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Durable plastic pollution easily, cleanly degrades with new catalyst

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Durable plastic pollution easily, cleanly degrades with new catalyst


Many people are familiar with the haunting images of wildlife — including sea turtles, dolphins and seals — tangled in abandoned fishing nets.

The main issue behind Nylon-6, the plastic inside these nets, carpet and clothing, is that it’s too strong and durable to break down on its own. So, once it’s in the environment, it lingers for thousands of years, littering waterways, breaking corals and strangling birds and sea life.

Now, Northwestern University chemists have developed a new catalyst that quickly, cleanly and completely breaks down Nylon-6 in a matter of minutes — without generating harmful byproducts. Even better: The process does not require toxic solvents, expensive materials or extreme conditions, making it practical for everyday applications.

Not only could this new catalyst play an important role in environmental remediation, it also could perform the first step in upcycling Nylon-6 wastes into higher-value products.

The research will be published on Thursday (Nov. 30) in the journal Chem.

“The whole world is aware of the plastic problem,” said Northwestern’s Tobin Marks, the study’s senior author. “Plastic is a part of our society; we use so much of it. But the problem is: What do we do when we’re finished with it? Ideally, we wouldn’t burn it or put it into landfills. We would recycle it. We’re developing catalysts that deconstruct these polymers, returning them to their original form, so they can be reused.”

Marks is the Charles E. and Emma H. Morrison Professor of Chemistry and Vladimir N. Ipatieff Professor of Catalytic Chemistry at Northwestern’s Weinberg College of Arts and Sciences and a professor of materials science and engineering at Northwestern’s McCormick School of Engineering. He also is a faculty affiliate at the Paula M. Trienens Institute for Sustainability and Energy. Northwestern co-authors include Linda J. Broadbelt, the Sarah Rebecca Roland Professor of Chemical and Biological Engineering and senior associate dean of McCormick, and Yosi Kratish, a research assistant professor in Marks’ group.

A deadly difficulty

From clothing to carpet to seat belts, Nylon-6 is found in a variety of materials that most people use every day. But, when people are done with these materials, they end up in landfills or worse: loose in the environment, including the ocean. According to the World Wildlife Federation, up to 1 million pounds of fishing gear is abandoned in the ocean each year, with fishing nets composed of Nylon-6 making up at least 46% of the Great Pacific Garbage Patch.

“Fishing nets lose quality after a couple years of use,” said Liwei Ye, the paper’s lead first author who is a postdoctoral fellow in Marks’ laboratory. “They become so water-logged that it’s difficult to pull them out of the ocean. And they are so cheap to replace that people just leave them in the water and buy new ones.”

“There is a lot of garbage in the ocean,” Marks added. “Cardboard and food waste biodegrades. Metals sink to the bottom. Then we are left with the plastics.”

The greenest solvent is no solvent

Current methods to dispose of Nylon-6 are limited to simply burying it in landfills. When Nylon-6 is burned, it emits toxic pollutants such as nitrogen oxides, which are linked to various health complications including premature death, or carbon dioxide, an infamously potent greenhouse gas.

Although other laboratories have explored catalysts to degrade Nylon-6, those catalysts require extreme conditions (such as temperatures as high as 350 degrees Celsius), high-pressure steam (which is energetically expensive and inefficient) and/or toxic solvents that only contribute to more pollution.

“You can dissolve plastics in acid, but then you are left with dirty water,” Marks said. “What do you do with that? The goal is always to use a green solvent. And what type of solvent is greener than no solvent at all?”

Recovering building blocks for upcycling

To bypass these issues, the researchers looked to a novel catalyst already developed in Marks’ laboratory. The catalyst harnesses yttrium (an inexpensive Earth-abundant metal) and lanthanide ions. When the team heated Nylon-6 samples to melting temperatures and applied the catalyst without a solvent, the plastic fell apart — reverting to its original building blocks without leaving byproducts behind.

“You can think of a polymer like a necklace or a string of pearls,” Marks explained. “In this analogy, each pearl is a monomer. These monomers are the building blocks. We devised a way to break down the necklace but recover those pearls.”

In experiments, Marks and his team were able to recover 99% of plastics’ original monomers. In principle, those monomers then could be upcycled into higher-value products, which are currently in high demand for their strength and durability.

“Recycled nylon is actually worth more money than regular nylon,” Marks said. “Many high-end fashion brands use recycled nylon in clothes.”

Efficiently targeting Nylon-6

In addition to recovering a high yield of monomers, the catalyst is highly selective — acting only on the Nylon-6 polymers without disrupting surrounding materials. This means industry could apply the catalyst to large volumes of unsorted waste and selectively target Nylon-6.

“If you don’t have a catalyst that’s selective, then how do you separate the nylon from the rest of waste?” Marks said. “You would need to hire humans to sort through all the waste to remove the nylon. That’s enormously expensive and inefficient. But if the catalyst only degrades the nylon and leaves everything else behind, that’s incredibly efficient.”

Recycling these monomers also avoids the need to produce more plastics from scratch.

“These monomers are produced from crude oil, so they have a huge carbon footprint,” Ye said. “That’s just not sustainable.”

What’s next?

After filing a patent for the new process, Marks and his team have already received interest from potential industrial partners. They hope others can use their catalysts on a large scale to help solve the global plastic problem.

“Our research represents a significant step forward in the field of polymer recycling and sustainable materials management,” Ye said. “The innovative approach addresses a critical gap in current recycling technologies, offering a practical and efficient solution for the nylon waste problem. We believe it has implications for reducing the environmental footprint of plastics and contributing to a circular economy.”

The study, “Catalyst metal-ligand design for rapid, selective and solventless depolymerization of Nylon-6 plastics,” was supported by RePLACE (Redesigning Polymers to Leverage A Circular Economy), funded by the Office of Science of the U.S. Department of Energy (award numbers SC0022290 and DE-FG02-03ER15457) and the National Science Foundation (grant number CHE-1856619). Additional support came from the Institute for Catalysis in Energy Processes, which is a major research project within the Center for Catalysis and Surface Science at the Paula M. Trienens Institute for Sustainability and Energy.



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Charge your laptop in a minute or your EV in 10? Supercapacitors can help

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Charge your laptop in a minute or your EV in 10? Supercapacitors can help


Imagine if your dead laptop or phone could charge in a minute or if an electric car could be fully powered in 10 minutes.

While not possible yet, new research by a team of CU Boulder scientists could potentially lead to such advances.

Published today in the Proceedings of the National Academy of Sciences, researchers in Ankur Gupta’s lab discovered how tiny charged particles, called ions, move within a complex network of minuscule pores. The breakthrough could lead to the development of more efficient energy storage devices, such as supercapacitors, said Gupta, an assistant professor of chemical and biological engineering.

“Given the critical role of energy in the future of the planet, I felt inspired to apply my chemical engineering knowledge to advancing energy storage devices,” Gupta said. “It felt like the topic was somewhat underexplored and as such, the perfect opportunity.”

Gupta explained that several chemical engineering techniques are used to study flow in porous materials such as oil reservoirs and water filtration, but they have not been fully utilized in some energy storage systems.

The discovery is significant not only for storing energy in vehicles and electronic devices but also for power grids, where fluctuating energy demand requires efficient storage to avoid waste during periods of low demand and to ensure rapid supply during high demand.

Supercapacitors, energy storage devices that rely on ion accumulation in their pores, have rapid charging times and longer life spans compared to batteries.

“The primary appeal of supercapacitors lies in their speed,” Gupta said. “So how can we make their charging and release of energy faster? By the more efficient movement of ions.”

Their findings modify Kirchhoff’s law, which has governed current flow in electrical circuits since 1845 and is a staple in high school students’ science classes. Unlike electrons, ions move due to both electric fields and diffusion, and the researchers determined that their movements at pore intersections are different from what was described in Kirchhoff’s law.

Prior to the study, ion movements were only described in the literature in one straight pore. Through this research, ion movement in a complex network of thousands of interconnected pores can be simulated and predicted in a few minutes.

“That’s the leap of the work,” Gupta said. “We found the missing link.”



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AI headphones let wearer listen to a single person in a crowd, by looking at them just once

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AI headphones let wearer listen to a single person in a crowd, by looking at them just once


Noise-canceling headphones have gotten very good at creating an auditory blank slate. But allowing certain sounds from a wearer’s environment through the erasure still challenges researchers. The latest edition of Apple’s AirPods Pro, for instance, automatically adjusts sound levels for wearers — sensing when they’re in conversation, for instance — but the user has little control over whom to listen to or when this happens.

A University of Washington team has developed an artificial intelligence system that lets a user wearing headphones look at a person speaking for three to five seconds to “enroll” them. The system, called “Target Speech Hearing,” then cancels all other sounds in the environment and plays just the enrolled speaker’s voice in real time even as the listener moves around in noisy places and no longer faces the speaker.

The team presented its findings May 14 in Honolulu at the ACM CHI Conference on Human Factors in Computing Systems. The code for the proof-of-concept device is available for others to build on. The system is not commercially available.

“We tend to think of AI now as web-based chatbots that answer questions,” said senior author Shyam Gollakota, a UW professor in the Paul G. Allen School of Computer Science & Engineering. “But in this project, we develop AI to modify the auditory perception of anyone wearing headphones, given their preferences. With our devices you can now hear a single speaker clearly even if you are in a noisy environment with lots of other people talking.”

To use the system, a person wearing off-the-shelf headphones fitted with microphones taps a button while directing their head at someone talking. The sound waves from that speaker’s voice then should reach the microphones on both sides of the headset simultaneously; there’s a 16-degree margin of error. The headphones send that signal to an on-board embedded computer, where the team’s machine learning software learns the desired speaker’s vocal patterns. The system latches onto that speaker’s voice and continues to play it back to the listener, even as the pair moves around. The system’s ability to focus on the enrolled voice improves as the speaker keeps talking, giving the system more training data.

The team tested its system on 21 subjects, who rated the clarity of the enrolled speaker’s voice nearly twice as high as the unfiltered audio on average.

This work builds on the team’s previous “semantic hearing” research, which allowed users to select specific sound classes — such as birds or voices — that they wanted to hear and canceled other sounds in the environment.

Currently the TSH system can enroll only one speaker at a time, and it’s only able to enroll a speaker when there is not another loud voice coming from the same direction as the target speaker’s voice. If a user isn’t happy with the sound quality, they can run another enrollment on the speaker to improve the clarity.

The team is working to expand the system to earbuds and hearing aids in the future.

Additional co-authors on the paper were Bandhav Veluri, Malek Itani and Tuochao Chen, UW doctoral students in the Allen School, and Takuya Yoshioka, director of research at AssemblyAI. This research was funded by a Moore Inventor Fellow award, a Thomas J. Cabel Endowed Professorship and a UW CoMotion Innovation Gap Fund.



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Theory and experiment combine to shine a new light on proton spin

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Theory and experiment combine to shine a new light on proton spin


Nuclear physicists have long been working to reveal how the proton gets its spin. Now, a new method that combines experimental data with state-of-the-art calculations has revealed a more detailed picture of spin contributions from the very glue that holds protons together. It also paves the way toward imaging the proton’s 3D structure.

The work was led by Joseph Karpie, a postdoctoral associate in the Center for Theoretical and Computational Physics (Theory Center) at the U.S. Department of Energy’s Thomas Jefferson National Accelerator Facility.

He said that this decades-old mystery began with measurements of the sources of the proton’s spin in 1987. Physicists originally thought that the proton’s building blocks, its quarks, would be the main source of the proton’s spin. But that’s not what they found. It turned out that the proton’s quarks only provide about 30% of the proton’s total measured spin. The rest comes from two other sources that have so far proven more difficult to measure.

One is the mysterious but powerful strong force. The strong force is one of the four fundamental forces in the universe. It’s what “glues” quarks together to make up other subatomic particles, such as protons or neutrons. Manifestations of this strong force are called gluons, which are thought to contribute to the proton’s spin. The last bit of spin is thought to come from the movements of the proton’s quarks and gluons.

“This paper is sort of a bringing together of two groups in the Theory Center who have been working toward trying to understand the same bit of physics, which is how do the gluons that are inside of it contribute to how much the proton is spinning around,” he said.

He said this study was inspired by a puzzling result that came from initial experimental measurements of the gluons’ spin. The measurements were made at the Relativistic Heavy Ion Collider, a DOE Office of Science user facility based at Brookhaven National Laboratory in New York. The data at first seemed to indicate that the gluons may be contributing to the proton’s spin. They showed a positive result.

But as the data analysis was improved, a further possibility appeared.

“When they improved their analysis, they started to get two sets of results that seemed quite different, one was positive and the other was negative,” Karpie explained.

While the earlier positive result indicated that the gluons’ spins are aligned with that of the proton, the improved analysis allowed for the possibility that the gluons’ spins have an overall negative contribution. In that case, more of the proton spin would come from the movement of the quarks and gluons, or from the spin of the quarks themselves.

This puzzling result was published by the Jefferson Lab Angular Momentum (JAM) collaboration.

Meanwhile, the HadStruc collaboration had been addressing the same measurements in a different way. They were using supercomputers to calculate the underlying theory that describes the interactions among quarks and gluons in the proton, Quantum Chromodynamics (QCD).

To equip supercomputers to make this intense calculation, theorists somewhat simplify some aspects of the theory. This somewhat simplified version for computers is called lattice QCD.

Karpie led the work to bring together the data from both groups. He started with the combined data from experiments taken in facilities around the world. He then added the results from the lattice QCD calculation into his analysis.

“This is putting everything together that we know about quark and gluon spin and how gluons contribute to the spin of the proton in one dimension,” said David Richards, a Jefferson Lab senior staff scientist who worked on the study.

“When we did, we saw that the negative things didn’t go away, but they changed dramatically. That meant that there’s something funny going on with those,” Karpie said.

Karpie is lead author on the study that was recently published in Physical Review D. He said the main takeaway is that combining the data from both approaches provided a more informed result.

“We’re combining both of our datasets together and getting a better result out than either of us could get independently. It’s really showing that we learn a lot more by combining lattice QCD and experiment together in one problem analysis,” said Karpie. “This is the first step, and we hope to keep doing this with more and more observables as well as we make more lattice data.”

The next step is to further improve the datasets. As more powerful experiments provide more detailed information on the proton, these data begin painting a picture that goes beyond one dimension. And as theorists learn how to improve their calculations on ever-more powerful supercomputers, their solutions also become more precise and inclusive.

The goal is to eventually produce a three-dimensional understanding of the proton’s structure.

“So, we learn our tools do work on the simpler one-dimension scenario. By testing our methods now, we hopefully will know what we need to do when we want to move up to do 3D structure,” Richards said. “This work will contribute to this 3D image of what a proton should look like. So it’s all about building our way up to the heart of the problem by doing this easier stuff now.”



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