Innovation & Technology

Plastic Pollution No More:
Chemistry Breakthroughs Revolutionize Recycling

Researchers from Northwestern and Cornell may have solved several critical challenges in recycling two of our most common plastics, thanks to machine learning and moisture from the air.

Published 1 month ago | About a 10 minute read

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A breath of fresh air for PET recycling

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Northwestern University chemists recently developed a non-toxic, resource-efficient, solvent-free method for breaking down plastic waste — by harnessing moisture from air.

The simple, new process uses an inexpensive catalyst to break apart the bonds in polyethylene terephthalate (PET) — the most common plastic in the polyester family — then, the broken pieces are merely exposed to ambient air. The trace amounts of moisture in air convert the broken-down PET into monomers — the crucial building blocks for plastics. From there, the researchers envision the monomers could be recycled into new PET products or other, more valuable materials.

Safer, cleaner, cheaper and more sustainable than current plastic-recycling methods, the new technique — published in the journal, Green Chemistry — greatly increases the viability of a circular economy for plastics.

“The US is the number-one plastic polluter per capita, and we only recycle 5 percent of those plastics,” said Northwestern’s Yosi Kratish, a research assistant professor of chemistry at Northwestern’s Weinberg College of Arts and Sciences and the study’s co-corresponding author. “There is a dire need for better technologies that can process different types of plastic waste. Most of the technologies that we have today melt down plastic bottles and downcycle them into lower-quality products. What’s particularly exciting about our research is that we harnessed moisture from air to break down the plastics, achieving an exceptionally clean and selective process. By recovering the monomers, which are the basic building blocks of PET, we can recycle or even upcycle them into more valuable materials.”

“Our study offers a sustainable and efficient solution to one of the world’s most pressing environmental challenges: plastic waste,” said Naveen Malik, the study’s first author. “Unlike traditional recycling methods, which often produce harmful byproducts like waste salts and require significant energy or chemical inputs, our approach uses a solvent-free process that relies on trace moisture from ambient air. This makes it not only environmentally friendly but also highly practical for real-world applications.”

Malik co-led the study with Kratish and Tobin J. Marks, the Charles E. and Emma H. Morrison Professor of Chemistry at Weinberg and a professor of materials science and engineering at Northwestern’s McCormick School of Engineering. At the time of the research, Malik was an postdoctoral fellow in Marks’ laboratory; now, he is a research assistant professor at India’s SRM Institute of Science and Technology.

Pain points in plastic recycling

Commonly used in food packaging and beverage bottles, PET plastics represent 12 percent of total plastics used globally. Because it does not break down easily, PET is a major contributor to plastic pollution. After use, it either ends up in landfills or, over time, degrades into microplastics or nanoplastics — which often end up in waterways.

Finding new ways to recycle plastic is a hot topic in research. But current methods to break down plastics require problematic conditions — including extremely high temperatures and solvents, which require copious amounts of energy and generate toxic byproducts. The catalysts used in these reactions (ex: platinum and palladium) are also often expensive or toxic, creating even more harmful waste. Post-process, researchers must then separate the recycled materials from the solvents — another time-consuming and energy-intensive process.

In previous work, Marks’ group at Northwestern became the first to develop catalytic processes that don’t require solvents. In the new study, the team again devised a solvent-free process.

“Using solvents has many disadvantages,” Kratish said. “They can be expensive, and you have to heat them up to high temperatures. Then, after the reaction, you are left with a soup of materials that you have to sort to recover the monomers. Instead of using solvents, we used water vapor from air. It’s a much more elegant way to tackle plastic recycling issues.”

An ‘elegant’ solution

To conduct the new study, the researchers used a molybdenum catalyst and activated carbon — both of which are inexpensive, abundant, non-toxic materials. To initiate the process, the researchers added PET — plastics made of large molecules with repeating units, which are linked together with chemical bonds — to the catalyst and activated carbon and heated up the mixture, and the chemical bonds within the plastic quickly broke apart.

Next, the researchers exposed the material to air. With the tiny bit of moisture from air, the material turned into terephthalic acid (TPA) — the highly valuable precursor to polyesters. The only byproduct was acetaldehyde — a valuable, easy-to-remove industrial chemical.

“Air contains a significant amount of moisture, making it a readily available and sustainable resource for chemical reactions,” Malik said. “On average, even in relatively dry conditions, the atmosphere holds about 10,000 to15,000 cubic kilometers of water. Leveraging air moisture allows us to eliminate bulk solvents, reduce energy input and avoid the use of aggressive chemicals — making the process cleaner and more environmentally friendly.”

“When we added extra water, it stopped working — it’s a fine balance,” Kratish said. “But it turns out, the amount of water in air was just the right amount.”

Solving multiple industry challenges

The resulting process is fast and effective. In just four hours, 94 percent of the possible TPA was recovered. The catalyst is also durable and recyclable, meaning it can be used time and time again without losing effectiveness. And the method works with mixed plastics, selectively recycling only polyesters. With its selective nature, the process bypasses the need to sort the plastics before applying the catalyst — a major economic advantage for the recycling industry.

When the team tested the process on real-world materials including plastic bottles, shirts and mixed plastic waste, it proved just as effective. It even broke colored plastics down into pure, colorless TPA.

Next, the researchers plan to increase the scale of the process for industrial use. By optimizing the process for large-scale applications, the researchers aim to ensure it can handle vast quantities of plastic waste.

“Our technology has the potential to significantly reduce plastic pollution, lower the environmental footprint of plastics and contribute to a circular economy — where materials are reused rather than discarded,” Malik said. “It’s a tangible step toward a cleaner future, and it demonstrates how innovative chemistry can address global challenges in a way that aligns with nature.”

Designing lower-impact HDPE with machine learning

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Meanwhile, chemists at Cornell University have found ways to reduce the environmental impact of another ubiquitous polymer: high-density polyethylene (HDPE). One of the world’s most common plastics, about 100 million metric tons of HDPE are produced annually — using more than 15 times the annual energy needed to power New York City and adding enormous amounts of plastic waste to landfills and oceans.

The Cornell researchers developed a machine-learning model that enables manufacturers to customize and improve HDPE materials — found in milk jugs, shampoo bottles, playground equipment and countless other things — by decreasing the amount of material needed for various applications. It can also be used to boost the quality of recycled HDPE to rival new, making recycling a more practical process.

“Implementation of this approach will facilitate the design of next-generation commodity materials and enable more efficient polymer recycling, lowering the overall impact of HDPE on the environment,” said Robert DiStasio Jr., associate professor of chemistry and chemical biology in the College of Arts and Sciences (A&S).

“Designing Polymers with Molecular Weight Distribution-Based Machine Learning,” published March 14 in the Journal of the American Chemical Society, is a collaboration between DiStasio and polymer experts Geoffrey Coates — the Tisch University Professor in the Department of Chemistry and Chemical Biology (A&S) — and Brett Fors, the Frank and Robert Laughlin Professor of Physical Chemistry (A&S). Doctoral student Jenny Hu is first author; postdoctoral researcher Zachary Sparrow, former postdoctoral researcher Brian Ernst and doctoral student Spencer Mattes contributed.

HDPE requires so much energy because it’s made on a huge scale, said Fors — whose lab focuses on sustainable polymers. There are also challenges to recycling it.

“It’s more expensive to recycle polyethylene than it is to make virgin plastic,” he said. “Another problem is when you mechanically recycle it, you start breaking polymer chains — which causes the properties to degrade.”

HDPE materials lose quality every time they are recycled, Coates added. “You can’t just take these plastics and melt them down. It’s not like aluminum that’s perfect every time. You have to work hard to valorize it and make the plastics useful,” adding that recyclers have about five cents to spend on valorizing — boosting the quality — for each pound of recycled plastic.

Currently, recycling facilities improve the quality of recycled output by adding a small amount of virgin plastic. However, the mix of recycled material varies day by day — varying the amount of new plastic needed. The key to using less material (and energy) manufacturing polyethylene — while controlling the quality and physical properties of recycled material — lies in understanding how the various lengths of polymer chains in a sample, called its molecular weight distribution, influences its properties. The key factors: how viscous it is during manufacturing, and its strength and toughness as a finished product.

PEPPr

DiStasio and his team trained their machine-learning model, called PEPPr (PolyEthylene Property PRedictor) using a library of more than 150 polyethylene samples synthesized and characterized by Coates, Fors and members of their labs.

“We needed a library of polymers with different molecular weight distributions,” DiStasio said. “We also wanted to have polymers with a diverse set of both processability and mechanical properties.”

Machine-learning power is necessary for the complex task of understanding the relationship between the composition of these materials and their properties, the researchers say.

DiStasio said PEPPr solves two problems. If the molecular weight distribution of an HDPE sample is known, the model can predict its properties: melt viscosity, toughness and strength. It can also be used for the inverse; if a user has a set of targeted properties in mind, the model can tell them what polymer sample would have those properties.

“If you want to make a plastic bag, you will need different properties in the melt than if you want to make a kayak,” Fors said.

The PEPPr approach is a first step toward smarter, more tailored polymer design and more effective and sustainable recycling processes, the researchers said. They plan to expand the scope of properties that can be predicted and add processing methods, which can be quite influential, to the model. They also hope to expand the model to include other polymer classes.

“We should be able to develop these types of models for any type of commercial polymer,” Fors said. “It should be a general way to tune properties and recycle other materials, as well.”

This research was supported by the National Science Foundation (NSF) Center for Sustainable Polymers and the Cornell Center for Materials Research, with funding from the NSF’s Research Experience for Undergraduates program.