BUFFALO, N.Y. – Recycled plastics are promoted on everything from water bottles and fleece jackets to shopping bags and yogurt cups. Verifying such claims, however, is another matter because there is no quick and reliable way to measure how much recycled plastic these products contain.
University at Buffalo researchers are addressing this problem by combining several scientific tests, as well as artificial intelligence, to create a new method for differentiating recycled plastic from new plastic.
Described in a study published today in Nature's Communications Engineering , the method aims to help companies, regulatory agencies and other organizations better monitor plastic recycling.
"Our goal is to create a quick and reliable tool that can be used to verify recycled material content, as well as enforce recycling regulations," says corresponding author Amit Goyal, PhD, SUNY Distinguished Professor and SUNY Empire Innovation Professor in the UB Department of Chemical and Biological Engineering.
The tool, he says, aims to "improve the quality of plastic products and help reduce plastic waste, which will support a more circular economy where plastic pollution and its associated health and environmental risks are reduced."
Why it's difficult to tell recycled plastic from new plastic
When plastic is recycled, it is melted, cleaned and remolded. The end product looks just like new plastic and it has a very similar chemical makeup. But there are subtle differences, such as microscopic impurities and broken polymer chains, found in recycled plastics.
To spot these differences, the research team employed four sensing techniques. They are:
· Triboelectric testing. This measures how plastic gains and holds an electrical charge when surfaces touch – in other words, static electricity. Recycled plastics often hold a charge longer due to structural defects from repeated processing.
· Dielectric/impedance spectroscopy. This technique applies an electric field to measure how a plastic stores and loses energy. Recycled plastics show lower energy storage and higher energy loss.
· Capacitance analysis. This tracks how quickly plastic charges and discharges in a circuit. Differences in timing can reveal changes in electrical properties caused by recycling plastic.
· Mid-infrared spectroscopy. This shines light on the chemical structure of plastics, and it can show fragmented polymer chains found in recycled plastic.
Machine learning analyzes data, predicts recycled content
Researchers tested the method by examining new and recycled PET, or polyethylene terephthalate, which is a common plastic used to make juice bottles, peanut butter jars and other goods.
To analyze and combine data from these tests, the researchers utilized machine learning, which is a type of AI. Their machine learning model studied the test results and learned to recognize patterns in the data that correlate with recycled plastic percentages.
The system was more than 97% effective at determining the percentage of recycled content in PET samples that contained anywhere from 0% to 50% recycled material.
"This is an ideal example of combining cutting-edge innovation in science and engineering with AI for social good, and to potentially realize significant societal impact," said Goyal.
Team's future work aims to create a portable device
Goyal says the team's future work will involve combining the method's different sensing techniques and machine learning model into a portable device.
"By fabricating such a device, we hope to enable widespread, real-time monitoring of recycled plastics in commercial products," he says.
The work's relevance will grow, he says, as more states and countries adopt regulations that require plastics to be made with some recycled materials. Such regulations are expected in the near future given ongoing work of the Intergovernmental Negotiating Committee – a United Nations-led initiative – to finalize an international legally binding agreement to end plastic pollution.
The New York State Center for Plastics Recycling Research and Innovation at UB provided funding for the research. The center is supported by a grant from New York State Environmental Protection Fund, administered by the New York State Department of Environmental Conservation.
