Northwestern University chemists have developed a new plastic upcycling process that could revolutionize recycling by significantly reducing or even eliminating the need to pre-sort mixed plastic waste. Using an inexpensive nickel-based catalyst, the method selectively breaks down polyolefin plastics—such as polyethylenes and polypropylenes, which make up nearly two-thirds of global plastic use—allowing industrial users to efficiently process large volumes of unsorted waste.

When the catalyst breaks down polyolefins, the low-value solid plastics transform into liquid oils and waxes, which can be upcycled into higher-value products, including lubricants, fuels and candles. Not only can it be used multiple times, but the new catalyst can also break down plastics contaminated with polyvinyl chloride (PVC), a toxic polymer that notoriously makes plastics “unrecyclable.”

“One of the biggest hurdles in plastic recycling has always been the necessity of meticulously sorting plastic waste by type,” said Northwestern’s Tobin Marks, the study’s senior author. “Our new catalyst could bypass this costly and labor-intensive step for common polyolefin plastics, making recycling more efficient, practical and economically viable than current strategies.”

“When people think of plastic, they likely are thinking about polyolefins,” said Northwestern’s Yosi Kratish, a co-corresponding author on the paper. “Basically, almost everything in your refrigerator is polyolefin based — squeeze bottles for condiments and salad dressings, milk jugs, plastic wrap, trash bags, disposable utensils, juice cartons and much more. These plastics have a very short lifetime, so they are mostly single-use. If we don’t have an efficient way to recycle them, then they end up in landfills and in the environment, where they linger for decades before degrading into harmful microplastics.”

A world-renowned catalysis expert, Marks is the Vladimir N. Ipatieff Professor of Catalytic Chemistry at Northwestern’s Weinberg College of Arts and Sciences and a professor of chemical and biological engineering at Northwestern’s McCormick School of Engineering. He is also a faculty affiliate at the Paula M. Trienens Institute for Sustainability and Energy. Kratish is a research assistant professor in Marks’ group, and an affiliated faculty member at the Trienens Institute. Qingheng Lai, a research associate in Marks’ group, is the study’s first author. Marks, Kratish and Lai co-led the study with Jeffrey Miller, a professor of chemical engineering at Purdue University; Michael Wasielewski, Clare Hamilton Hall Professor of Chemistry at Weinberg; and Takeshi Kobayashi a research scientist at Ames National Laboratory.

From yogurt cups and snack wrappers to shampoo bottles and medical masks, most people interact with polyolefin plastics multiple times throughout the day. Because of its versatility, polyolefins are the most used plastic in the world. By some estimates, industry produces more than 220 million tons of polyolefin products globally each year. Yet, according to a 2023 report in the journal Nature, recycling rates for polyolefin plastics are alarmingly low, ranging from less than 1% to 10% worldwide.

The main reason for this disappointing recycling rate is polyolefin’s sturdy, stubborn composition. It contains small molecules linked together with carbon-carbon bonds, which are famously difficult to break.

“When we design catalysts, we target weak spots,” Kratish said. “But polyolefins don’t have any weak links. Every bond is incredibly strong and chemically unreactive.”

Currently, only a few, less-than-ideal processes exist that can recycle polyolefin. It can be shredded into flakes, which are then melted and downcycled to form low-quality plastic pellets. But because different types of plastics have different properties and melting points, the process requires workers to scrupulously separate various types of plastics. Even small amounts of other plastics, food residue or non-plastic materials can compromise an entire batch. And those compromised batches go straight into the landfill.

Another option involves heating plastics to incredibly high temperatures, reaching 400 to 700 degrees Celsius. Although this process degrades polyolefin plastics into a useful mixture of gases and liquids, it’s extremely energy intensive.

“Everything can be burned, of course,” Kratish said. “If you apply enough energy, you can convert anything to carbon dioxide and water. But we wanted to find an elegant way to add the minimum amount of energy to derive the maximum value product.”

To uncover that elegant solution, Marks, Kratish and their team looked to hydrogenolysis, a process that uses hydrogen gas and a catalyst to break down polyolefin plastics into smaller, useful hydrocarbons. While hydrogenolysis approaches already exist, they typically require extremely high temperatures and expensive catalysts made from noble metals like platinum and palladium.

“The polyolefin production scale is huge, but the global noble metal reserves are very limited,” Lai said. “We cannot use the entire metal supply for chemistry. And, even if we did, there still would not be enough to address the plastic problem. That’s why we’re interested in Earth-abundant metals.”

For its polyolefin recycling catalyst, the Northwestern team pinpointed cationic nickel, which is synthesized from an abundant, inexpensive and commercially available nickel compound. While other nickel nanoparticle-based catalysts have multiple reaction sites, the team designed a single-site molecular catalyst.

The single-site design enables the catalyst to act like a highly specialized scalpel — preferentially cutting carbon-carbon bonds — rather than a less controlled blunt instrument that indiscriminately breaks down the plastic’s entire structure. As a result, the catalyst allows for the selective breakdown of branched polyolefins (such as isotactic polypropylene) when they are mixed with unbranched polyolefins — effectively separating them chemically.

“Compared to other nickel-based catalysts, our process uses a single-site catalyst that operates at a temperature 100 degrees lower and at half the hydrogen gas pressure,” Kratish said. “We also use 10 times less catalyst loading, and our activity is 10 times greater. So, we are winning across all categories.”

With its single, precisely defined and isolated active site, the nickel-based catalyst possesses unprecedented activity and stability. The catalyst is so thermally and chemically stable, in fact, that it maintains control even when exposed to contaminants like PVC. Used in pipes, flooring and medical devices, PVC is visually similar to other types of plastics but significantly less stable upon heating. Upon decomposition, PVC releases hydrogen chloride gas, a highly corrosive byproduct that typically deactivates catalysts and disrupts the recycling process.

Amazingly, not only did Northwestern’s catalyst withstand PVC contamination, PVC actually accelerated its activity. Even when the total weight of the waste mixture is made up of 25% PVC, the scientists found their catalyst still worked with improved performance. This unexpected result suggests the team’s method might overcome one of the biggest hurdles in mixed plastic recycling — breaking down waste currently deemed “unrecyclable” due to PVC contamination. The catalyst also can be regenerated over multiple cycles through a simple treatment with inexpensive alkylaluminium.

“Adding PVC to a recycling mixture has always been forbidden,” Kratish said. “But apparently, it makes our process even better. That is crazy. It’s definitely not something anybody expected.”

For more information: Nature Chemistry

Stretchable electronics are increasingly used in devices like smartphones, smartwatches, curved displays, and wearable sensors, but they face a trade-off between flexibility and electrical performance, as stretchable materials like elastomers typically underperform compared to rigid ones like metals or semiconductors. To address this, researchers have turned to origami and kirigami—Japanese techniques of folding and cutting paper—to enable stretchability in non-stretchable electronic materials. Origami creates bendable structures with hinges suitable for mounting rigid components, while kirigami uses slits to allow full structural deformation, making it ideal for large-area designs but less compatible with rigid parts.

In a groundbreaking study, Professor Eiji Iwase and Mr. Nagi Nakamura from the Department of Applied Mechanics and Aerospace Engineering at Waseda University, Japan, developed an innovative hybrid technique using kiri-origami structures.

“In this study, we have proposed a kiri-origami structure that incorporates both folding and cutting lines, combining the strengths of origami and kirigami while canceling out their weaknesses,” explains Iwase. “This structure enables large-number-of-unit, large area electronic devices, allowing rigid electronic components to be folded by stretching.”

The proposed kiri-origami design features a mutual orthogonal cutting line pattern. In this pattern, triangular joint panels consisting of two folding lines act as hinges and connect two square panels formed by the cutting lines. When stretched from a flat state, the square panels rise and rotate. This opens slits between the panels, ultimately resulting in a Z-shape around the hinges. This structure allows simultaneous mounting of rigid components and stretching to a target shape, while also supporting large-area and large-number-of-unit structures.

In ideal kiri-origami structures, called rigid-origami structures, the panels do not deform, and the hinges rotate frictionlessly. However, for a real stretchable electronic substrate, panel deformation and elastic repulsive forces cannot be overlooked, giving rise to an “elastic origami model.” To investigate these effects, the researchers tested the deformation of a rectangular elastic origami model using a simple stretching method, where the sample is clamped and stretched uniaxially. They observed that the elastic model deformed differently from the rigid model. They found that this difference occurs due to two factors: first, the clamping edges in the rigid model are free edges, while they are fixed in the elastic model. Second, the entire structure distorts while stretching due to non-uniform tension.

To mitigate these effects, the researchers developed a new folding method that introduces buffer structures. The buffer structures are trapezoidal extensions that connect all the edges of the kiri-origami structure to the clamps. The width of the shorter edge of the buffer structures is equal to the initial width of the kiri-origami structure, while the larger edge is set to the target stretched width of the rigid model. When a tensile force is applied, they extend and behave like springs. As a result, the entire structure stretches in two directions, matching the deformation of the rigid model while maintaining uniform tension.

The researchers demonstrated this technique by fabricating a stretchable display with more than 500 hinges and 145 LEDs. All hinges could fold up simultaneously, and the device’s performance was maintained before and after folding.

“Our approach makes it possible to develop stretchable electronic devices that can accommodate complex shapes and do not compromise on performance, including next-generation high-performance wearable sensors, curved displays, and flexible sensors and actuators for human assistance robots,” remarks Iwase.

This kiri-origami technique thus offers a scalable, structurally engineered solution for integrating high-performance electronic materials into flexible, stretchable devices—paving the way for innovative applications in electronics, healthcare, and robotics.

For more information: npj Flexible Electronics

Image: Kiri-origami structures combine the benefits of both origami and kirigami, incorporating their advantages while canceling their disadvantages, enabling the development of high-performance, stretchable, large-number-of-unit electronic devices.

Researchers at the University of Toronto Engineering have developed a new multimodal AI tool that could significantly accelerate the application of newly discovered materials. Led by Professor Seyed Mohamad Moosavi, the team’s study introduces an AI system capable of predicting how a material might perform in real-world conditions from the moment it is created—helping ensure that promising innovations reach their full potential.

The system focuses on a class of porous materials known as metal-organic frameworks (MOFs). Moosavi says that last year alone, materials scientists created more than 5,000 different types of MOFs, which have tunable properties that lead to a wide range of potential applications.

For example, MOFs can be used to separate CO2 from other gases in a waste stream, preventing the carbon from reaching the atmosphere and contributing to climate change. They can also be used to deliver drugs to particular areas of the body, or to add new functions to advanced electronic devices.

According to Moosavi, one major challenge facing the field is that a MOF created for one purpose often turns out to have the ideal properties for a completely different application.

For example, in one of their previous studies, it was found that a material originally synthesized for photocatalysis was instead very effective for carbon capture — but this discovery was only made seven years later.

“In materials discovery, the typical question is, ‘What is the best material for this application?’” says Moosavi.

“We flipped the question and asked, ‘What’s the best application for this new material?’ With so many materials made every day, we want to shift the focus from ‘what material do we make next’ to ‘what evaluation should we do next.’”

This approach aims to reduce the time lag between discovery and deployment of MOFs.

To help make this possible, ChemE PhD student Sartaaj Khan developed a multimodal machine learning system trained on various types of data typically available immediately after synthesis — specifically, the precursor chemicals used to make the material, and its powder X-ray diffraction (PXRD) pattern.

“Multimodality matters,” says Khan. “Just as humans use different senses — such as vision and language — to understand the world, combining different types of material data gives our model a more complete picture.”

The AI system uses a multimodal pretraining strategy to gain insights into a material’s geometry and chemical environment, enabling it to make accurate property predictions without needing post-synthesis structural characterization.

This can speed up the discovery process and help researchers recognize promising materials before they’re overlooked or shelved.

To test the model, the team conducted a ‘time-travel’ experiment. They trained the AI on material data available before 2017 and asked it to evaluate materials synthesized after that date.

The system successfully flagged several materials — originally developed for other purposes — as strong candidates for carbon capture. Some of those are now undergoing experimental validation in collaboration with the National Research Council of Canada.

Looking ahead, Moosavi plans to integrate the AI into the self-driving laboratories (SDLs) at U of T’s Acceleration Consortium, a global hub for automated materials discovery.

“SDLs automate the process of designing, synthesizing and testing new materials,” he says.

“When one lab creates a new material, our system could evaluate it — and potentially reroute it to another lab better equipped to assess its full potential. That kind of seamless inter-lab coordination could accelerate materials discovery.”

For more information: Nature Communications

Image: PhD student Sartaaj Takrim Khan, left, and Professor Seyed Mohamad Moosavi (ChemE) created a multimodal AI tool that can predict how metal-organic frameworks might perform in the real world. (Photo by Tyler Irving)