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

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/