing through your body and you’d never know it: the now-eponymous Majorana fermion. 

A fermion is a category of elementary particle (the smallest, most basic particles of matter) that includes electrons, protons and neutrons. Unlike those more familiar fermions, Majorana’s equations predicted a particle that would not interact much with anything. That evasiveness would make Majorana fermions largely invisible to particle colliders and other instruments scientists use to detect and understand elementary particles.  

And so, Majorana’s particle remained entirely theoretical for over 70 years. It wasn’t until the 2010s that physicists came up with new experiments that might reveal the particles by using custom-made nanowires made of a newly found type of matter.

When cooled to near absolute zero and with certain voltages applied, researchers hypothesized that Majorana fermions might be detected at the tips of the nanowires, not as individual particles, but in the collective behavior of many electrons flowing on the surface of the wire.

Scientists call such collective particle behavior a “quasiparticle,” and it’s akin to the wave created by thousands of fans at a stadium standing and sitting in coordination. The key to evoking that stadium-like “wave” of quasiparticle behavior was in the stuff the nanowires were made of: a topological material.  

To the naked eye, a topological material might look indistinguishable from, say, a ceramic tile. But topological materials are very different from your bathroom tiles, and in a peculiar way.

The outer surface of a topological material has properties unlike its interior. And yet, the entire material (both surface and interior) is made of a single, uniform substance. Topological materials get their name from topology, a branch of mathematics that describes properties of shapes that do not change even when deformed in certain ways.  

Scientists have discovered a number of topological materials with seemingly contradictory, yet measurably different and persistent, surface properties — that distinction makes topological materials unique compared to all other known states of matter.

One type of topological material is called a topological insulator because electrical current flows only on its surface, while its interior remains electrically insulating. And, that surface-level flow of electrons is unimpeded by cutting, scratching or otherwise changing the material. Other topological materials allow the unrestricted flow of light or even sound along their surface. 

Microsoft’s Majorana 1 chip — a complex device that uses nanowires made of topological materials and operates at near absolute zero — is based on the idea that Majorana fermions can be used to create qubits. While the state, or value, of a regular computer bit can be either zero or one, a qubit can be in multiple states simultaneously. That uniquely quantum phenomenon is called superposition, and it’s key to how quantum computers can theoretically solve problems far too complex for even the fastest supercomputers in use today.  

While there are several different ways to create qubits, their superposition is generally fragile and easily spoiled by slight environmental changes, like temperature or light. Creating qubits using materials with topologically persistent properties is one way to potentially reduce that fragility.  

Topological computers have greater stability, explains Nayak. “Certain features are really robust and stable.”

Whether using topological materials or other techniques, it will take considerably more research and development before quantum computers are capable of overtaking classical computers.

“Quantum computers would help us understand, predict and discover yet more new states of matter and related phenomena,” says Hess. “Those currently undiscovered states may provide the foundations of as-yet unimagined quantum-based technologies.” 

From fundamental scientific discovery to application, realizing any transformative technology requires time, investment and sustained effort.

Some experts are skeptical about whether the Majorana 1 actually demonstrates functional qubits made from the elusive fermions first predicted by Ettore Majorana nearly 90 years ago. Nonetheless, Nayak and team at Microsoft are continuing to push ahead to validate and scale up the technology with the goal of eventually creating a practically useful quantum computer that is fast, controllable and stable.

“The process of discovery is long and complex, and it took years of hard work from many just to get to this point,” says Alex Klironomos, senior advisor in the NSF Division of Materials Research. “If you want to enable major technological advances, you have to take calculated scientific risks.”  

For more information: Nature

Image: A close-up of some of the intricate features on Microsoft’s Majorana 1 chip.(Credit: John Brecher for Microsoft)

Quantum atomic motion on metals leads to insights

Researchers at the Max Planck Society, Munich, have been exploring how atoms and molecules diffuse and react on metallic surfaces in a variety of technological applications related to chemical energy generation and storage. Their ability to simulate and predict this motion is crucial to understanding material degradation, chemical selectivity, and to optimizing the conditions of catalytic reactions.

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Argonne researchers develop new membrane technology to extract lithium from water

As global demand for lithium surges due to its critical role in electric vehicles, electronics, and defense technologies, concerns about supply and sustainability are mounting. In response, scientists at the U.S. Department of Energy’s Argonne National Laboratory—some of whom are also affiliated with the University of Chicago’s Pritzker School of Molecular Engineering—have developed an innovative membrane technology that efficiently extracts lithium from water. This breakthrough could help secure a more reliable and scalable lithium supply chain for the future.

“The new membrane we have developed offers a potential low-cost and abundant alternative for lithium extraction here at home,” said Seth Darling, chief science and technology officer for Argonne’s Advanced Energy Technologies directorate. He is also director of the Advanced Materials for Energy-Water Systems (AMEWS) Energy Frontier Research Center at Argonne and a PME senior scientist.

Right now, most of the world’s lithium comes from hard-rock mining and salt lakes in just a few countries, leaving supply chains vulnerable to disruption. Yet most of the Earth’s lithium is actually dissolved in seawater and underground salt water reserves. The problem? Extracting it from these unconventional sources has been prohibitively expensive, energy-hungry and inefficient. Traditional methods struggle to separate lithium from other, more abundant elements like sodium and magnesium.

In salt water, lithium and other elements exist as cations. These are atoms that have lost one or more electrons, giving them a positive electric charge. The key to efficient lithium extraction lies in filtering out the other cations based on both size and degree of charge.

The new membrane offers a promising low-cost solution. It’s made from vermiculite, a naturally abundant clay that costs only about $350 per ton. The team developed a process to peel apart the clay into ultrathin layers — just a billionth of a meter thick — and then restack them to form a kind of filter. These layers are so thin they’re considered 2D.

But there was a hitch: Untreated, the clay layers fall apart in water within half an hour due to their strong affinity to it. 

To solve this problem, researchers inserted microscopic aluminum oxide pillars between the layers, giving the structure the look of a high-rise parking lot under construction — with many solid pillars holding each ​“floor” in place. This architecture prevents collapse while neutralizing the membrane’s negative surface charge, a crucial step for subsequent modifications.

Next, sodium cations were introduced into the membrane, where they settled around the aluminum oxide pillars. This changed the membrane’s surface charge from neutral to positive. In water, both magnesium and lithium ions carry a positive charge, but magnesium ions carry a higher charge (+2) compared with lithium’s (+1). The membrane’s positively charged surface repels the higher charged magnesium ions more forcefully than it does the lithium ions. This difference allows the membrane to capture lithium ions more easily while keeping magnesium ions out.

To further refine performance, the team added even more sodium ions. This decreased the membrane’s pore size. The result is that the membrane allows the smaller ions like sodium and potassium to pass through while catching the larger lithium ions.

“Filtering by both ion size and charge, our membrane can pull lithium out of water with much greater efficiency,” said first author Yining Liu, a Ph.D. candidate at UChicago and a member of the AMEWS team. ​“Such a membrane could reduce our dependence on foreign suppliers and open the door to new lithium reserves in places we never considered.”

The researchers believe this breakthrough could have broader applications, from recovering other key materials like nickel, cobalt and rare earth elements, to removing harmful contaminants from water supplies.

“There are many types of this clay material,” said Liu. ​“We’re exploring how it might help collect critical elements from seawater and salt lake brines or even help clean up our drinking water.”

In a world increasingly shaped by access to clean water and secure supplies of critical materials, innovations like this may help power not just our devices, but our future.

For more information: Nature Materials

Image: Atomic structure of vermiculite membrane showing 2D layers supported by aluminum oxide pillars. Yellow balls are doped sodium ion. (Image by Argonne National Laboratory.)

Robotic probe quickly measures key properties of new materials

MIT researchers have developed a fully autonomous robotic system designed to accelerate the discovery of new semiconductor materials for solar cells and electronics. The system uses a robotic probe to automatically measure photoconductance—an essential property that indicates how a material responds electrically to light. By automating this process, the technology aims to overcome a major bottleneck in materials research, significantly speeding up the pace of innovation.

The researchers inject materials-science-domain knowledge from human experts into the machine-learning model that guides the robot’s decision making. This enables the robot to identify the best places to contact a material with the probe to gain the most information about its photoconductance, while a specialized planning procedure finds the fastest way to move between contact points.

During a 24-hour test, the fully autonomous robotic probe took more than 125 unique measurements per hour, with more precision and reliability than other artificial intelligence-based methods.

By dramatically increasing the speed at which scientists can characterize important properties of new semiconductor materials, this method could spur the development of solar panels that produce more electricity.

“I find this paper to be incredibly exciting because it provides a pathway for autonomous, contact-based characterization methods. Not every important property of a material can be measured in a contactless way. If you need to make contact with your sample, you want it to be fast and you want to maximize the amount of information that you gain,” says Tonio Buonassisi, professor of mechanical engineering and senior author of a paper on the autonomous system.

His co-authors include lead author Alexander (Aleks) Siemenn, a graduate student; postdocs Basita Das and Kangyu Ji; and graduate student Fang Sheng

Since 2018, researchers in Buonassisi’s laboratory have been working toward a fully autonomous materials discovery laboratory. They’ve recently focused on discovering new perovskites, which are a class of semiconductor materials used in photovoltaics like solar panels.

In prior work, they developed techniques to rapidly synthesize and print unique combinations of perovskite material. They also designed imaging-based methods to determine some important material properties.

But photoconductance is most accurately characterized by placing a probe onto the material, shining a light, and measuring the electrical response.

“To allow our experimental laboratory to operate as quickly and accurately as possible, we had to come up with a solution that would produce the best measurements while minimizing the time it takes to run the whole procedure,” says Siemenn.

Doing so required the integration of machine learning, robotics, and material science into one autonomous system.

To begin, the robotic system uses its onboard camera to take an image of a slide with perovskite material printed on it.

Then it uses computer vision to cut that image into segments, which are fed into a neural network model that has been specially designed to incorporate domain expertise from chemists and materials scientists.

“These robots can improve the repeatability and precision of our operations, but it is important to still have a human in the loop. If we don’t have a good way to implement the rich knowledge from these chemical experts into our robots, we are not going to be able to discover new materials,” Siemenn adds.

The model uses this domain knowledge to determine the optimal points for the probe to contact based on the shape of the sample and its material composition. These contact points are fed into a path planner that finds the most efficient way for the probe to reach all points.

The adaptability of this machine-learning approach is especially important because the printed samples have unique shapes, from circular drops to jellybean-like structures.

“It is almost like measuring snowflakes — it is difficult to get two that are identical,” Buonassisi says.

Once the path planner finds the shortest path, it sends signals to the robot’s motors, which manipulate the probe and take measurements at each contact point in rapid succession.

Key to the speed of this approach is the self-supervised nature of the neural network model. The model determines optimal contact points directly on a sample image — without the need for labeled training data.

The researchers also accelerated the system by enhancing the path planning procedure. They found that adding a small amount of noise, or randomness, to the algorithm helped it find the shortest path.

“As we progress in this age of autonomous labs, you really do need all three of these expertise — hardware building, software, and an understanding of materials science — coming together into the same team to be able to innovate quickly. And that is part of the secret sauce here,” Buonassisi says.

Once they had built the system from the ground up, the researchers tested each component. Their results showed that the neural network model found better contact points with less computation time than seven other AI-based methods. In addition, the path planning algorithm consistently found shorter path plans than other methods.

When they put all the pieces together to conduct a 24-hour fully autonomous experiment, the robotic system conducted more than 3,000 unique photoconductance measurements at a rate exceeding 125 per hour.

In addition, the level of detail provided by this precise measurement approach enabled the researchers to identify hotspots with higher photoconductance as well as areas of material degradation.

“Being able to gather such rich data that can be captured at such fast rates, without the need for human guidance, starts to open up doors to be able to discover and develop new high-performance semiconductors, especially for sustainability applications like solar panels,” Siemenn says.

The researchers want to continue building on this robotic system as they strive to create a fully autonomous lab for materials discovery.

For more information: Science Advances

Pattern Materials makes its mark in Houston

Alex Lathem, a graduate student at Rice University, has launched Pattern Materials, a startup focused on revolutionizing graphene production by making it faster, more affordable, and scalable. The company leverages Lathem’s proprietary laser-induced and flash graphene technologies, which enable the rapid creation of graphene and carbon nanotube-like patterns in a single step. These advanced materials, known for their exceptional conductivity, flexibility, and strength, have the potential to significantly enhance electronic devices such as sensors. Pattern Materials is already gaining traction, earning $134,500 and fourth place at the Rice Business Plan Competition, along with third place at Energy Venture Day during CERAWeek.

The technology was developed in the lab of Rice’s James Tour, professor of materials science and nanoengineering and the T.T. and W.F. Chao Professor of Chemistry, who discovered and has been innovating with graphene for more than a decade. He’s also an advisor to Pattern Materials.

“There’s a lot of graphene research out there now and it should be ready for commercialization – that’s the kind of bet that we’re making,” Lathem said.

To prepare for the pitch competitions, Lathem utilized Rice’s Liu Idea Lab for Innovation and Entrepreneurship (Lilie). Lilie is the home of experiential learning and co-curricular activities in entrepreneurship and innovation at Rice.

“We were still thinking too much like it was a thesis, and got a whole lot of feedback from investors saying ‘make it more clear what you’re doing,” Lathem said. “‘Focus on the product, focus on the solution.’”

Pattern Materials’ next focus is on working with sensor manufacturers to create pilot programs.

“Those are the key people we want to be working with, because our patterns basically could serve as the template or the backbone for those sensors,” he said. “In a sensor, there’s always some component that’s the actual sensitive material – that’s what graphene is really good for. Our intention is to replace that piece with our material, and so that will involve working with these manufacturers pretty closely to know what properties they need.”

The company plans to be based in Houston and work toward vertical integration. The city has a lot of interest in new technology and new manufacturing, Lathem said.

“The ceiling is very high for what we can do, the potential. We want to see how far we can take it, not just on domestic usage, but packaging,” he continued. “We believe in the material. We love the potential and we want to see how far we can take it and what impact we can have on not just domestic manufacturing, but sensor usage and making the world kind of a better, safer place in all the ways that sensors are used nowadays. And hopefully as well, it will be a great sort of example for what’s possible in Houston.”

For more information: Rice University

AI system helps researchers unlock hidden potential in newly discovered materials

Researchers at the University of Toronto Engineering have developed a new multimodal AI tool that could significantly accelerate the application of newly discovered materials. Led by Professor Seyed Mohamad Moosavi, the team’s study introduces an AI system capable of predicting how a material might perform in real-world conditions from the moment it is created—helping ensure that promising innovations reach their full potential.

The system focuses on a class of porous materials known as metal-organic frameworks (MOFs). Moosavi says that last year alone, materials scientists created more than 5,000 different types of MOFs, which have tunable properties that lead to a wide range of potential applications.

For example, MOFs can be used to separate CO2 from other gases in a waste stream, preventing the carbon from reaching the atmosphere and contributing to climate change. They can also be used to deliver drugs to particular areas of the body, or to add new functions to advanced electronic devices.

According to Moosavi, one major challenge facing the field is that a MOF created for one purpose often turns out to have the ideal properties for a completely different application.

For example, in one of their previous studies, it was found that a material originally synthesized for photocatalysis was instead very effective for carbon capture — but this discovery was only made seven years later.

“In materials discovery, the typical question is, ‘What is the best material for this application?’” says Moosavi.

“We flipped the question and asked, ‘What’s the best application for this new material?’ With so many materials made every day, we want to shift the focus from ‘what material do we make next’ to ‘what evaluation should we do next.’”

This approach aims to reduce the time lag between discovery and deployment of MOFs.

To help make this possible, ChemE PhD student Sartaaj Khan developed a multimodal machine learning system trained on various types of data typically available immediately after synthesis — specifically, the precursor chemicals used to make the material, and its powder X-ray diffraction (PXRD) pattern.

“Multimodality matters,” says Khan. “Just as humans use different senses — such as vision and language — to understand the world, combining different types of material data gives our model a more complete picture.”

The AI system uses a multimodal pretraining strategy to gain insights into a material’s geometry and chemical environment, enabling it to make accurate property predictions without needing post-synthesis structural characterization.

This can speed up the discovery process and help researchers recognize promising materials before they’re overlooked or shelved.

To test the model, the team conducted a ‘time-travel’ experiment. They trained the AI on material data available before 2017 and asked it to evaluate materials synthesized after that date.

The system successfully flagged several materials — originally developed for other purposes — as strong candidates for carbon capture. Some of those are now undergoing experimental validation in collaboration with the National Research Council of Canada.

Looking ahead, Moosavi plans to integrate the AI into the self-driving laboratories (SDLs) at U of T’s Acceleration Consortium, a global hub for automated materials discovery.

“SDLs automate the process of designing, synthesizing and testing new materials,” he says.

“When one lab creates a new material, our system could evaluate it — and potentially reroute it to another lab better equipped to assess its full potential. That kind of seamless inter-lab coordination could accelerate materials discovery.”

For more information: Nature Communications

Image: PhD student Sartaaj Takrim Khan, left, and Professor Seyed Mohamad Moosavi (ChemE) created a multimodal AI tool that can predict how metal-organic frameworks might perform in the real world. (Photo by Tyler Irving)

Symphony of Elements Exhibition at Sloss Furnaces Offers Metallography Education and Engagement

Against the backdrop of towering smokestacks and steel-laden history, the Sloss Furnaces National Historic Landmark, Birmingham, Ala., became a portal into the inner workings of modern metallurgy with the opening of the Symphony of Elements: Art and Science of Metals exhibition on May 23. This innovative showcase, running through August 2025, offers a rare and compelling fusion of technical achievements, advanced materials science, and visual art.

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Simulations reveal how grains in metals and ceramics grow

An international team of scientists headed by Prof. Marco Salvalaglio from TUD–Dresden University of Technology in Germany discovered that internal stresses—not just interface energy—play a key role in shaping the microstructure of crystalline materials. These findings challenge classical theories and may improve how we design materials for engineering and technology.

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Near-perfect defects in 2D material could serve as quantum bits

Scientists worldwide are striving to scale quantum technologies, which hinges on reliably generating qubits—the basic units of quantum information. One promising material for this is hexagonal boron nitride (h-BN), a two-dimensional substance known for hosting solid-state single-photon emitters (SPEs). These atomic structures can emit individual photons, making them strong candidates for building scalable quantum systems.

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Scientists discover an unusual chiral quantum state in a topological material

Chirality, or “handedness,” is a fundamental property where an object differs from its mirror image, seen across nature from molecules to DNA. In a breakthrough, Princeton University researchers have discovered a hidden chiral quantum state in a material previously believed to be non-chiral. This finding not only challenges existing assumptions in physics but also deepens our understanding of quantum phenomena, potentially opening new avenues in quantum research.

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TESCAN expands its presence in Asia

Czech-based electron microscope manufacturer TESCAN plans to establish a local subsidiary in Taiwan in 2025 to meet rising demand from semiconductor clients across the Asia-Pacific region.

Founded in Brno, the Czech Republic’s second-largest city, TESCAN built its reputation over three decades in fields like materials science and geoscience. In recent years, however, the company has pivoted toward the semiconductor industry, with a particular focus on the rapidly expanding advanced packaging segment.

TESCAN’s advanced packaging FA solution is built around a hybrid workflow that integrates scanning electron microscopy (SEM), focused ion beam (FIB), and other inspection tools into a seamless, cross-platform system. The setup aims to reduce testing time, cut labor requirements, and speed up R&D while improving yield outcomes.

Described as a “full-body checkup” for chips, the solution uses a suite of diagnostic tools—much like a team of medical specialists—to identify failure points across materials and structures. This approach has proven essential for OSAT providers, foundries, and IC design houses alike.

According to TESCAN Taiwan country manager Robert Feng, FA begins with non-destructive testing to locate potential defects without damaging the sample. The next phase involves destructive analysis using laser cutting for speed, followed by dual-beam systems to isolate and expose the faulty regions.

The process continues with SEM imaging via the dual-beam system to analyze interfaces and defect signatures. To address the rising need for structural stress and material composition analysis, TESCAN also provides a 4D STEM-enabled platform that measures internal stress fields and compositional shifts, supporting both process refinement and next-gen packaging evolution.
TESCAN’s semiconductor strategy—centered on failure analysis and advanced packaging—is gaining momentum thanks to integrated technologies and region-specific applications.

According to APAC managing director Sean Lee, the semiconductor business in Asia-Pacific contributed nearly 50% of the company’s global revenue in 2024. “There’s still plenty of room to grow,” he said.

For 2025, Lee projects a 40% revenue surge in APAC, fueled largely by Chinese demand, with semiconductor-related sales expected to account for about half of that growth.

As a challenger in the semiconductor equipment space, TESCAN is still trailing global leaders in market share. To gain ground, the company is leaning into product flexibility and differentiation.

Lee highlights technologies such as CoWoS, 2.5D/3D, and heterogeneous integration as major drivers of increased FA complexity. TESCAN’s strategy focuses on large-format and customized inspection demands, delivering broader and deeper coverage tailored to client-specific requirements.
TESCAN’s edge, Lee says, lies in its singular focus: “We only do electron microscopes.” Unlike competitors with sprawling product portfolios, the company offers more streamlined and responsive collaboration.

Most equipment vendors favor standardized models to maximize cost and production efficiency. TESCAN, however, starts with the unmet needs of leading customers and gradually scales into more price-sensitive segments—a strategy built on flexibility and differentiation.

Across the region, Lee says, packaging customers want FA tools that are faster, more precise, and competitively priced. TESCAN has targeted sample preparation, the bottleneck in the testing workflow, and introduced AI and machine learning to streamline it. The result: faster output, fewer manual errors, and relief for an industry plagued by skilled labor shortages.

Feng notes that training an operator in sample preparation and analysis typically takes six to twelve months. But with product lifecycles shrinking, delays are no longer acceptable. TESCAN’s solution reduces prep time from four hours to under one, even for first-time users.

Lee points out that Taiwan and China together account for over 70% of the global advanced packaging market. Many Chinese customers are Taiwan-owned or managed by Taiwanese executives, making Greater China the most critical hub for packaging technology and a core driver of TESCAN’s APAC expansion.

Although Lee concedes that launching the Taiwan office in 2025 is “a beat late” and would have been better timed two years earlier, he believes conditions remain favorable. As client technologies mature and US-China chip tensions intensify, China’s localization drive makes this an opportune moment.

Following the acquisitions of TESCAN Korea and anti-vibration system maker Daeil Microanalysis Laboratory (DML), the company will open new subsidiaries in Taiwan and Singapore in 2025. Moving away from agent-based distribution marks a major step in strengthening brand visibility and service capabilities across the APAC semiconductor market.

In the past, Taiwan clients relied on local agents for sales and service, which created delays in communicating feedback to TESCAN’s R&D hub in the Czech Republic, slowing development and impeding local adaptation.

To avoid missing out on co-innovation opportunities, TESCAN opted to establish its subsidiaries, enabling technical teams to work directly with clients. This move shortens communication loops, accelerates market responsiveness, and enhances local support across key APAC markets—including Taiwan, China, South Korea, and Malaysia—while deepening regional collaboration.

 

Image – Sean Lee (L) and Robert Feng (R). Courtesy of: DIGITIMES.

 

For more information:
TESCAN
https://www.tescan.com/

 

Seeing inside next-generation microelectronics using x-rays

As electronics shrink, researchers are turning to nanoscale materials like ultra-thin nanosheets for next-generation devices. Studying these tiny structures without damaging them is challenging, but scientists used a 12-nanometer-wide X-ray beam to examine them safely. This revealed two competing mechanisms behind how these nanosheets deform, offering insights crucial for advancing microelectronics.

Consumers want electronics that are ever smaller and faster. But the fabrication methods industry and researchers use to create tiny, high-power electronics are complex and can cause the nanostructures to have unwanted defects and deformations. Understanding these inner details—and doing so in a way that doesn’t cause further damage—is essential to determining how to use nanostructures in real-world applications. This study provides a non-destructive method for studying materials that yields insights into the structure of these devices at the nanoscale. It also opens a new avenue for developing novel nanoscale structures for electronics applications.

Nanosheets are used in tiny next-generation electronic components called Gate-All-Around Field Effect Transistors (GAAFETs). These components form the basis of computer microprocessors at the heart of smartphones and computers. In this study, a team of researchers from IBM collaborated with scientists from the National Synchrotron Light Source II (NSLS-II), a Department of Energy Office of Science user facility at Brookhaven National Laboratory, to map the deformations within nanosheets. The researchers investigated these structures using the Hard X-ray Nanoprobe (HXN) beamline at the NSLS-II light source.

By exploiting the brilliant source of X-rays and the resolving power provided by a nanofocusing optics setup called a multilayer Laue lens, the researchers were able to identify two competing mechanisms at different length scales that contribute to the deformation. The first, which is long-range and previously known, is due to the mismatch of lattice constant between the different elements and a relaxation effect near edges. The second, a much shorter-range effect, is associated with the layering itself and is dominant within a length scale of the nanosheet thickness from the edge. These new insights could help researchers predict essential performance parameters of future devices, such as the carrier mobility.

For more information: Nature Communications

Image: Artist’s impression of how X-rays make it possible to study the distortions of the layers in the microelectronics material. The atoms at the edges of the layers are either squished tighter or pulled apart, creating a bend along the different layers.

Testing and designing materials to perform better under stress

Nuclear fusion, the process powering the sun and stars, offers a promising path to carbon-free electricity without long-lasting nuclear waste, but it requires materials that can endure extreme heat, stress, and neutron damage. Researchers are exploring advanced metal alloys and ceramic composites as potential solutions to these challenges. At Stony Brook University, Assistant Professor David Sprouster is leading several research projects focused on overcoming the materials science and engineering hurdles critical to making fusion energy a practical reality.

“My research is really about stress testing these different materials to see how we can improve their function when exposed to different combinations of extremes,” said Sprouster. “It’s also fun to design them, to fabricate them in the lab, and then to break them.”

Sprouster and his team have received three multi-million dollar recent grants that focus on materials for fusion energy, with two from the Department of Energy, Office of Fusion Energy Sciences Fusion Innovation Research Engine (FIRE) Collaboratives.

In a recent study, Sprouster’s group compared two steels with similar alloy compositions, but fabricated in two different ways: one by traditional casting and the other through direct current sintering. In direct current sintering, both heat and pressure are used to rapidly convert powders into a solid monolithic material. As compared to conventional casting, this process allows the relatively complicated and graded structures of fusion chamber walls to be formed. Both fabricated steels are designed to resist deformation under heat and stress over time, a phenomenon known as “creep.”

“Creep is a very slow process — it happens over days, weeks, months and years — and depends on the applied stress and temperature,” said Sprouster. “It’s a tough moving target, but we have had success in designing the least ‘creepy’ materials, and to engineer the movement of dislocations, the defects within materials that allow plastic deformation to occur to improve our overall fundamental understanding of creep.”

Sprouster’s recent work concluded that both the conventionally cast and sintered materials showed equally good creep resistance. But they observed that when temperature increases, dislocations move more easily, which makes the material more prone to deformation. Equipped with this new knowledge, materials engineers can predict how these steels will perform under high-temperature service conditions, such as in fusion reactors.

In a second study, Sprouster’s group fabricated composites of steel with hafnium hydride through direct current sintering for neutron shielding applications within advanced nuclear fusion reactors. “The hydrogen is there to stop the neutrons. It has a very good cross-section for neutron absorption,” said Sprouster. “So, it basically takes most of the neutrons away so that you can shield the critical components that are close to the plasma.”

One of the key findings from this work was that upon heating, hafnium hydride breaks down and releases hydrogen, and the hafnium metal reacts with the iron to produce new intermetallic phases. However, due to the composite nature of this shield, the release is relatively show and at a much higher temperature than anticipated in the fusion reactor application.

These projects serve a shared purpose — to construct safer, more efficient and more durable materials for extreme nuclear environments for future fusion reactors.

“The fusion space has become an enormously attractive research area,” said Lance Snead, research professor in the Department of Materials Science and Chemical Engineering. “Historically, the Department of Energy was the primary agency funding this future energy source, but with the realization that fusion can be a near-term source of electricity, private investors now dominate the field.”

“Fusion is very exciting right now,” said Sprouster. “There’s a lot of activity and good collaborations across the universities, national labs and within industry. The community is very energetic and focused on materials science solutions to tough engineering problems.”

“Last year over 1.6 billion dollars in private research funding went into fusion, or three times that of the federal contribution,” said Snead. “As a key to the success of any of the current fusion concepts hinges on the ability to develop new and robust fusion chamber materials, Professor Sprouster has positioned his group in a very exciting growth area for research.”

Image: From left: Mingxi Ouyang, PhD student; Lance Snead, research professor; David Sprouster, assistant professor; and post-doctoral students Kent Christian and Jiao Li. Photos by John Griffin.

UC Irvine to lead use of AI in solving grand challenges below Earth’s surface

The University of California Office of the President has awarded $6 million over three years to a UC Irvine-led initiative called Geophysicist.AI, which aims to apply artificial intelligence to major geophysical challenges within Earth’s crust. The project focuses on advancing sustainable geothermal energy, underground carbon dioxide sequestration, and the safe, long-term storage of spent nuclear fuel, among other critical environmental goals.

“Tapping into Earth’s abundant but hard-to-reach deep geothermal energy and better understanding and potentially predicting induced seismicity, as well as other subsurface capabilities, will take new technologies and novel approaches, and we think AI and machine learning will help us in a substantial way,” said Geophysicist.AI lead principal investigator Mohammad Javad Abdolhosseini Qomi, UC Irvine associate professor of civil and environmental engineering and materials science and engineering.

“We intend to design Geophysicist.AI with the attributes of a skilled geophysicist, including the ability to integrate and analyze heterogeneous data and models, solve mathematical representations of coupled processes across scales, and formulate and test hypotheses to provide a deeper understanding and explanation of observed geophysical processes,” he added.

With UC Irvine researchers in the lead, the project will tap the expertise of civil and environmental engineers, geoscientists, mathematicians and computer scientists at UC campuses in Riverside, San Diego, Berkeley and Santa Cruz. Also participating will be scientists from Lawrence Livermore National Laboratory and Los Alamos National Laboratory.

“Our goal is to develop a scalable artificial intelligence ecosystem that integrates large language and physics-informed models with massive amounts of real-world data to transform geophysicists’ ability to solve the most difficult subsurface challenges,” said co-principal investigator Eric Mjolsness, UC Irvine professor of computer science. “Also, we believe that the development of Geophysicist.AI will require us to employ novel methods, so both the project and its outcome will be useful more broadly in scientific applications beyond geophysics.”

Russ Detwiler, UC Irvine associate professor of civil and environmental engineering, said that as co-principal investigator, he envisions the team using Geophysicist.AI to address two main grand challenges of geoengineering. The first is to help humanity tap into enhanced geothermal systems, which entails circulating fluid through low-permeability rock as much as 2.5 miles deep to extract heat and drive turbines. Detwiler said that this endeavor – aided by AI and machine learning – is complex due to the interplay of thermal, mechanical and chemical processes at multiple scales.

The second goal is to use AI and machine learning to help predict induced seismicity from engineering pursuits beneath Earth’s surface. The researchers think a problem of this intricacy is a good match for AI since it involves terabytes of seismic data being generated continuously.

A key source of data will be the Sanford Underground Research Facility in South Dakota. Geophysicist.AI scientists will join with counterparts at the Center for Understanding Subsurface Signals and Permeability, a U.S. Department of Energy Earthshot center managed through the Pacific Northwest National Laboratory, to gain access to this resource.

In addition, the team will take advantage of the Department of Energy’s multiphysics simulators, which run on DOE supercomputers. UC Irvine also has substantial high-performance computing capabilities and deep, interdisciplinary AI expertise that will benefit the project, Mjolsness said.

“Working with our principal investigators here at UC Irvine, scientists at the other UC campuses and collaborators at the national laboratories will enable us to generate sufficient preliminary proof-of-concept results, publish as a team and [promote] synergistic activities much like a full-blown research center,” Qomi said. “Our work over the next few years should put us in a good position to compete for future federal funding.”

For more information: University of California, Irvine
Image: Mohammad Javad Abdolhosseini Qomi (left), UC Irvine associate professor of civil and environmental engineering and materials science and engineering, is lead principal investigator on the Geophysicist.AI project, while colleagues Eric Mjolsness (center), professor of computer science, and Russ Detwiler (right), associate professor of civil and environmental engineering, are co-principal investigators.

Indian scientists find ‘quantum fingerprint’ for exotic materials

Scientists at the Raman Research Institute have made a breakthrough in quantum materials by discovering a novel method to identify a key property called a topological invariant—an unchanging characteristic even when a material is deformed. This property is essential for understanding the unusual behaviors of topological materials, which are foundational to future technologies like quantum computing, fault-tolerant electronics, and energy-efficient systems. Historically, detecting these unique traits has been a major challenge, making this advancement a significant step forward in the field.

To grasp the concept of topological invariance, scientists often use the analogy of a “vada” (South Indian snack) and a coffee cup. Both have a single hole, making them topologically equivalent – one can be continuously deformed into the other without cutting or gluing. In contrast, a vada and an “idli” (steamed rice cake) are not topologically equivalent, as they possess different numbers of holes, making continuous deformation impossible. This fundamental idea of “counting holes” is key to unlocking the hidden properties within these exotic materials.

In materials such as topological insulators and superconductors, electrons exhibit unusual behavior directly influenced by the material’s quantum “shape.” These shapes are defined not by their physical appearance but by deeper, intrinsic topological invariants, such as winding numbers in one-dimensional systems or Chern numbers in two-dimensional systems. These numbers act as a kind of hidden code, dictating how particles move through the material.

The RRI team, led by Professor Dibyendu Roy and PhD researcher Kiran Babasaheb Estake, has found an innovative method to detect this hidden code using a property called the spectral function. This function acts as a “quantum fingerprint,” providing insights into how energy and particles behave within the material. Their research specifically focused on analyzing the momentum-space spectral function (SPSF).

Traditionally, researchers relied on techniques like angle-resolved Photoemission Spectroscopy (ARPES) to study electron behaviour. The groundbreaking new research, recently published in Physical Review B, demonstrates that the same spectral function holds the keys to unlocking a material’s hidden topology. This offers a revolutionary way to “see” the underlying structure without direct observation.

“The spectral function has been used for many years as an experimental tool to probe physical quantities such as the density of states and the dispersion relation of electrons in a system through ARPES. It was not seen as a tool to probe topology or topological aspects of an electronic system,” stated Kiran Babasaheb Estake, a PhD student in theoretical Physics at RRI and the lead author of the study.

He added, “We have demonstrated through various examples that the spectral function also contains signatures about a system’s topology.”

This study potentially offers a universal tool for exploring and classifying topological materials. Its implications could pave the way for new discoveries in condensed matter physics, ultimately benefiting the development of quantum computers,next-generation electronics, and more energy-efficient systems.

For more information: Raman Research Institute
Image: Representation of what is topological equivalence

US scientists discover new 2D material that could be used in electrochemical energy tech

Nearly a decade ago, scientists predicted that boron atoms would bond too tightly to copper to form borophene—a promising, flexible, metallic 2D material with potential in electronics, energy, and catalysis. New research led by Rice University’s Boris Yakobson confirms this prediction, but in an unexpected way, offering fresh insights into the elusive material. Yakobson emphasized that since borophene remains on the edge of existence, every new discovery about it significantly advances our understanding of materials science, physics, and electronics.

“Our very first theoretical analysis warned that on copper, boron would bond too strongly, and even if borophene did form, it would be hopelessly attached to the substrate. Now, more than a decade later, it turns out we were right ⎯ and the result is not borophene, but something else entirely,” said Yakobson.

Researchers revealed that unlike systems such as graphene on copper, where atoms may diffuse into the substrate without forming a distinct alloy, the boron atoms in this case formed a defined 2D copper boride ⎯ a new compound with a distinct atomic structure. The finding sets the stage for further exploration of a relatively untapped class of 2D materials, according to researchers.

The research reveals that since the first realization of borophene on Ag(111), two-dimensional (2D) boron nanomaterials have attracted substantial interest because of their polymorphic diversity and potential for hosting solid-state quantum phenomena.

“Here, we use atomic-resolution scanning tunneling microscopy (STM) and field-emission resonance (FER) spectroscopy to elucidate the structure and properties of atomically thin boron phases grown on Cu(111). Specifically, FER spectroscopy reveals charge transfer and electronic states that strongly differ from the decoupled borophene phases observed on silver, suggesting that the deposition of boron on copper results in strong covalent bonding characteristic of a 2D copper boride,” said researchers.

Earlier, studies synthesized borophene on metals like silver and gold, but copper remained an open ⎯ and contested ⎯ case. Some studies also highlighted that the boron might form polymorphic borophene on copper, while others suggested it could phase-separate into borides or even nucleate into bulk crystals.

Researchers revealed that resolving these possibilities required a uniquely detailed investigation combining high-resolution imaging, spectroscopy and theoretical modeling.

Yakobson underlined that what experimentalists first saw were rich patterns of atomic resolution images and spectroscopy signatures, which required a lot of hard work of interpretation.

These efforts revealed a periodic zigzag superstructure and distinct electronic signatures, both of which deviated significantly from known borophene phases. A strong match between experimental data and theoretical simulations helped resolve a debate about the nature of the material that forms at the interface between the copper substrate and the near-vacuum environment of the growth chamber, according to a press release.

“2D copper boride is likely to be just one of many 2D metal borides that can be experimentally realized,” said Mark Hersam, Walter P. Murphy Professor of Materials Science and Engineering at Northwestern University and a co-corresponding author on the study.

“We look forward to exploring this new family of 2D materials that have broad potential use in applications ranging from electrochemical energy storage to quantum information technology.”

For more information: Science Advances

Image: Calculated charge redistribution in copper boride (Cu8B14), where teal represents charge depletion and yellow represents charge accumulation.

These contacts let you see in the dark with your eyes closed

Scientists have developed innovative contact lenses that allow both humans and mice to see infrared light by converting it into visible colors—without the need for bulky equipment or batteries. These transparent lenses enable users to perceive both regular and infrared light simultaneously and can detect multiple infrared wavelengths at once. Remarkably, the lenses perform even better with eyes closed due to infrared light’s superior penetration. In tests, mice avoided infrared light, and humans could interpret flickering codes and light directions, showcasing the lenses’ potential for practical applications.

“Our research opens up the potential for non-invasive wearable devices to give people super-vision,” says senior author Tian Xue, a neuroscientist at the University of Science and Technology of China. “There are many potential applications right away for this material. For example, flickering infrared light could be used to transmit information in security, rescue, encryption or anti-counterfeiting settings.”

The lenses use specially engineered nanoparticles that absorb invisible infrared light and convert it into light our eyes can see, typically in the 400 to 700 nanometer range. More specifically, the technology targets near-infrared light, which lies just beyond human vision, in the 800 to 1600 nanometer range.

In earlier studies, the team showed these particles could give mice infrared vision when injected directly into the eye. This time, they’ve achieved similar results using a much less invasive approach—by building the particles right into soft contact lenses.

To create the contact lenses, the team combined the nanoparticles with flexible, non-toxic polymers that are used in standard soft contact lenses. After showing that the contact lenses were non-toxic, they tested their function in both humans and mice.

They found that contact lens-wearing mice displayed behaviors suggesting that they could see infrared wavelengths. For example, when the mice were given the choice of a dark box and an infrared-illuminated box, contact-wearing mice chose the dark box whereas contact-less mice showed no preference. The mice also showed physiological signals of infrared vision: the pupils of contact-wearing mice constricted in the presence of infrared light, and brain imaging revealed that infrared light caused their visual processing centers to light up.

In humans, the infrared contact lenses enabled participants to accurately detect flashing morse code-like signals and to perceive the direction of incoming infrared light. “It’s totally clear cut: without the contact lenses, the subject cannot see anything, but when they put them on, they can clearly see the flickering of the infrared light,” said Xue.

“We also found that when the subject closes their eyes, they’re even better able to receive this flickering information, because near-infrared light penetrates the eyelid more effectively than visible light, so there is less interference from visible light.”

An additional tweak to the contact lenses allows users to differentiate between different spectra of infrared light by engineering the nanoparticles to color-code different infrared wavelengths. For example, infrared wavelengths of 980 nm were converted to blue light, wavelengths of 808 nm were converted to green light, and wavelengths of 1,532 nm were converted to red light. In addition to enabling wearers to perceive more detail within the infrared spectrum, these color-coding nanoparticles could be modified to help color blind people see wavelengths that they would otherwise be unable to detect.

“By converting red visible light into something like green visible light, this technology could make the invisible visible for color blind people,” says Xue.

Because the contact lenses have limited ability to capture fine details (due to their close proximity to the retina, which causes the converted light particles to scatter), the team also developed a wearable glass system using the same nanoparticle technology, which enabled participants to perceive higher-resolution infrared information.

Currently, the contact lenses are only able to detect infrared radiation projected from an LED light source, but the researchers are working to increase the nanoparticles’ sensitivity so that they can detect lower levels of infrared light.

“In the future, by working together with materials scientists and optical experts, we hope to make a contact lens with more precise spatial resolution and higher sensitivity,” says Xue.

For more information: Cell