Earlier heat waves this summer showed how prolonged high temperatures tightened Europe’s power market.

The head of a financial services firm said that rising temperatures are now “a source of risk and return in any investment you have."

The government is counting on a new generation of Italians who see nuclear power as a source of low-carbon electricity and a tool to help fight climate change.

There’s new benchmark measuring AI’s ability to perform mathematical cryptanalysis. Anthropic’s frontier model actually found new attacks.

The benchmark: “CryptanalysisBench: Can LLMs do Cryptanalysis?” The idea is to benchmark the ability of LLMs to discover new mathematical cryptanalytic attacks against a series of historical algorithms.

Abstract: Cryptanalysis—the task of finding attacks against cryptographic schemes—its at the intersection of mathematical reasoning and cybersecurity, two areas where LLMs have advanced fastest. Cryptanalysis represents both a clean testbed for frontier reasoning (as practical attacks can be automatically verified) and a domain with unusually high stakes, since the primitives under study underpin our digital security. In this paper we ask whether LLMs can do cryptanalysis, and find that the answer is increasingly yes. We introduce CryptanalysisBench, 191 tasks across six families of cryptographic primitives (block ciphers, hash functions, etc.) drawn primarily from four NIST standardization competitions. Our benchmark consists of three tiers: (i) primitives with known practical breaks; (ii) primitives with no known practical break, evaluated both at full strength and as scaled-down variants; and (iii) a challenge set of production primitives at the frontier of cryptanalysis. Five frontier models (Claude Opus 4.8, Sonnet 5, Mythos 5, GPT-5.5, and the open-weights GLM-5.2) break 65%­86% of Tier 1 schemes, 6­12 Tier-2 schemes at full strength, and 24­61 across all scaled-down variants. Beyond deriving known results, models produce novel cryptanalysis, such as a key-recovery attack that exploits a design flaw in the SpoC AEAD and an error in KINDI’s published CCA-security proof, both to the best of our knowledge not previously known...

The concept of “surveillance pricing” is just one part of a much larger problem and business model: corporations maximizing their profits by invading our privacy. The all-too-common business model is to systematically harvest, collate, and store as much of our personal data as possible, and then monetize it through use and sale. When it comes to surveillance pricing, that looks like corporations offering the same product to two different people at two different prices, based on harvested personal information. That's why EFF supports A.B. 2654, authored by Assemblymember Chris Ward, which bans this harmful practice. 

As an organization based in San Francisco, EFF was proud to learn that the San Francisco Board of Supervisors had also introduced a resolution to similarly support the legislation. However,  we were disappointed to learn the San Francisco Board of Supervisors has since stalled a vote on the resolution stating their own support for A.B. 2654 after receiving an email from the San Francisco Chamber of Commerce criticizing the bill using well-worn and debunked concerns. We’ve sent the Supervisors a letter asking them to reconsider.

Banning surveillance pricing would be good for consumers. The FTC has found that companies will set higher prices based on personal information. “For instance,” the FTC found last year, “if a consumer is profiled as a new parent, the consumer may intentionally be shown higher-priced baby thermometers on the first page of their in-app search results, based on their residential zip code and time of purchase.” Let's say that again: the U.S. government has found that companies may seek to use surveillance pricing to charge parents searching for a thermometer in the middle of the night more money in a time of need.

Privacy is a human right, not something that people should understand as a currency to give away or protect based on how it will impact the price of groceries. EFF has long opposed pay-for-privacy schemes, in which a company charges a higher price to a customer who refuses to submit to processing of their personal data. Surveillance pricing is another version of that practice. You should never have to worry that your privacy rights depend on how much you make.

At a time when prices for everyday goods continue to climb, some surveillance pricing defenders note that using personal information could lead to lower prices for some consumers. Yet some recent studies indicate there will be losers and winners  based on factors such as whether a consumer is willing or able to switch products. Who loses or wins also will turn on the accuracy of the underlying data – yet surveillance pricing is often based on false information.

That said, even if surveillance pricing has the capability to lead to lower prices (which it often doesn't) we oppose it as just another way that corporations try to make customers pay for their privacy.

The San Francisco Chamber of Commerce’s concerns are fully addressed in the text of A.B. 2654. The Chamber raises questions about how businesses will comply with the law. But the bill is quite clear: “a retailer shall not engage in surveillance pricing.” It also has a clear definition of what “surveillance pricing” is. The banned practice is defined as: “[i] a customized price for a good for a specific consumer or group of consumers, [ii] based, in whole or in part, on personally identifiable information collected through electronic surveillance,” including if that information is “acquired from a third party.” In other words, “surveillance pricing” is a customized price based on personal information.

The SF  Chamber’s letter also asks about the bill's “treatment of discounts and loyalty programs.” In this way, too, A.B. 2654 is quite clear. The bill includes three broad carveouts that ensure it doesn't disrupt loyalty programs and discounts:

• First, for price differences “based solely on costs associated with providing the good to different consumers.”
• Second, for a discount offered to a consumer who is taking steps to terminate a service.
• Third, for a discount, conspicuously posted on a retailer’s website, that is uniformly available based on (1) criteria anyone can meet, such as signing up for a mailing list, (2) membership in a broadly defined group, such as seniors, or (3) participation in a loyalty program.

An opt-in senior discount to the movies is not the problem. The systematic collection of all of our personal information to determine whether someone is a senior and if so whether they should pay more or less for that matinee is. 

As we said in our blog post outlining our support for this bill:

Surveillance pricing is very similar to online behavioral advertising, a business practice that EFF urges governments to ban. Both practices incentivize all businesses to collect as much of our personal data as possible, in order to later monetize it. Both practices lead some businesses to collate and store our data into dossiers about us for later use. Both practices use these surveillance-based dossiers to manipulate and limit our economic choices, by altering the advertisements and prices we see online.

We urge the San Francisco Board of Supervisors to join the coalition of groups that support A.B. 2564, and stand against companies mining our personal information to charge us different prices for the same thing. 

You can read our letter to the Supervisors here.

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.

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.”

Recent reports have raised alarm about the use of PatronScan, an ID-checking and face-scanning system, at multiple LGBTQ+ bars in San Francisco’s Castro neighborhood. Much of the attention has focused on reports that the system photographs patrons as they enter venues and questions about whether those images are used for facial recognition.

A broader privacy concern also deserves scrutiny. For years, PatronScan has marketed itself not just as an ID-verification tool, but as a system that allows bars and clubs to identify patrons, keep records about them, and share information across venues. As one news article published in 2019 documented, PatronScan built a network that allowed participating bars to flag patrons and share information about them with other establishments. 

And in California, it’s not at all clear how PatronScan’s business model of scanning IDs and sharing the information from those scans with other bars comports with the law. California’s ID privacy law, which was amended in 2018 to add ID “scans,” states that no businesses shall “retain or use” any information from a scanned ID card except for limited purposes such as to verify age, comply with a legal requirement, or prevent fraud. 

A venue cannot claim to be a safe space while feeding its patrons’ data to a third party database.

Californians should be deeply concerned about businesses that collect information from government-issued IDs and use it to build databases about where people go, whom they associate with, and whether they should be allowed into other public gathering places. That concern is especially strong in LGBTQ+ spaces, which have long served as refuges for people to go without being tracked, monitored, or put on lists. 

We reached out to Patronscan with questions regarding their practices and their views on California ID law. They referred us to their published FAQ question “Is Patronscan privacy compliant in California?” which claims that the use of Patronscan kiosks is legal in California. They also said “Patronscan does not do facial recognition in North America, or any kind of automated analysis of the ID or the live photo image.” 

The California Legislature Has Investigated PatronScan’s Business Model 

In 2018, the California Legislature published bill analyses that went into detail about PatronScan’s business. Reviewing PatronScan's own materials, the California Senate Judiciary Committee found that the company had collected and retained information on 561,087 customers in Sacramento alone during the first five months of 2018—a remarkable figure for a city whose population had only recently topped 500,000.

Lawmakers also found that at that time, PatronScan retained information for at least 90 days or longer in some cases, shared information among participating bars, and maintained bans that lasted an average of more than 19 years. A PatronScan “Public Safety Report” used 10,000 scans collected on a single day to report on “where customers live, how far they have traveled, and how many different venues the customers patronized.” 

This was not simply checking IDs at the door. PatronScan was building a database. 

An immigrants’ rights group, the Coalition for Human Immigrant Rights (CHIRLA), wrote about its concern at the time with these growing ID databases, saying that “placing individuals on a database that labels them a "threat to public safety" has “significant immigration consequences that could lead to deportation, revoking of current status, or denial of future immigration relief.” 

Today, Patronscan states that it retains personal information about all customers for 21 days, and about flagged customers for up to five years. This includes the customer’s name, date of birth, photograph, gender, and zip code. It also includes the dates and times that the customer entered particular bars. Such databases are a grave privacy threat. Personal data is routinely stolen by thieves, misused by a company’s employees, seized by government agencies, and diverted to new purposes by a company’s executives. 

California Law Still Bans ID-Scan Databases, And Bars Should Follow That Law

In 2018, California lawmakers closed what they viewed as a loophole. Existing law already prohibited businesses from retaining or using information obtained when they “swiped” a driver's license, except for the narrow purposes of legal requirements (like a judicial warrant) or “preventing fraud, abuse, or material misrepresentation.” 

After reviewing companies like PatronScan, the Legislature amended the law to make clear that the same restrictions that apply to businesses that “swipe” ID cards also apply when those IDs are “scanned.” PatronScan opposed that change, arguing it wanted to preserve the ability to share information among bars so participating venues could decide whether to admit patrons.

The bill became law anyway. Yet PatronScan continues to market and sell a system that apparently retains information from scanned IDs, and allows participating venues to flag patrons and share information across its network. 

At a minimum, that raises serious questions about how those practices fit with California's existing ID privacy law. Bar and nightlife venue owners who utilize PatronScan should think twice about its effects on their customers, and consider going back to standard, visual ID checks. These physical checks have been effective at keeping underage patrons out of 21-and-over venues for decades, and don’t present the serious privacy dangers of creating a private database of bar patrons. 

For venues serving vulnerable communities like immigrants or the LGBTQ+ community, the stakes of using this technology are even higher. It’s disappointing and alarming to see some of California’s more well-known LGBTQ+ nightlife spots instead lining up as PatronScan’s early adopters. A venue cannot claim to be a safe space while feeding its patrons’ data to a third party database. These businesses should reject PatronScan, return to the standard ID checks that every other bar has been able to utilize, and prove to their customers that their privacy and security still matters. 

Governments are switching, but I’m not sure it makes a difference:

…some municipalities, including Denver, Colorado, are ditching their Flock arrays. But keep in mind that if they’re only switching from Flock to another brand of license-plate readers, like Axon, it’s like a gambling addict trying to kick the habit by switching from FanDuel to DraftKings.

[…]

Despite what you may read on the Flock website, Axon cameras are pretty effective when it comes to hoovering up personal details that can go far beyond your license plate numbers. That means a municipality that opts for Axon cameras instead of Flock units won’t necessarily reduce the amount privacy its citizens lose through their use...

The fossil fuel industry has argued that federal law bars the lawsuit. But local Colorado governments say it’s an appropriate exercise of state power.

Food and housing insecurity, chronic illness, and mental health challenges persist among many families.

The governor’s support signals momentum behind rethinking how the state pays for the cost of natural disasters.

As well as pushing up temperatures, the building El Niño is expected to worsen drought and strain food production in some parts of the world.

The phenomenon, called a pyrocumulonimbus, is so rare in Europe that France’s firefighters federation said it had never before recorded one in the country.

Indonesia faces a dilemma as it attempts a large-scale waste-management reform while also protecting the livelihoods of around 600,000 waste pickers.

Whales are facing a new threat around South Africa: the conflict in the Middle East.

Nature Climate Change, Published online: 28 July 2026; doi:10.1038/s41558-026-02722-w

Traditional resilience planning for extreme weather focuses on reinforcing municipal boundaries and internal infrastructure. Now, a study demonstrates how a city’s socioeconomic and physical ties to external urban networks act as a critical catalyst for intra-city mobility resilience under extreme rainfall.

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.

EFF’s first EFFecting Change livestream was all the way back in July of 2024. Maybe you've caught each stream, or maybe you’ve only caught a few. Or maybe you’re like me and prefer to listen to conversations like these on your daily commute! Either way, if you want to stay on top of these monthly conversations, you can now subscribe to our new podcast feed for EFFecting Change—starting with our conversation on the Right to Repair movement with Adam Savage and iFixit CEO Kyle Wiens:

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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.