Study shows light can reshape atom-thin semiconductors for next-generation optical devices

Rice University researchers have discovered that light can induce a physical shift in the atomic lattice of transition metal dichalcogenides (TMDs), a class of atom-thin semiconductors. This tunable effect, observed in a Janus-type TMD, opens the door to technologies that use light instead of electricity, enabling faster, cooler computer chips, ultrasensitive sensors, and flexible optoelectronic devices.

“In nonlinear optics, light can be reshaped to create new colors, faster pulses or optical switches that turn signals on and off,” said Kunyan Zhang, a Rice doctoral alumna who is a first author on a study documenting the effect. “Two-dimensional materials, which are only a few atoms thick, make it possible to build these optical tools on a very small scale.”

TMDs are layered crystals made of a transition metal such as molybdenum and two layers of a chalcogen element like sulfur or selenium. Their combination of electrical conductivity, light absorption and mechanical flexibility has made them one of the most versatile classes of materials for next-generation electronics and optoelectronics.

Within this family, Janus materials stand out for their asymmetry — an idea reflected in their name. Like their mythological namesake, these materials have two different faces: The top and bottom atoms are made of different chemical species, creating an internal imbalance that gives the crystal a built-in electrical polarity, making it particularly sensitive to light and external forces.

“Our work explores how the structure of Janus materials affects their optical behavior and how light itself can generate a force in the materials,” Zhang said.

Using laser light of different colors, the team studied how a two-layer Janus TMD material — molybdenum sulfur selenide stacked on molybdenum disulfide — converts light through a process called second harmonic generation (SHG), in which the material emits light at twice the frequency of the incoming beam. They found that when the incoming light matched the material’s natural resonances, the doubled-frequency light pattern became distorted, signaling that the atoms inside were being displaced.

“We discovered that shining light on Janus molybdenum sulfur selenide and molybdenum disulfide creates tiny, directional forces inside the material, which show up as changes in its SHG pattern,” Zhang said. “Normally, the SHG signal forms a six-pointed ‘flower’ shape that mirrors the crystal’s symmetry. But when light pushes on the atoms, this symmetry breaks — the petals of the pattern shrink unevenly.”

The team traced the distortion to optostriction, a process in which the electromagnetic field of light itself exerts a mechanical push on atoms. In Janus materials, that push is amplified by strong coupling between the atomic layers, allowing even minute forces to produce measurable strain.

“Janus materials are ideal for this because their uneven composition creates an enhanced coupling between layers, which makes them more sensitive to light’s tiny forces — forces so small that it is difficult to measure directly, but we can detect them through changes in the SHG signal pattern,” Zhang said.

That sensitivity could make these materials useful far beyond the lab. Components that switch or route light using this principle could make optical chips faster and far more energy-efficient, since light-based circuits generate less heat than conventional electronics. The same responsiveness could be harnessed to create precise sensors capable of detecting the smallest vibrations or pressure changes or tunable light sources for advanced displays and imaging tools.

“Such active control could help design next-generation photonic chips, ultrasensitive detectors or quantum light sources ⎯ technologies that use light to carry and process information instead of relying on electricity,” said Shengxi Huang, an associate professor of electrical and computer engineering and materials science and nanoengineering at Rice and a corresponding author on the study. Huang is also a member of the Smalley-Curl Institute, the Rice Advanced Materials Institute and the Ken Kennedy Institute at Rice.

By showing how Janus TMDs’ built-in imbalance opens new ways to steer the flow of light, the study highlights how small structural features can unlock large technological potential.

For more information: ACS Nano

Image: Shengxi Huang is an associate professor of electrical and computer engineering and materials science and nanoengineering at Rice University. (Photo by Jeff Fitlow/Rice University)

Argonne-led Q-NEXT quantum center renewed for five years

The U.S. Department of Energy (DOE) has renewed funding for Q-NEXT, a National Quantum Information Science Research Center led by Argonne National Laboratory with SLAC National Accelerator Laboratory, for another five years. Backed by $125 million—$25 million in fiscal year 2026 and future funding subject to congressional approval—this investment ensures Q-NEXT will continue driving advancements in quantum information science and technology, reinforcing the United States’ leadership in this transformative field.

“Quantum information science is a cornerstone of the nation’s technological future, with the potential to transform industries including computing, healthcare and national security,” said inaugural Q-NEXT Director David Awschalom, who is also a senior scientist at Argonne, the director of quantum engineering at the University of Chicago Pritzker School of Molecular Engineering, and director of the Chicago Quantum Exchange. ​“Through our renewed mission, Q-NEXT will continue to push the boundaries of what is possible in quantum science, delivering the foundational knowledge, tools and technologies needed to ensure national leadership in this critical field.”

Awschalom has now assumed the role of chief science officer.

Q-NEXT’s mission is to unlock the future of quantum information by seamlessly integrating quantum and traditional information systems across optical networks. Building on its achievements over the past five years, the center will focus on demonstrating the potential of distributed quantum entanglement — a phenomenon where qubits, the fundamental unit of quantum information, remain connected even when separated by large distances.

“We’re building on the strong foundation we’ve laid over the past five years to take on our renewed mission — harnessing distributed entanglement to show what’s possible with scalable quantum platforms,” said Q-NEXT Director and Argonne scientist Martin Holt. ​“By uniting quantum technologies across optical networks, we will pave the way for systems capable of revolutionizing how we process, transmit and receive information.”

Q-NEXT brings together a strong network of partners: two DOE national laboratories, 11 leading universities and six tech companies. This cross-sector collaboration ensures that the center’s work is both innovative and practical, bridging the gap between scientific discovery and real-world application. Universities contribute expertise in quantum sensing and communication, while industry partners provide access to state-of-the-art prototypes and manufacturing capabilities. Together, Q-NEXT partners form a vibrant ecosystem that drives progress in quantum science.

“We envision quantum systems that work across chip-to-chip, lab-to-lab and city-to-city scales,” said Q-NEXT Deputy Director Jennifer Dionne, who is a professor of materials science and engineering and, by courtesy, of radiology at Stanford University and SLAC. ​“We’re excited to advance a shared set of hardware, software and protocols to make quantum networks and sensors efficient and practical.”

Q-NEXT’s renewed efforts will focus on three scientific goals:

  1. Communication — developing quantum communication networks that link devices across metropolitan areas. Q-NEXT aims to demonstrate algorithms that run across multiple remote, connected quantum processors.
  2. Sensing — using quantum entanglement to achieve unprecedented precision in sensing applications. Q-NEXT will demonstrate real-world uses, such as in medicine and navigation, as well as foundational scientific discoveries in gravitation and quantum mechanics where quantum entanglement gives a clear advantage in sensing and measurement.
  3. Materials — developing new approaches to integrating materials that can be scaled for industry use. Q-NEXT will tackle the most important challenges involved in merging distinct quantum materials systems with advanced functionality and integrating them into practical quantum devices.

Q-NEXT will pursue its goals through large-scale, team-based projects that combine materials science, device engineering and quantum physics theory. By leveraging world-class science facilities, Q-NEXT aims to deliver breakthroughs that will shape the future of quantum technology. These facilities include the Argonne and SLAC Quantum Foundries, the Stanford Synchrotron Radiation Lightsource and several DOE Office of Science user facilities: Argonne’s Advanced Photon Source and Center for Nanoscale Materials, the Argonne Leadership Computing Facility and SLAC’s Linac Coherent Light Source.

In addition to its scientific mission, Q-NEXT will continue to build the next generation of quantum scientists, engineers, technicians and other professionals. Through educational programs, internships and training opportunities such as the DOE Science Undergraduate Laboratory Internship and the Open Quantum Initiative Undergraduate Fellowship, the center is preparing students, early-career researchers and operations specialists to thrive in the rapidly growing quantum industry.

“By fostering a skilled workforce, Q-NEXT is ensuring that the U.S. remains at the forefront of quantum innovation,” Awschalom said.

Q-NEXT was established in 2020, and in its first five years, it led the establishment of the Argonne and SLAC Quantum Foundries. Together these two national facilities contribute to a robust supply chain of standardized materials and devices.

“We’ve built an active, cross-disciplinary collaboration that’s laid the groundwork for networked quantum information by intertwining computing, sensing and communication,” Holt said. ​“Over the next five years, we’ll strengthen these efforts, strategically coordinating with the other NQISRCs and the national quantum ecosystem to accelerate the arrival of transformative quantum technologies.”

Collaboration with industry has been essential to the center’s mission — turning cutting-edge research into innovations that can transform technology and strengthen the nation’s quantum leadership.

“IBM’s work with the National Quantum Information Science Research Centers, such as Q-NEXT led by Argonne, is vital to our mission to build the future of computing,” said Jay Gambetta, director of IBM Research and IBM Fellow. IBM is a Q-NEXT partner. ​“Together, we are exploring how efficient quantum networks can be created through optical links connected to IBM’s quantum networking units. This could deliver the fundamental technology needed to link the multiple, interconnected fault-tolerant quantum computers of the future over kilometer distances, allowing the convergence of quantum computation and communication within a future quantum computing internet that could revolutionize scientific and industrial discovery.”

Q-NEXT is one of five DOE National Quantum Information Science Research Centers renewed for another five years.

“Quantum technologies are driving innovations across society, and our laboratory is focusing on delivering science breakthroughs to accelerate these innovations,” said Argonne Director Paul Kearns. ​“With a renewed Q-NEXT, we will continue to play an integral role in the national quantum ecosystem through coordinated, complementary efforts with DOE’s other quantum research centers. We are committed to realizing the promise of quantum information to build a more connected world and shape a future of scientific progress that strengthens our nation’s security, prosperity and technological leadership.”

For more information: Argonne National Laboratory

Image: The DOE has renewed the Q-NEXT quantum center for five years with a $125 million investment. One of Q-NEXT’s major first-run accomplishments was the establishment of two national quantum foundries — one at Argonne (pictured) and one at SLAC. (Image by Argonne National Laboratory.)

New ORNL aluminum alloy to strengthen domestic auto supply chain

Over the next decade, large amounts of aluminum auto body scrap will enter salvage systems, but its impurities have traditionally limited reuse in critical automotive parts. Researchers at the Department of Energy’s Oak Ridge National Laboratory (ORNL) have addressed this challenge by developing RidgeAlloy, an innovative aluminum alloy that transforms low-value scrap into high-quality material for structural vehicle components. Produced by remelting and recasting post-consumer aluminum, RidgeAlloy meets strength, ductility, and crashworthiness standards, creating a sustainable domestic supply chain. This breakthrough supports DOE’s critical materials goals, as aluminum is essential for energy technologies that produce, transmit, store, and conserve energy.

“The team advanced from a paper concept to a successful, full-scale part demonstration of a new alloy in only 15 months,” said Allen Haynes, director of ORNL’s Light Metals Core Program. “That’s an unheard-of pace of innovation in developing complex structural alloys.” 

Aluminum-intensive vehicles entered the U.S. market around 2015, with Ford’s F-150 truck series among the first to be mass produced. By the early 2030s, many of these vehicles are projected to reach end-of-life, creating a surge of high-quality aluminum body sheet scrap — up to 350,000 tons annually in North America. Much of this sheet scrap is expected to be downcycled into low-grade castings or exported, which is a missed opportunity to use those resources as a source of high-quality domestic aluminum. 

“You can repurpose post-consumer aluminum into something non-structural like engine blocks,” said Alex Plotkowski, ORNL group leader of Computational Coupled Physics. “But it won’t have the properties needed for higher value, structurally sound body applications.” 

That’s because the vehicle shredding process introduces impurities, such as iron, from various parts, including fasteners like rivets. This makes the scrap chemistry too unpredictable and low performing for commercial automotive structural alloys. As a result, most lightweight parts are made using primary aluminum, which is produced from raw ore in an energy-intensive process. 

While primary aluminum is mostly imported, the U.S. has some of the world’s best infrastructure for vehicle shredding and aluminum scrap recovery. 

“Using remelted scrap instead of primary aluminum is estimated to result in up to 95% reduction in the energy needed for processing a part,” said Amit Shyam, leader of ORNL’s Alloy Behavior and Design Group. 

To make that possible, the team applied world-class scientific tools such as high-throughput computing, which involved more than two million calculations to predict the optimal alloy compositions with targeted properties, as well as materials characterization and neutron diffraction at ORNL’s Spallation Neutron Source, a DOE Office of Science user facility. These tools helped the researchers understand how specific impurities affect alloy behavior. Neutrons are uniquely suited for this kind of research because they can penetrate deep into dense metals without damaging the material, allowing scientists to observe internal structures and atomic-scale changes. 

After pinpointing the desired blend through rapid computational predictions and laboratory trials, the new alloy was tested in a real-world environment. PSW Group’s Trialco Aluminum in Chicago supplied recycled aluminum ingots, metal blocks ready for remelting, cast from mixed auto body sheet scrap and tailored to RidgeAlloy’s specifications. The ingots were shipped to Falcon Lakeside Manufacturing in Michigan, where they were successfully cast into automotive parts using high-pressure die-casting. 

“The part we chose was medium-sized and moderately complex,” Plotkowski said. “The ultimate goal is to eventually cast larger parts, perhaps even automotive giga-castings, but this is the first step.” 

The cast parts confirmed that RidgeAlloy, consisting of aluminum, magnesium, silicon, iron and manganese, had the combination of properties necessary for structural vehicle castings, even when made from recycled blends with higher iron and silicon content. It delivers strength, corrosion resistance and ductility, enabling the production of structural castings of underbodies, frame components and other critical parts from post-consumer aluminum scrap. This breakthrough offers the opportunity to reshape the value equation of how North American auto body sheet scrap is sorted and reused.

“This team figured out how to take full advantage of a national lab’s world-class suite of capabilities to rapidly fill a huge gap in our understanding of lightweight automotive materials,” Haynes said. 

By the early 2030s, RidgeAlloy could enable recycled structural castings at volumes equal to at least half of the annual primary aluminum production in the U.S. This would reduce energy use, cut costs and strengthen domestic supply chains.

“RidgeAlloy offers the first technology capable of recapturing the value of a fast-approaching and historically massive wave of domestic, high-quality recycled automotive aluminum sheet alloys,” Haynes said. “That’s the big picture supply chain impact our team aimed for.”

There is also potential for future applications in industrial machinery, agricultural equipment, aerospace, mobile power generation equipment, off-road vehicles such as snowmobiles, motorcycles, and marine vehicles including jet skis.

For more information: Oak Ridge National Laboratory

Image: This automotive part was manufactured from RidgeAlloy, a new structural alloy developed by researchers at ORNL. It was cast using metals recycled entirely from post-consumer aluminum auto body sheets. Credit: ORNL, U.S. Dept. of Energy

Scientists turn common semiconductor into a superconductor

Researchers have long sought to make semiconductors—essential for computer chips and solar cells—function as superconductors, which can carry electricity without resistance for faster, more efficient performance. This has been difficult because it requires a precise atomic structure that enables free electron movement. Now, an international team has achieved a breakthrough by creating a form of germanium that exhibits superconductivity, allowing electric currents to flow indefinitely without energy loss. This advancement could significantly enhance electronic and quantum devices while reducing power consumption.

“Establishing superconductivity in germanium, which is already widely used in computer chips and fiber optics, can potentially revolutionize scores of consumer products and industrial technologies,” explains Javad Shabani, a physicist at New York University and director of its Center of Quantum Information Physics and Quantum Institute.

Peter Jacobson, a physicist at the University of Queensland, adds that the findings could accelerate progress in building practical quantum systems. “These materials could underpin future quantum circuits, sensors, and low-power cryogenic electronics, all of which need clean interfaces between superconducting and semiconducting regions,” he says. “Germanium is already a workhorse material for advanced semiconductor technologies, so by showing it can also become superconducting under controlled growth conditions there’s now potential for scalable, foundry-ready quantum devices.”

Germanium and silicon, both group IV elements with diamond-like crystal structures, occupy a unique position between metals and insulators. Their versatility and durability make them central to modern manufacturing. To induce superconductivity in such elements, scientists must carefully alter their atomic structure to increase the number of electrons available for conduction. These electrons then pair up and move through the material without resistance — a process that is notoriously difficult to fine-tune on the atomic scale.

In the new study, researchers developed germanium films heavily infused with gallium, a softer element commonly used in electronics. This technique, known as “doping,” has long been used to modify a semiconductor’s electrical behavior. Normally, high levels of gallium destabilize the crystal, preventing superconductivity.

The team overcame this limitation using advanced X-ray methods to guide a refined process that encourages gallium atoms to take the place of germanium atoms in the crystal lattice. Although this substitution slightly distorts the crystal, it preserves its overall stability and allows it to carry current with zero resistance at 3.5 Kelvin (about -453 degrees Fahrenheit), confirming that it had become superconducting.

“Rather than ion implantation, molecular beam epitaxy was used to precisely incorporate gallium atoms into the germanium’s crystal lattice,” says Julian Steele, a physicist at the University of Queensland and a co-author of the study. “Using epitaxy — growing thin crystal layers — means we can finally achieve the structural precision needed to understand and control how superconductivity emerges in these materials.”

As Shabani notes, “This works because group IV elements don’t naturally superconduct under normal conditions, but modifying their crystal structure enables the formation of electron pairings that allow superconductivity.”

For more information: Nature Nanotechnology

Image: Josephson junction structures—quantum devices made of two superconductors and a thin non-superconducting barrier—using different forms of germanium (Ge): super-Ge (in gold), semiconducting Ge (in blue), and super-Ge on wafer-level scale. Millions of Josephson junction pixels (10 micrometer square) can be created with this new material stack on wafer scale. Inset shows crystalline form of Super-Ge on the same matrix of semiconductor Ge, a key for crystalline Josephson junction. Credit: Patrick Strohbeen/NYU

Aichelin Group to acquire Nitrex Heat Treating Solutions and UPC-Marathon

Aichelin Group, Mödling, Austria, has signed an agreement to acquire the Nitrex Heat Treating Solutions (NTS) and UPC-Marathon (UPC) divisions of Montreal-based Nitrex. The acquisition aims to strengthen Aichelin’s global footprint in heat treatment systems and expand its technology portfolio with advanced nitriding and process control capabilities.

The strategic move brings together two highly complementary product and service lines. Nitrex is recognized for its expertise in nitriding and vacuum technologies, while UPC-Marathon contributes advanced control systems and process automation platforms. These capabilities will integrate with Aichelin’s broad offerings in industrial heat treatment equipment, enabling a more comprehensive range of solutions for global manufacturers.

Christian Grosspointner, chief executive officer of Aichelin Group, stated that the acquisition marks a significant step in shaping the future of industrial heat treatment by aligning trusted brands and decades of innovation. He emphasized that the combined group will deliver enhanced reliability, energy efficiency, and digitally connected heat treatment solutions tailored to evolving customer needs.

With this acquisition, Aichelin reinforces its focus on innovation, sustainability, and digital transformation. The addition of Nitrex and UPC-Marathon will accelerate the development of AI-enabled platforms such as QMULUS.ai, which help customers optimize operational performance, reduce costs, and improve overall equipment effectiveness across diverse industrial applications.

Read further here. https://www.nitrex.com/en/aichelin-group-signs-agreement-to-acquire-nitrex-heat-treating-solutions-and-upc-marathon/

One Minute Mentor: Transformation behavior of Austenite

The transformation behavior of retained austenite in M2 steel was examined under varying tempering conditions. The steel was initially subjected to interrupted quenching at 105 °C (225 °F), a process that elevated the retained austenite content to approximately 55 vol %, compared to the typical 25 vol % observed after direct quenching to room temperature. This was followed by tempering at 565 °C (1050 °F), without intermediate cooling. Results indicated that double tempering was significantly more effective than single tempering at transforming retained austenite under these conditions. Furthermore, repeated tempering cycles accelerated the complete transformation of retained austenite, achieving full conversion in a shorter total processing time than single-stage tempering.

For more information, click on the link below (subscription required). Then scroll to Figure 5.Jon L. Dossett; George E. Totten, Control of Distortion in Tool Steels, ASM International, 2014 https://doi.org/10.31399/asm.hb.v04d.9781627081689

SECO/WARWICK India Wins New Contract as Shital Vacuum Treat Adds Third Vacuum Furnace

SECO/WARWICK India has secured a new order from Shital Vacuum Treat Pvt Ltd, marking the third time the Indian heat-treatment specialist has chosen SECO/WARWICK’s vacuum furnace technology. The newly ordered furnace will support a wide range of processes including vacuum hardening, tempering, solution treatment, aging, annealing, brazing, and high-pressure gas quenching. Designed to meet NADCA (North American Die Casting Association) global standards, the equipment also positions Shital Vacuum Treat for future NADCAP certification.

As a long-standing provider of heat-treatment services to India’s automotive, aerospace, toolmaking, and machinery sectors, Shital Vacuum Treat plays a critical role in enabling high-performance manufacturing for small and medium-sized enterprises. This latest investment is driven by increased demand and reflects the company’s ongoing commitment to quality and process excellence.

The furnace will be fully manufactured under the “Made in India” initiative at SECO/WARWICK’s Pune facility, located just 25 kilometers from Shital’s headquarters. “Shital Vacuum Treat is a long-standing partner, promoter, and a true ambassador of SECO/WARWICK technology in the Indian market,” said Arvind Agarwal, Managing Director of SECO/WARWICK India. “Their third investment in our equipment demonstrates their continued trust in our solutions and underscores our shared commitment to advancing India’s industrial capabilities.”

Read further here. https://www.secowarwick.com/en/news/secowarwick-india-secures-a-new-contract/

Ipsen promotes seven field service engineers to senior roles

Ipsen, Cherry Valley, IL, announced the promotion of seven field service engineers to the role of senior field service engineer, recognizing their technical expertise, leadership, and dedication to customer service. The newly promoted team members include Matt Hopkins, Jesse Lawrence, Larry Dahm, Glenn Hawkins, Mike Dawson, Daniel Greifemberg, and Craig Ludewig.

These promotions reflect Ipsen’s ongoing commitment to developing a highly skilled service workforce and upholding the company’s reputation for quality, reliability, and customer trust. Each of the new senior engineers brings extensive hands-on experience supporting industrial heat-treating systems across domestic and international markets.

Lu Chouraki, field service manager at Ipsen, noted that the role of a field service engineer extends beyond technical problem-solving to include communication and professionalism that foster customer confidence. John Dykstra, chief service officer, emphasized that these engineers often serve as the face of Ipsen, and their consistent recognition by both customers and peers speaks to their impact.

Collectively, the promoted engineers have decades of experience across vacuum and atmosphere furnace systems. In their new roles, they will continue mentoring junior technicians, leading complex installations and troubleshooting efforts, and further enhancing Ipsen’s global service capabilities.

According to Chouraki, the promotions not only acknowledge individual contributions but also raise the standard across the organization. As senior field service engineers, they will play a key role in advancing Ipsen’s service excellence and customer relationships.

Read further here: https://ipsenglobal.com/knowledge-center/ipsen-elevates-seven-to-senior-field-service-roles/

Solar Atmospheres adds new 10-bar vacuum furnace at South Carolina facility

Solar Atmospheres, Greenville, SC, announced the installation and commissioning of a new 10-bar vacuum furnace at its Southeast facility. Designed and built by sister company Solar Manufacturing, the advanced horizontal furnace expands the company’s capacity to process large, high-performance materials, particularly titanium and specialty alloys.

The furnace features a working zone measuring 48 inches wide by 48 inches high by 96 inches deep, with the ability to handle loads up to 12,000 pounds. Equipped with a high-efficiency vacuum pumping system, the furnace achieves an ultimate vacuum level of 1×10⁻⁶ Torr, enabling precise processing in clean environments critical for aerospace and other high-specification applications.

Steve Prout, president of Solar Atmospheres Southeast, noted that the new equipment enhances the company’s regional offerings for high-pressure quenching while improving cost efficiency through scale. He emphasized that the addition reflects Solar Atmospheres’ continued focus on innovation, quality, and customer support.

https://solaratm.com/solar-atmospheres-commissions-new-10-bar-vacuum-furnace-in-greenville-sc/

Checking the quality of materials just got easier with a new AI tool

Advancing technologies like batteries, electronics, and pharmaceuticals relies on discovering and verifying new materials, a process traditionally slowed by costly, time-consuming quality checks using specialized instruments. While AI has accelerated material discovery by identifying promising candidates from vast databases, MIT engineers have now developed a new AI tool that streamlines the verification process, potentially reducing delays and costs in materials-driven industries.

In a new study the researchers present “SpectroGen,” a generative AI tool that turbocharges scanning capabilities by serving as a virtual spectrometer. The tool takes in “spectra,” or measurements of a material in one scanning modality, such as infrared, and generates what that material’s spectra would look like if it were scanned in an entirely different modality, such as X-ray. The AI-generated spectral results match, with 99 percent accuracy, the results obtained from physically scanning the material with the new instrument.

Certain spectroscopic modalities reveal specific properties in a material: Infrared reveals a material’s molecular groups, while X-ray diffraction visualizes the material’s crystal structures, and Raman scattering illuminates a material’s molecular vibrations. Each of these properties is essential in gauging a material’s quality and typically requires tedious workflows on multiple expensive and distinct instruments to measure.

With SpectroGen, the researchers envision that a diversity of measurements can be made using a single and cheaper physical scope. For instance, a manufacturing line could carry out quality control of materials by scanning them with a single infrared camera. Those infrared spectra could then be fed into SpectroGen to automatically generate the material’s X-ray spectra, without the factory having to house and operate a separate, often more expensive X-ray-scanning laboratory.

The new AI tool generates spectra in less than one minute, a thousand times faster compared to traditional approaches that can take several hours to days to measure and validate.

“We think that you don’t have to do the physical measurements in all the modalities you need, but perhaps just in a single, simple, and cheap modality,” says study lead Loza Tadesse, assistant professor of mechanical engineering at MIT. “Then you can use SpectroGen to generate the rest. And this could improve productivity, efficiency, and quality of manufacturing.”

The study was led by Tadesse, with former MIT postdoc Yanmin Zhu serving as first author.

Tadesse’s interdisciplinary group at MIT pioneers technologies that advance human and planetary health, developing innovations for applications ranging from rapid disease diagnostics to sustainable agriculture.

“Diagnosing diseases, and material analysis in general, usually involves scanning samples and collecting spectra in different modalities, with different instruments that are bulky and expensive and that you might not all find in one lab,” Tadesse says. “So, we were brainstorming about how to miniaturize all this equipment and how to streamline the experimental pipeline.”

Zhu noted the increasing use of generative AI tools for discovering new materials and drug candidates, and wondered whether AI could also be harnessed to generate spectral data. In other words, could AI act as a virtual spectrometer?

A spectroscope probes a material’s properties by sending light of a certain wavelength into the material. That light causes molecular bonds in the material to vibrate in ways that scatter the light back out to the scope, where the light is recorded as a pattern of waves, or spectra, that can then be read as a signature of the material’s structure.

For AI to generate spectral data, the conventional approach would involve training an algorithm to recognize connections between physical atoms and features in a material, and the spectra they produce. Given the complexity of molecular structures within just one material, Tadesse says such an approach can quickly become intractable.

“Doing this even for just one material is impossible,” she says. “So, we thought, is there another way to interpret spectra?”

The team found an answer with math. They realized that a spectral pattern, which is a sequence of waveforms, can be represented mathematically. For instance, a spectrum that contains a series of bell curves is known as a “Gaussian” distribution, which is associated with a certain mathematical expression, compared to a series of narrower waves, known as a “Lorentzian” distribution, that is described by a separate, distinct algorithm. And as it turns out, for most materials infrared spectra characteristically contain more Lorentzian waveforms, while Raman spectra are more Gaussian, and X-ray spectra is a mix of the two.

Tadesse and Zhu worked this mathematical interpretation of spectral data into an algorithm that they then incorporated into a generative AI model.

“It’s a physics-savvy generative AI that understands what spectra are,” Tadesse says. “And the key novelty is, we interpreted spectra not as how it comes about from chemicals and bonds, but that it is actually math — curves and graphs, which an AI tool can understand and interpret.”

The team demonstrated their SpectroGen AI tool on a large, publicly available dataset of over 6,000 mineral samples. Each sample includes information on the mineral’s properties, such as its elemental composition and crystal structure. Many samples in the dataset also include spectral data in different modalities, such as X-ray, Raman, and infrared. Of these samples, the team fed several hundred to SpectroGen, in a process that trained the AI tool, also known as a neural network, to learn correlations between a mineral’s different spectral modalities. This training enabled SpectroGen to take in spectra of a material in one modality, such as in infrared, and generate what a spectra in a totally different modality, such as X-ray, should look like.

Once they trained the AI tool, the researchers fed SpectroGen spectra from a mineral in the dataset that was not included in the training process. They asked the tool to generate a spectra in a different modality, based on this “new” spectra. The AI-generated spectra, they found, was a close match to the mineral’s real spectra, which was originally recorded by a physical instrument. The researchers carried out similar tests with a number of other minerals and found that the AI tool quickly generated spectra, with 99 percent correlation.

“We can feed spectral data into the network and can get another totally different kind of spectral data, with very high accuracy, in less than a minute,” Zhu says.

The team says that SpectroGen can generate spectra for any type of mineral. In a manufacturing setting, for instance, mineral-based materials that are used to make semiconductors and battery technologies could first be quickly scanned by an infrared laser. The spectra from this infrared scanning could be fed into SpectroGen, which would then generate a spectra in X-ray, which operators or a multiagent AI platform can check to assess the material’s quality.

“I think of it as having an agent or co-pilot, supporting researchers, technicians, pipelines and industry,” Tadesse says. “We plan to customize this for different industries’ needs.”

The team is exploring ways to adapt the AI tool for disease diagnostics, and for agricultural monitoring through an upcoming project funded by Google. Tadesse is also advancing the technology to the field through a new startup and envisions making SpectroGen available for a wide range of sectors, from pharmaceuticals to semiconductors to defense.

For more information: Matter

Image: The circle with the chip symbolizes SpectroGen, with the connecting threads depicting the process of generating a material’s spectrum.

Physics-based machine learning could unlock better 3D-printed materials

An NSF-funded initiative led by Lehigh University’s Parisa Khodabakhshi is advancing machine learning models that integrate physical laws to accelerate the design of high-performance alloys for aerospace, automotive, and healthcare applications. This effort complements additive manufacturing—also known as 3D printing—which constructs objects layer by layer using materials like metals, polymers, and biomaterials, offering a powerful combination for next-generation engineering solutions.

“This layer-by-layer approach allows for the fabrication of parts with complex geometries that are often difficult, or even impossible, to achieve with conventional manufacturing methods,” says Parisa Khodabakhshi, an assistant professor of Mechanical Engineering and Mechanics at Lehigh University’s P.C. Rossin College of Engineering and Applied Science. “However, the thermomechanical properties of the final additively manufactured parts are influenced by a large number of process parameters, making design optimization particularly challenging.”

Establishing the map between variations in process parameters and the final part’s properties requires several simulations across a wide range of length scales, making the task computationally expensive. “The computational demands of performing all the necessary simulations make it impractical,” says Khodabakhshi. As a result, manufacturers often resort to trial-and-error methods to achieve desired thermal or mechanical properties in the end product.

“However, you cannot fully explore the entire design space that way to find the optimal design, which is why we’re currently not able to utilize the full potential of additive manufacturing.”

Khodabakhshi recently received a three-year, $350,000 grant from the National Science Foundation to develop a computationally efficient model that accurately predicts how additive manufacturing process parameters influence the solidification microstructure, which in turn determines the properties of the final part. Specifically, Khodabakhshi will develop a physics-based, data-driven reduced-order model for predicting microstructure evolution in binary alloy solidification (or when a mixture of two metals changes from liquid to solid). 

“For example, say I want a part that has specific thermal properties,” she says. “I don’t know what my process parameters should be to achieve those properties. The simulations that link given process parameters to the resulting solidification microstructure, and consequently the final properties of the built part, are highly nonlinear. We refer to this simulation as the forward map. From there, I can construct the inverse map, which connects desired properties back to the process parameters.” The NSF project focuses on developing a computationally efficient model for the process-structure (PS) relationship.

The ultimate goal is to optimize the manufacturing of additively manufactured parts, which are especially useful in the aerospace, automotive, and healthcare industries. Fields in which confidence in manufacturing is paramount.

Her team’s approach uses a scientific machine learning framework that blends data-driven machine learning algorithms with physical laws. 

“As engineers, we don’t want to just train a black-box algorithm,” says Khodabakhshi. “We want to embed physics into the problem to satisfy the governing equations of the physical phenomena so that we’re confident about the output that we receive from the from the algorithm. That’s the difference between conventional machine learning and scientific machine learning.”

For more information: Lehigh University

Image: Parisa Khodabakhshi, assistant professor, mechanical engineering and mechanics

New model reveals the hidden structure of everyday materials

Scientists are working to better understand how components in mixed materials like concrete or underground rock are distributed, which could lead to stronger materials and safer storage of substances like carbon dioxide or nuclear waste. A key tool in this effort is the Poisson model, which randomly divides space using flat surfaces to simulate how materials mix. Although useful in fields like radiation transport, the model lacked precise formulas to describe relationships between multiple points—limiting its accuracy and application in complex systems until recent breakthroughs addressed this gap.

In a new study, Stanford researchers introduced a clever math method that helps reveal what a material is made of, just by knowing details from one random spot. They used a well-known statistical model, called the Poisson model, which randomly divides space, to study materials such as sand and concrete. This new approach enables scientists to understand the tiny structure of these materials with very high accuracy, which could aid in designing stronger and more reliable materials.

Lead study author Alec Shelley said, “With this study, we’ve solved the famous Poisson model for heterogenous materials.”

“Our result could have a broad impact on several areas of science, because heterogenous materials are common and their models almost never have exact solutions.”

The tiny structure inside materials affects how strong, durable, and useful they are. Thanks to the new research, scientists can now understand these structures more precisely.

“What Alec has succeeded in doing in this study is quite remarkable,” said Daniel Tartakovsky, a professor of energy science and engineering. “Using his approach, you could design a composite material to your specifications and obtain certain properties based on the proper mixture of components.”

Shelley and Tartakovsky plan to use their new math method to predict what different materials are made of. Their model can reveal a long list of important properties that depend on a material’s tiny inner structure, such as hardness and elasticity, tensile strength (how much it can stretch before breaking), electrical and heat conductivity, how fast one substance moves through another, magnetic behavior, and how much light passes through.

Concrete has tiny air pockets inside it. If engineers can accurately model these spaces, they could use materials like fly ash, slag, or biochar to fill them in. This would reduce the amount of cement needed, helping to lower carbon dioxide emissions from cement production, while also making the concrete stronger and more affordable.

Additional applications include modeling fractured and porous media, a central challenge in groundwater management, as well as in nuclear waste disposal, geothermal energy, and carbon sequestration.

“These systems are complex and difficult to model,” said Tartakovsky. “However, the Poisson model’s multipoint functions that we solve in this study offer a new tool for understanding and predicting their behavior.”

In this way, as a microstructural model, the Poisson model can accurately simulate a wide range of heterogenous materials, including everything from the appearance and distribution of ice fragments on a frozen lake to the marbling in a juicy steak.

Shelley shared a neat way to understand the Poisson model. Imagine taking a blank piece of paper and randomly drawing lines across it to create different sections. Then, color each section however you want, it’s like creating a colorful mosaic! The new research takes this idea a step further by picturing another piece of paper laid over that mosaic.

If you poke a hole in the top sheet, you see one color below. That small peek gives a clue about the whole pattern. By making more holes and using a math method called multipoint correlations, you can predict the full design more accurately each time. This approach mirrors how scientists study heterogeneous materials, such as concrete or rock, by using small samples to understand the bigger picture.

“It’s like we’ve created the perfect Battleship player for guessing colors in this model,” Shelley said.

To handle the intricate math involved in the Poisson model’s multipoint correlations, Shelley took a hands-on approach. He began by sketching ideas in a notebook to help picture the problem. Figuring out two points was simple, but things got complicated fast, by the time he worked on three points, he had to deal with 128 different terms.

By the time he reached the four-point scenarios, the complexity was overwhelming, pushing him to turn to computer simulations. It was a necessary shift that saved him from spending months buried in calculations. on manual work.

According to Shelley, the seemingly painstaking work was anything but. “I love math, and I was a math double major in undergrad, so I had the knowledge to go in and try this problem out,” he said.

For more information: Physical Review Letters

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

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

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

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

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

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

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

For more information: ACS Applied Materials

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

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

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

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

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

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

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

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

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

For more information: Science Advances

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

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

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

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

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

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

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

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

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

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

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

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

For more information: University of Michigan

Image: A 3D map of the quantum potential.

AI-powered approach simplifies exploration of complex materials

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

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

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

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

For more information: Nature

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

Breakthrough phason discovery in twisted 2D materials transforms quantum computing

Scientists have directly imaged a rare atomic vibration known as a phason—a phenomenon long theorized but never observed—within a twisted two-dimensional material called tungsten diselenide. These ultra-thin materials, just a few atoms thick, can be stacked in unusual configurations that produce entirely new physical behaviors. Using a cutting-edge technique called electron ptychography, researchers captured the most precise images of individual atoms to date, revealing how phasons vary with atomic arrangement and opening new possibilities for future electronic technologies.

They are only a special case of a broader category of moiré phonons, the result of when two layers of 2D material are shifted slightly one from the other. That fold between the two creates a moiré superlattice—a larger periodic structure that gives rise to unique thermal and electronic behavior. Phasons are an extremely soft, low-frequency variety of these phonons.

Even though phonons and phasons are not seen, their effect is widespread. Heat is atomic vibration in itself, and an understanding of such patterns is required if one expects to have any control over heat transfer in electronics. Greater control could imply materials that cool faster or suck heat off sensitive parts.

You can’t just get rid of phasons; that’s the blessing and curse,” said Pinshane Huang, a professor of materials science and engineering at the University of Illinois at Urbana-Champaign and lead author of the paper. “They’ve always been hiding in plain sight, changing the properties of 2D moiré materials.”

Phasons had mystified scientists for decades. They were thought to belong in theoretical models only, but no one had concrete proof. Not until Huang teamed up with Yichao Zhang, a postdoctoral researcher of nanoscale heat conduction. Their task: to freeze the blur of atomic movement created by heat.

What our primary goal was, was to see heat when looking at an atom,” Huang explained. “What this does is get so fantastic spatial resolution that vibrations of the atoms have something to do with how fuzzy the atoms appear,” she said. “These are tiny movements, and we can basically look at one atom at a time and see its heat-related motion.”.

To do this, the team utilized electron ptychography, a new imaging method with a resolution of less than 15 picometers. That is about one-thousandth the width of an individual atom. It allowed the team to see the width, shape, and movement of atoms in twisted bilayer tungsten diselenide, or WSe₂.

“Back when I started out, we figured the highest resolution you could get was maybe a little bit less than an angstrom,” Huang said. “But once ptychography arrived on the scene, we were starting to see resolution numbers as low as 0.2 angstroms,” she continued. “That put us thinking, ‘Well, heat gets atoms jigged around about 0.05 angstroms,’ she said. “Now that we can actually see heat, it shows what a giant jump in resolution can do for what microscopes can do,” she continued.

Twisted structures are important because they alter the local atomic environment in strong and unexpected manners. When one layer is twisted against another in bilayer materials such as WSe₂, there is a mismatch in the grids of atoms. That mismatch creates new areas—some with atoms close together and others not so close.

When researchers mapped these helical regions, they found more vibrations in some areas. Vibrations were especially strong around solitons, where solitons are borders between different stacking modes. AA-stacked regions, in which atoms lie atop each other, also showed more intense vibrations.

By integrating electron ptychography with lattice dynamics and molecular dynamics simulations, the scientists made a robust conclusion. Phasons were discovered by them as the main culprit behind thermal vibration in low-angle twisted bilayers. This tells us that the moiré pattern is not just an illusion—it plays a pivotal role in determining how heat traverses through the material. These findings provide new doors to investigate and even control heat in 2D materials at the atomic level.

Phasons are tiny, but their potential effect could be huge. Controlling the skill of sensing and studying these vibrations could change the way electronics are designed in the future. “One potential application of this technique is creating materials that are better heat conductors,” said Zhang. “We can look at one atom and find a defect that keeps the material from cooling more efficiently,” he said.

This could result in better means of atomic-level thermal control,” he added. “To watch individual atoms and see how they react to heat will tell us some very important things,” he said.

Twisted 2D materials are already being researched for use in transistors, sensors, and quantum computing hardware. Adding heat behavior to that research means engineers can create entire devices at the atomic level. Such devices would be able to be made smaller, faster, and much more efficient than today’s electronics.

Perhaps most exciting is the manner in which this finding reconciles theory with direct observation. Phasons were previously hypothetical, known by equations and models alone. Now, they are observable in real time.

Using equipment like electron ptychography, even the slightest motion of the atoms can be watched and measured. Science no longer needs to speculate about what atoms do—it’s watching them in plain sight, one vibration at a time.

For more information: Science

Image: (left) atoms present in the 2D material. (right) photos of single atoms. (CREDIT: The Grainger College of Engineering)

Furukawa Electric named to CDP’s 2024 Supplier Engagement Leaderboard for climate action

Furukawa Electric Co., Ltd., Tokyo, Japan announced that it has been named to the 2024 Supplier Engagement Assessment (SEA) Leaderboard by CDP, an international environmental nonprofit organization. The recognition places the company among the highest-ranked corporations worldwide for its initiatives addressing climate change across the supply chain and for its transparent environmental disclosures.

The CDP supplier engagement assessment evaluates corporate actions that promote emissions reduction and collaboration throughout the value chain. This marks the fifth time Furukawa Electric has earned a place on the leaderboard, reflecting sustained progress in environmental management and reporting.

Aligned with its “Furukawa Electric Group Vision 2030,” the company has advanced its ESG-driven management strategy to enhance long-term corporate value. Under its Environmental Vision 2050—first established in 2021 and updated in March 2025—Furukawa Electric is pursuing initiatives that contribute to a carbon-free society. The company also received SBTi 1.5°C certification in July 2023 for its science-based emissions reduction targets.

Furukawa Electric stated that the recognition underscores its commitment to tracking and reducing greenhouse gas emissions throughout its supply chain. Future plans include expanding the use of renewable energy sources such as hydroelectric and solar power and continuing energy efficiency improvements across offices and manufacturing plants. The company reaffirmed its goal of achieving net-zero greenhouse gas emissions across its entire value chain by 2050.

Read further here

Johnson Matthey to open first hydrogen internal combustion engine testing facility in Gothenburg

Johnson Matthey, London, England announced plans to open its first hydrogen internal combustion engine (H₂ICE) testing facility at its existing site in Gothenburg, Sweden. The upgraded center will expand the company’s heavy-duty vehicle testing capabilities, reflecting growing industry and regulatory momentum toward cleaner mobility technologies. The facility is expected to become operational in autumn 2025.

Building on Johnson Matthey’s previous hydrogen combustion research, the new installation will allow full engine testing for the first time. It will assess catalyst performance within broader aftertreatment and control systems, supporting the development of efficient hydrogen-powered mobility solutions.

The project follows the successful completion of Project Brunel, a collaboration launched in 2021 with Cummins, PHINIA, and Zircotec, which delivered key advances in H₂ICE engine performance and durability.

The upgraded Gothenburg site will feature its own hydrogen supply and storage system rated at up to 500 bar, hydrogen flow meters and analyzers, control and safety systems, adapted exhaust measuring instruments, and enhanced fire and gas detection. It will support engines up to 600 kW (800 hp).

Tauseef Salma, chief technology officer of Johnson Matthey Clean Air, said the expansion underscores the company’s commitment to advancing hydrogen mobility as part of global decarbonization efforts. Salma noted that hydrogen engines offer a mature and complementary solution to battery electric vehicles in achieving emissions reduction goals across Europe and beyond.

Johnson Matthey also recently joined the Global Hydrogen Mobility Alliance as a founding member. The coalition, comprising more than 30 major companies including BMW, Toyota, Hyundai, Air Liquide, and Linde, aims to accelerate adoption of hydrogen technologies in Europe’s transport sector.

Read further here

Ilika and Cirtec advance miniaturized medical implants

Cirtec Medical, Brooklyn Park, Minnesota announced a collaborative approach with  Ilika, Southampton, United Kingdom, combining application-specific integrated circuit (ASIC) technology with solid-state battery (SSB) innovation to power the next generation of active implantable medical devices (AIMDs). The integration aims to enable compact, rechargeable, and efficient implants for applications such as neuromodulation and cardiac sensing.

The joint solution addresses long-standing design challenges in AIMDs, including limited energy storage capacity, wireless power transfer efficiency, and stable memory retention during low-power or pulsed operating cycles.

Ilika’s Stereax M300 solid-state battery delivers high energy density in a miniaturized footprint, reducing recharge times and extending implant life to minimize the need for replacement surgeries. Cirtec’s ASIC platform complements this by managing power regulation, memory integrity, and startup control, even under variable energy conditions.

By combining these technologies, Ilika and Cirtec offer a scalable and reliable energy architecture for medical device developers, supporting safer, longer-lasting, and more sustainable implant designs that enhance patient outcomes while accelerating product development for medtech innovators.

‍Read further here