Researchers at Washington University in St. Louis, Missouri, have started a multiyear project to develop new types of plastic that can be used for a wide range of applications — then, it can all be recycled into a single base material for reuse, the McKelvey School of Engineering reported.
Right now, less than a third of waste materials in the United States are recycled, according to the university. While much of the waste that goes to landfills or becomes litter is the result of a lack of education or effort, there is also a major problem with plastic recycling in particular.
While plastic may be viewed as one category of materials, it is made up of a range of chemical formulas with an incredible number of additives and dyes. These different plastics can't be recycled together — not just because it produces low-quality materials, but also because different types of plastic need different recycling processes altogether, and mixing in even a single piece of the wrong type of plastic can taint an entire batch.
A new framework for plastic products, one wherein all plastics are drawn from a common pool of resources and returned to that same pool via a single recycling process without costly, messy sorting would save both industries and consumers an incredible amount of money. This would also reduce waste across the board, prevent a massive amount of litter, and cut pollution caused by manufacturing new plastic.
That's what this project is geared toward. Christopher Cooper, assistant professor of energy, environmental, and chemical engineering at WashU; Brooks Abel, assistant professor of chemistry at the University of California Berkeley; and Debra Audus and Sara Orski of the National Institute of Standards and Technology accepted a grant of almost $1.4 million from the National Science Foundation to use artificial intelligence to design a variety of sustainable plastic polymers that can easily be recycled back into a monomer.
"This work will create new synthetic strategies to control chain-end and side-chain functionality, branch type and frequency, and dynamic bond incorporation for polymers produced by cationic ring-opening polymerization," Cooper said.
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"At the same time, a physics-informed AI will be continuously improved through active learning approaches and then used to perform inverse design to create new [architecturally diverse and deconstructable] polymers. These ADD polymers will be designed to achieve targeted properties within specified tolerances and will be validated against industry benchmarks."
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