Why some nitrogen-processing enzymes are more efficient than others

Nitrogen gas is abundant in Earth’s atmosphere, but most living organisms can’t readily use this nitrogen. Only a subset of microbes that have enzymes known as nitrogenases can break nitrogen gas apart and convert it into ammonia.

There are three different classes of nitrogenases found in nitrogen-fixing microbes, which vary based on the types of metal that they contain. Nitrogenases that contain the metal molybdenum are the most efficient, and two new studies from MIT offer an explanation for why that is.

The findings could help guide the design of engineered enzymes or synthetic catalysts that can convert nitrogen gas to ammonia, the researchers say.

The team found that while molybdenum doesn’t directly bind to nitrogen, it helps nearby iron atoms bind to nitrogen more strongly. This is a critical first step in breaking the bond between the two nitrogen atoms that form nitrogen gas.

“It’s that initial binding step that’s really the hard part. Once you’ve started to break the nitrogen-nitrogen triple bond and make some new nitrogen-hydrogen bonds, it’s pretty easy to get the rest of the way,” says Daniel Suess, the Arthur Amos Noyes Associate Professor of Chemistry at MIT and a senior author of both papers.

MIT postdoc Tong Wu and former postdoc Madeleine Ehweiner are the lead authors of one of the papers, and Alexandra Brown PhD ’23 is the lead author of the other. Kyle Lancaster, a professor of chemistry at Cornell University, is a senior author of the latter paper, along with Suess. Both papers appear today in the journal Chem.

Efficient enzymes

Before microbes evolved the ability to fix nitrogen around 3 billion years ago, the strong triple bond between atoms of N₂ could only be split with high-energy events such as a lightning strike.

“Once an enzyme came along that could convert dinitrogen to ammonia, that changed the game because now cells could harvest nitrogen from the air for biomass,” Suess says.

Within the active site of nitrogenase is a catalytic cofactor that typically consists of a cluster of iron, sulfur, carbon, and in some cases another metal. Nitrogenases whose cofactors contain molybdenum are the most efficient, followed by those containing the metal vanadium. Nitrogenases that don’t have any metal other than iron are the least efficient.

Why the molybdenum-containing enzyme is more efficient has been a puzzle, especially because it’s thought that molybdenum itself doesn’t bind directly to nitrogen gas.

“In all cases, iron is thought to interact with N₂, so it’s a bit of a mystery,” Suess says. “If all the chemistry is happening at iron, why is it that this molybdenum is affecting catalysis?”

To answer that question, Suess’s lab has developed simpler versions of iron-sulfur clusters that they can use to model the naturally occurring cofactors. These can be modified by adding different metal atoms, allowing the researchers to study how those metals change the cofactors’ properties. 

In the first paper, led by Wu and Ehweiner, the researchers swapped in different metal atoms and then measured the ability of the iron in the cofactor to bind to nitrogen. They found that only cofactors with a large metal atom, such as molybdenum or tungsten, were able to strongly bind N₂. With vanadium,  chromium, or iron, which are smaller, the cofactors did not bind N₂ and performed other reactions instead.

“That paper essentially recapitulates what you see in biology, which is that the iron-sulfur clusters that have molybdenum in them seem to be better at binding dinitrogen than those with lighter metals,” Suess says.

Sharing electrons

In the second paper, led by Brown, the researchers uncovered a possible mechanism that explains that phenomenon. 

In that paper, the researchers studied how cofactors containing different metals interact with compounds called N-heterocyclic carbenes. These molecules behave similarly to N₂ in some ways, making them a good model for this type of study. Like N₂, they are resistant to accepting any electrons from another molecule, which is an essential step to breaking chemical bonds. 

The researchers found that when molybdenum was included in the cluster, it became easier for iron to donate some of its electrons to the N-heterocyclic carbenes, in a process known as back-bonding. This occurs because molybdenum, a large atom, has large orbitals that can overlap with the orbitals of the nearby iron atom. That alters iron’s electron density in ways that make it easier for iron to pass electrons to N₂.

“Without these direct metal-metal interactions, the iron has to do all the work, but adding the molybdenum allows for this electronic cooperativity,” Suess says.

Once N₂ is bound to an iron atom, the rest of the reaction can proceed. A proton can come in from water or another source to create an N-H bond, which then makes it much easier for the remaining N-N bonds to be broken and bind to protons, forming NH₃. 

The findings could help guide scientists who are working on designing enzymes that could be engineered into organisms that help them generate their own NH₃, eliminating or reducing the need for fertilizer. The results could also help chemists to design synthetic catalysts that could produce ammonia industrially, using less energy than the Haber-Bosch process. 

“The general principle is that you can make an iron site in any context behave differently when you have these metal-metal interactions than when you don’t have these interactions,” Suess says. “The primary result of these findings is to teach us about the natural world and how nature accomplishes this really important and miraculous reaction. And, maybe that can be translated into new processes.”

The research was funded primarily by the U.S. Department of Energy, the National Science Foundation, and the National Institute of General Medical Sciences.

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MIT and Broad Institute researchers break diffraction barrier in super-resolution microscopy

Researchers in the lab of Sam Peng, the Pfizer Inc. - Gerald Laubach Career Development Assistant Professor of Chemistry at MIT and a core institute member of the Broad Institute of MIT and Harvard, have developed a groundbreaking super-resolution imaging technology that allows scientists to visualize molecular structures with sub-angstrom-level localization precision — three orders of magnitude beyond the nanometer limits of standard fluorescent dyes — while drastically simplifying the imaging process. 

Unlike traditional dyes that fade rapidly under illumination and limit data collection, the platform, called U-STORM (Upconversion enabled Stochastic Optical Reconstruction Microscopy) utilizes a new class of compositionally engineered upconverting nanoparticles (UCNPs) that blink spontaneously and indefinitely. 

This work represents a fundamental shift in both optical materials and biological imaging. An open-access description of the study was published July 27 in Nature Nanotechnology.

Overturning a decades-old paradigm

For decades, the scientific community widely considered upconverting nanoparticles to be completely photostable and non-blinking. Because localization-based super-resolution microscopy techniques like STORM rely entirely on the stochastic “blinking” (switching between “on” and “off” states) of light emitters to distinguish closely packed molecules, UCNPs were historically deemed unsuitable for this type of imaging.

“Our laboratory has long been interested in overcoming these limitations,” says Peng. “Our work began with a question: Can we develop a super-resolution imaging platform that is simultaneously long-term, multicolor, simple to operate, and capable of achieving extremely high localization precision without using imaging buffers or additional optical control?”

By meticulously controlling nanoparticle composition, the MIT and Broad Institute team discovered that these small (~10nm) core-shell particles could actually be coaxed into spontaneous blinking under continuous near-infrared excitation. Remarkably, this blinking behavior continues indefinitely without the need for complex imaging buffers, oxygen scavengers, or external optical modulation.

U-STORM’s key breakthroughs

An angstrom is a tiny unit of measurement used by chemists to measure size and distances at the atomic level. U-STORM’s ability to blink indefinitely has afforded researchers the opportunity to collect over 88,000 localization events from the same particle, sharpening the localization precision down to an unprecedented 0.6 Å.

Unlike conventional multicolor super-resolution imaging, which requires multiple expensive lasers and meticulous optical alignment, U-STORM can operate with just one near-infared laser, which works to simultaneously excite nanoparticles emitting different colors. This results in a drastic reduction of an experiment’s complexity.

To obtain images with multiple colors, rather than capturing images sequentially over multiple rounds, U-STORM captures multiple colors simultaneously. Researchers have successfully demonstrated this by mapping epidermal growth factor receptor dimers and multimers in biological samples under physiological conditions without any specialized imaging buffers.

Broader impact

Beyond expanding the boundaries of microscopy, this research establishes an entirely new design principle for lanthanide nanomaterials. The team is already working to expand the color palette, make the particles even smaller and brighter, and deploy U-STORM to investigate complex nanoscale protein organizations and cellular signaling pathways.

Ultimately, U-STORM promises to provide laboratories worldwide with an accessible, easy-to-implement, yet incredibly powerful route toward high-precision molecular imaging.

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How a medical database developed at MIT evolved into a global standard of data-sharing

Before the advancement of scientific data storage and collaboration via the cloud, medical investigators seeking health research breakthroughs had to overcome significant obstacles to collaboration and key clinical data gathering. 

Data were siloed and difficult to distribute, so those looking to undertake research had no option but to gather them themselves. This not only made research more expensive, but it was challenging to compare findings across datasets. 

In 1975, researchers studying arrhythmias at MIT and Boston’s Beth Israel Hospital envisioned another way: the team began collecting and digitizing electrocardiogram recordings with the intention of not only studying them, but of also making them available to the wider research community.

The team built their own computers for the process, painstakingly duplicated tapes one by one, and created more than 100,000 annotations for the recordings. The process took years, but by summer 1980, the tapes were finally ready. The team initially thought their tool would reach fewer than a dozen academic and industry groups. But interest kept pouring in. Over the next decade, they went on to mail about 100 copies. 

The data eventually became the first database of the global platform PhysioNet — founded in 1999 at the Harvard-MIT program in Health Sciences and Technology — as a clinical data repository for complex physiological signals. 

At the time, that type of data-sharing, which may seem like the default today, was a near-revolutionary idea. PhysioNet’s “founding was incredibly visionary,” says Thomas Heldt, Richard J. Cohen (1976) Professor in Medicine and Biomedical Physics, associate director of MIT’s Institute for Medical Engineering and Science, and the senior author of a recent paper in Nature Health examining the platform’s impact. 

Eventually, those magnetic tapes sent through the mail became burned CD-ROMs, which then evolved into FTP servers hosted on the newly minted internet. Today, as PhysioNet looks back at over 25 years of operation, the platform hosts hundreds of databases, and has become one of the most comprehensive biomedical and clinical data repositories in existence. Last year, more than 15,000 scientific publications cited PhysioNet, and users from more than 180 countries have registered on the platform. It is widely used by researchers, manufacturers, and clinical decision-makers.

“The research impact is truly significant,” says Heldt, who is also a professor in the MIT Department of Electrical Engineering and Computer Science and a principal investigator at the Research Laboratory of Electronics, “and quite humbling.” 

“It is really beautiful to see that such a vision has proven right and so enabling for so many people.”

Setting a standard 

Around 2009, a PhD student named Tom Pollard was conducting research on critically ill patients at one of London’s leading hospital systems. Although the hospital generated large volumes of valuable clinical data, the infrastructure and processes needed to curate and support their wider research use were still developing. 

“Hospital data were collected primarily to support immediate patient care, with less attention given to how they might be curated and reused for research,” says Pollard, now a research scientist at MIT’s Laboratory for Computational Physiology (LCP), technical director of PhysioNet, and the lead author on the Nature Health paper. 

The problem was not simply privacy. Hospital information systems were built primarily to support patient care and administration, not research. Data were fragmented across systems and rarely curated with future reuse in mind, making it difficult and expensive to turn them into coherent research resources.

But Pollard needed data to complete his dissertation. After poking around on the internet, he eventually discovered the Medical Information Mart for Intensive Care (MIMIC), a database of de-identified electronic health records hosted by PhysioNet. Recognizing its potential, his clinical supervisor, Kevin Fong, organized a visit to Boston. Soon afterward, Fong and Pollard were sitting across the table from Roger Mark, discussing how their teams might collaborate.

Academic incentives have long favored publications and exclusive analyses over the less-visible work involved in preparing data for others to use. That tension persists today. PhysioNet’s founders embraced a different model, believing that sharing research resources could accelerate discovery and ultimately improve human health, he says. MIMIC became central to Pollard’s dissertation, and after completing his PhD, he came to MIT to help build the next generation of the database. 

In the years since PhysioNet was established, the value of sharing research data has gained much wider recognition. The late Roger Mark, MIT’s distinguished professor of health sciences and technology emeritus and one of PhysioNet’s founders, described its purpose as building an “accessible multinational community around data” to “positively impact global health.”

Earlier this year, Mark and the late George Moody, PhysioNet’s co-founder, jointly received the prestigious IEEE Biomedical Engineering Award for their contributions to PhysioNet and biomedical signal processing. IEEE cited their “leadership in ECG signal processing and global dissemination of curated biomedical and clinical databases, thereby accelerating biomedical research worldwide.”

The source code for the platform, like much of its data, is public. According to the Nature piece: “As the platform evolved, PhysioNet’s community broadened substantially beyond its origins in signal processing and cardiovascular health to encompass clinical informatics, critical care and machine learning for health.” People have used that to build their own PhysioNet-esque infrastructure, says Heldt. Pollard points to similar platforms like Health Data Nexus as examples of PhysioNet’s legacy. 

Although there are now more resources out there hosting similar electronic health data, according to Google DeepMind researcher Vivek Natarajan, both PhysioNet and MIMIC “set the standard,” he says, “and it’s still the standard right now.”

That standard, according to those who use the platform, changed how research is conducted. Access to data should not be the determinant for which ideas are possible, according to Ziad Obermeyer, an associate professor at the University of California at Berkeley School of Public Health and the College of Computing, Data Science, and Society. 

“PhysioNet changed how I think about the bottleneck in research. It is often not ideas or talent. It is friction. When access to data is slow, expensive, and hard, the ideas that die first are the high-risk ones, the things that probably will not work, but would be transformative if they did. That is exactly the wrong model if you want real progress,” he says. “PhysioNet lowers the fixed cost of trying ambitious ideas, and that changes what science becomes possible.”

The AI boom

PhysioNet, once a repository mainly for those working in biomedical signal processing and the health-care fields, has evolved in its 25 years. Originally, the holdings consisted solely of cardiovascular ECG data. Now PhysioNet is a largely a source for electronic health records, imaging data, and software and AI models.  

Particularly as artificial intelligence approaches took off, “the community shifted,” Heldt explains. Those in need of signal processing data still use PhysioNet databases, but the pool of users has expanded to encompass staff at large tech companies, teachers, and practitioners in all areas of medicine, as well as researchers in health-related machine learning and AI. Today, that latter group “dominates the user community,” says Heldt. 

The platform hosts the highest-quality datasets available for health-care AI research, according to Natarajan, whose research involves AI, science, and medicine and who has published several papers that used its datasets. 

“It has been an important cornerstone that has catalyzed all the progress in health-care AI over the last decade,” says Natarajan. In addition to using PhysioNet data, he and his colleagues have contributed data to the platform, helping create the self-sustaining ecosystem that typifies PhysioNet. 

Looking toward the coming decades, stewards of the platform like Heldt and Pollard envision continuing to expand its reach with an annual conference. The team is also preparing to pilot a new system that will allow users to annotate data and contribute their own expertise, enriching PhysioNet’s resources for the next phase of the platform.

“The kind of research that people want to do now needs to be interdisciplinary. Statisticians, computer scientists, clinicians, pharmacists, and nurses must all come together and contribute their knowledge to develop algorithms that are useful for people” says Pollard. “The community has broadened, and advances in AI have expanded both the questions researchers can address and what they believe is possible.” 

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Professor Emeritus Robert Cohen, pioneering polymers researcher and devoted mentor, dies at 79

Robert E. Cohen, the Raymond A. (1921) and Helen E. St. Laurent Professor of Chemical Engineering, Emeritus, whose pioneering research helped shape the fields of polymers and soft matter while inspiring generations of students, passed away peacefully on July 9 following a long battle with Parkinson's disease. He was 79.

"Bob Cohen was an innovator in every sense of the word: in his research, his approach to mentorship, and in every aspect of our community at MIT," says Kristala Prather '94, the Arthur D. Little Professor and head of the Department of Chemical Engineering (ChemE). "Bob combined extraordinary intellect with remarkable humility. As a teacher, colleague, advisor, and friend, he had a gift for making people feel respected, valued, and heard. That generosity shaped every part of his work and inspired everyone fortunate enough to know him."

During more than four decades at MIT, Cohen continually reimagined how chemical engineering students should be educated. Recruited for his expertise in polymer science, he brought to MIT the polymer laboratory course he had developed during his postdoctoral work at the University of Oxford, establishing class 10.467 (Polymer Science Laboratory). The rigorous undergraduate course introduced generations of students to polymer synthesis, physical chemistry, and the evaluation of mechanical properties through hands-on experimentation.

In 1986, Cohen founded the Program in Polymer Science and Technology, now known as the Program in Polymers and Soft Matter (PPSM). Recognizing that advances in polymer science require expertise spanning chemistry, physics, engineering, and materials science, he created one of MIT's first truly interdisciplinary graduate programs. PPSM continues to prepare doctoral students to tackle complex challenges across the broad field of polymers and soft materials.

"Bob Cohen is the reason I returned to MIT as a graduate student," says Paula Hammond '84, PhD '93, Institute professor, dean of the School of Engineering, and a PPSM alumna. "His vision for multidisciplinary polymer education was unlike anything I had experienced. I benefited from him as a teacher in the classroom, as a member of my thesis committee, and a life-long mentor. As a department head, I saw firsthand the extraordinary impact he had on generations of students and on the field itself."

Cohen also conceived the unique PhD in chemical engineering practice (PhDCEP) degree, recognizing that future leaders in chemical engineering would benefit from combining advanced research with industrial experience and business education. The first and only program if its kind, the PhDCEP program integrates coursework, MIT's renowned David H. Koch School of Chemical Engineering Practice, doctoral research, and study at the MIT Sloan School of Management. 

Cohen also founded and directed the DuPont/MIT Alliance from 2000 to 2012, creating a highly successful partnership that brought together researchers from MIT and DuPont to develop innovative materials and manufacturing technologies. The collaboration advanced research in bioelectronics, biomimetic materials, alternative energy, and metabolic engineering, while fostering lasting collaborations across disciplines.

Cohen was a prolific collaborator whose pioneering research established him as one of the world's leading chemical engineers. His contributions include omniphobic surfaces, block copolymer nanoreactors for inorganic cluster synthesis, tough-stiff nanocomposites, chain folding in confined geometries, and layer-by-layer assemblies at the biotic-abiotic interface. Yet when asked about his proudest accomplishments, he rarely pointed to his scientific discoveries. Instead, he spoke about his students, and took immense pride in watching many former PhD students go on to become faculty members and leaders at top institutions around the world.

Raised in Oil City, Pennsylvania, Cohen developed an early appreciation for chemical engineering. After earning his master's and doctoral degrees from Caltech and completing a postdoctoral fellowship at the University of Oxford, he joined the MIT faculty in 1973. Over the next four decades, he became internationally recognized as a groundbreaking researcher, educator, entrepreneur, and mentor. 

Cohen was a member of the National Academy of Engineering and the American Academy of Arts and Sciences, as well as a fellow of the American Institute of Chemical Engineers, the Polymer Division of the American Chemical Society, the American Physical Society and the Materials Research Society. Cohen co-founded MatTek Corp., helping translate advances in biomaterials into practical applications.

Although Cohen received many prestigious honors throughout his career, he often said the award that meant the most to him was the inaugural Paul J. Flory Polymer Education Award, presented by the American Chemical Society in 2012. The honor recognized his leadership in building the interdisciplinary PPSM program and transforming undergraduate polymer education. Fittingly, it celebrated what he valued most: helping students discover their potential.

Cohen is survived by his beloved wife, Jane; his son, Eliot Cohen, his wife Jacqueline Aldred Cohen, and their children Ada, Brennan, and Callan; his daughter, Genevieve Cohen, and her daughter Emma; his sister, Nancy Stein, and her husband Herb; sisters-in-law Lee Woodman and Betsy Woodman; brother-in-law Wally Coleman; and many beloved nieces and nephews.

A memorial service is scheduled for Oct. 17 at the MIT Chapel. In lieu of flowers, donations may be made in Cohen’s memory to the Michael J. Fox Foundation.

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Yu Deng ’11 and Hong Wang PhD ’19 awarded Fields Medal

MIT alumni Yu Deng ’11 and Hong Wang PhD ’19 were among the four young mathematicians awarded Fields Medals on July 23 at the 2026 International Congress of Mathematicians (ICM). The other two honorees were John Pardon and Jacob Tsimerman.

The Fields Medal is awarded once every four years at the ICM, and is regarded as one of the highest honors a mathematician can receive. 

Yu Deng received his BS in mathematics at MIT in 2011, and was a Putnam Fellow in 2010. He earned his Fields Medal for his work in partial differential equations (PDE), including the rigorous derivation of the Boltzmann equation from hard-sphere dynamics for rarefied gases, the derivation of wave kinetic equations from nonlinear dispersive systems, and probabilistic approaches to nonlinear Schrödinger dynamics. His first published paper (in Analysis & PDE) was on the latter topic, and stemmed from summer research conducted at MIT on a problem suggested by Gigliola Staffilani. Deng is currently a professor at the University of Chicago.

Hong Wang received her PhD at MIT in 2019 under the supervision of Larry Guth PhD ’05. She is awarded the Fields Medal for her work in harmonic analysis and geometric measure theory, including applications of multiscale and decoupling techniques to the local smoothing conjecture for the planar wave equation, and other major advances such as the solution of the Kakeya problem in three dimensions (with Joshua Zahl). As a student in the department, she was a graduate mentor in the Summer Program in Undergraduate Research (SPUR) and, alongside her mentee, was awarded the Hartley Rogers Jr. SPUR Prize, presented to the best student-mentor team. Wang, a Silver Professor of Mathematics at New York University and a professor at the Institut des Hautes Études Scientifiques in Paris, is the third woman ever to win a Fields Medal.

“The achievements of Yu Deng and Hong Wang are truly monumental, and we are all elated that they were awarded Fields Medals,” department head and RSA Professor of Mathematics Michel Goemans says. “Their success is a testimony of the amazing mathematical talent we have at all levels at MIT, and the top-quality education, mentorship, and research opportunities we provide to both our large pool of math majors and our PhD students, during their lifelong mathematical journey.” 

Goemans adds, “MIT is a unique and exciting place to learn mathematics, and I am sure we have more future Fields medalists among our students and junior members of the department.”

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Making robots faster by helping them think ahead

A new method developed by MIT researchers makes robots better at thinking ahead while they are acting, leading to smoother motions and quicker reactions.

This technique enables the artificial intelligence model that plans a robot’s motion to forecast its future position. The model uses this prediction to seamlessly transition current movements into the next actions.

Many existing methods cause a robot to stop and think about what it needs to do next, leading to slow and jerky motions. By basing its calculations on the future state of the robot, rather than its current position, the MIT method helps robots operate much faster.

Importantly, the technique does not add any computational overhead to the planning process and can be applied to varied robotic hardware.

This new method doubled the speed of robots performing activities like pick-and-place tasks, while significantly reducing lag time between motions. It also boosted the performance of robotic arms in highly dynamic activities, such as playing table tennis and Whack-a-Mole.

The system could be especially useful for robots that perform fast and agile maneuvers in challenging real-world environments, like emergency response or search-and-rescue. It could also allow robots to react more quickly when recovering from mistakes.

“This work sets up a good foundation for efficient, fast, accelerated, and low-cost robotics applications. We look forward to expanding our work into the latest world action models, so it has even stronger capabilities as we keep pushing to make physical AI faster,” says Song Han, an associate professor in the MIT Department of Electrical Engineering and Computer Science (EECS), member of the Research Laboratory of Electronics, and lead author of a paper on this method.

Han is joined on the paper by co-lead authors Jiaming Tang, an MIT EECS graduate student, and Yufei Sun, a student at Tsinghua University; as well as others at Nvidia, the University of California at Berkeley, the University of California at San Diego, and Caltech. The research will be presented at the Intelligent Robots and Systems Conference. 

Forecasting the future 

In state-of-the-art robotics applications, generative AI systems called vision-language-action (VLA) models act as the brain of a robot, planning its next moves and executing those actions. 

A VLA model takes environmental observations from the robot’s camera and instructions about its task, outputs the next few motions as one chunk of actions, then executes those actions on the robotic hardware.

But VLA inference — the real-time procedure during which the model processes visual inputs, reasons about the task, and outputs actions — is computationally demanding, so the robot can experience substantial pauses while planning its next actions. These pauses disrupt the fluidity of its motions and make it slower to react to changes in the environment. 

“Our motivation was to overlap the thinking process with the execution process to make the reaction speed faster,” Tang says.

The MIT researchers developed a new system called VLASH that enables a VLA to predict the future state of the robot and its environment. It uses this information to plan the next set of motions while the robot is completing the current action chunk.

This solves a major hurdle faced by many other methods, which use the current state of the robot to predict its next moves.

“Since the environment will change after the robot moves, if we plan based on stale observations of the current environment, there will be a misalignment that causes very unstable control,” Tang explains.

VLASH avoids this misalignment due to a key insight by the researchers. Although the model doesn’t know exactly what the environment will look like in the future, it does know the robot’s current position and how it will move to perform the actions it is about to take. 

The framework uses this information to predict the state of the robot after it completes its current chunk of actions. It uses that estimation to plan the next motions.

“In this way, we give the robot awareness of its future state,” Tang says.

Augmenting acceleration

On its own, this technique speeds up the robot’s motions by eliminating lag time that usually occurs between action chunks, accelerating reaction speeds more than 30-fold. 

But to make their approach even faster, the MIT researchers generate coarser chunks of actions, so the robot executes a few larger steps that follow the same trajectory. This technique is called action quantization.

While action quantization led to a slight dip in accuracy, it enables a robot to complete the overall task two to three times faster.

However, the researchers found that simply feeding future robot states to the VLA during deployment is not enough to enable accurate and stable control of the robot. 

They developed a training-augmentation method that groups training data in such a way that the VLA learns to use future state information instead of current observations. 

By reusing some training data, this fine-tuning method accelerated training fivefold with no additional computational overhead. 

“Even though there is a very large model working in the background, VLASH lets the robot react and execute its actions very fast, much more like a human would. This could help to make robots for all sorts of dynamic tasks more effective,” Tang says.

When compared with baseline methods in simulation, VLASH consistently performed faster while maintaining the accuracy of robotic maneuvers. The system also outpaced these methods on real hardware in pick-and-place, stacking, and sorting tasks.

For instance, VLASH placed cubes in a box while sorting them by color twice as fast as these methods, while achieving the same 90 percent accuracy as the best baseline. The system can also perform highly dynamic tasks like playing ping-pong and whack-a-mole.

In the future, the researchers want to combine VLASH with more powerful generative AI systems called world models that can predict the robot’s actual environmental observations, in an effort to boost performance and open new applications.

This work is supported, in part, by the MIT-IBM Computing Research Lab, Amazon, the National Science Foundation, and Nvidia.

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MIT engineers design recyclable elastic yarn

After a closet cleanout, what options are there for recyling our old threads? Not many. Apart from bringing used clothes to a donation center, there is no process for recycling textiles like there is for bottles and cans. And, the average American throws out around 81 pounds of clothing each year. That amounts to more than 11 million tons of textiles that end up in the landfill or incinerator. 

But MIT engineers hope to cut down on the growing mountain of textile waste, with a new, recyclable yarn. 

The team has designed a yarn made from a form of plastic that is commonly used in milk bottles and grocery bags. The new yarn, which has a feel similar to traditional sewing thread, can be woven into stretchy, lightweight clothing. The researchers say that at the end of its use, a yarn-spun garment could be melted down and redrawn into new yarn, and then woven into new clothing or even cast into buttons, belt buckles, and other plastic accessories.

To demonstrate the yarn’s recyclability, the researchers spun a spool of yarn, melted the yarn down, and respun it into new yarn, multiple times. They found that even after 10 cycles, the yarn was as strong and flexible as conventional thread. 

They envision the new yarn could be an alternative to elastic spandex-polyester or spandex-nylon yarns, which are spun from a combination of fibers that cannot be recycled together. The team’s new yarn, in contrast, is made from a specific combination of plastic materials that mimics the tough and stretchy properties of spandex yarns, while also being easily recycled. 

“Eighty percent of textiles on the U.S. market currently contain some amount of spandex, which makes them nonrecyclable,” says Svetlana Boriskina, a research scientist in MIT’s Department of Mechanical Engineering. “There’s no widely adopted technology now that recycles textiles into textiles. With our new yarn, we hope to change that.”

Boriskina and her colleagues have published the details of the new yarn in a study published in the journal ACS Materials Letters. MIT co-authors include first author SeongHyeon Kim, Duo Xu, Volodymyr Korolovych, Domingo Flores-Hernandez, Kaniz Moriam, and Daniel Braconnier.

The core of the problem

Spandex is a polyurethane-based synthetic fiber that is springy but not very strong. A thread of an elastic yarn is made from two parts: a spandex-based core, surrounded by a sheath of tough polyester or nylon. The combination of these materials gives elastic yarns their unique stretch and strength. 

But this same material mixture makes elastic yarns nearly impossible to recycle. Yarns would first have to be chemically treated to separate the polyester sheath from the spandex core. The polyester-based sheath material could then be melted down and reused. But there is no way to recycle the yarn as a whole, without chemical separation.

“Even though chemical separation technologies exist, they add extra cost and complexity, and usually require toxic chemicals that are harmful to the environment,” Boriskina says. “That’s why most stretchy garments go to the dump.”

In 2021, Boriskina’s group developed a new type of yarn made from polyethylene. Polyethylene is the most common type of plastic in the world, used to make everything from grocery bags, water bottles, trash bins, and toys to industrial pipes and plastic sheeting. Polyethylene is a thermoplastic, meaning that it can be melted down and remade, and thus recycled. 

And yet, polyethylene had never really been considered as a textile. In their previous work, Boriskina and her colleagues showed they could spin yarn out of polyethylene, which they then wove into various garments. In those experiments, they focused on the yarn’s moisture wicking, stain-resisting, and cooling properties. 

Spaghetti yarn

In their new study, the group aimed to tailor polyethylene yarn to mimic the strength and flexibility of spandex; they also sought to demonstrate the yarn’s recyclability. 

They first looked for formulations of stretchy, polyethylene-based copolymers that resemble a spandex elastic core. Separately, they engineered polyethylene yarns that can act as the sturdier sheath. Looking through the scientific literature and combing through industrial reports, the team evaluated many chemical variations of polyethylene.

“The chemical structure of polyethylene is like Christmas garland — a backbone of carbon, carbon, carbon, and also these dangling ‘decorations’ of hydrogen atoms or short branches with the same structure as a backbone,” Boriskina explains. “How these chains are arranged can change the properties of the whole structure.”

“Polyethylene can give us a wide range of properties, depending on how you make it,” adds first author SeongHyeon Kim.

For the yarn’s core, the team used one polyethylene-based resin that results in a more stretchy fiber. They chose a second, stiffer resin as the basis for the yarn’s sheath. The researchers obtained pellets of each resin from a chemical manufacturer, and then put each type of pellet through a process of fiber fabrication, first pouring them into a hopper, then heating the pellets to about 350 degrees Fahrenheit, past their melting temperature. The melted polyethylene was then drawn through small extruders to make hair-thin fibers. 

“You just melt it in a barrel with a heater, and then you extrude and spin it into fibers,” Kim says. “It’s like a spaghetti machine.”

The team used an industrial yarn spinner to wind the sheath fibers around a core fiber to make the final, elastic yarn. 

Because both the yarn’s core and sheath come from the same chemical family of polyethylene, Boriskina says the materials do not have to be separated before recycling, in contrast to spandex-based elastic yarns. The new yarn can be melted as is, and reformed into new yarn or other plastic products.

“Because they are exactly the same chemistry, they play nicely together,” she says. “That’s what makes this yarn very recyclable.”

As a demonstration, the team twisted an elastic core-sheath yarn, then melted it down and re-spun it, 10 times. Each time, they tested the yarn’s mechanical properties by precisely stretching a thread and measuring the pulling force at which the thread eventually broke. From these tests, they found that the yarn’s recycled versions were just as strong as the original sheath yarn. These recycled yarns can now be used to make new stetchy yarns by twisting them around a newly spun elastic core.

“Now we have something that can be knitted and woven,” Boriskina says. “That is the next stage.”

The team says their new recipe for polyethylene yarn can be scaled up into industrial-sized spools. Just like conventional spandex fibers, it would take kilometers of yarn to weave a single textile. But once woven and used, the team envisions that a polyethylene garment could conceivably be dropped in a recycling bin and sent to a facility to be melted down and respun, enabling a more sustainable, circular fashion and textile economy. 

“Hopefully it will prevent the need for making more and more textile materials, because you can keep recycling a large portion of it,” Boriskina says. 

This work was supported in part by the DEVCOM Soldier Center through the U.S. Army Research Office, the Office of Naval Research Global via Tecnologico de Monterrey, and the MIT Portugal Program.

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