This project aims to reduce both the amount of waste plastic that ends up in landfills or incinerators and the amount of virgin plastic required for new products by focusing on two challenging issues: (1) sorting of mixed plastic waste and (2) valorization of recovered plastic. High-throughput, automated sorting will be accomplished through the combination of novel sensor technology that will register the molecular signature of each piece of plastic, and machine learning that, on the basis of this molecular signature, will identify in real time the specific type of each piece of plastic. From the integration of these new capabilities with existing technologies, an advanced mixed waste plastic sorting process emerges that is adaptable to sorting other materials to enable their recycling as well. Upcycling of sorted plastic will be achieved through dissolution of select types of plastic in environmentally responsible solvents in order to recover the desirable type of plastic (i.e., polyolefins), separate it from additives or impurities, and render it suitable for reuse in new products. Tailored chemical modification of polyolefins will produce functional waxes that can serve as building blocks for high-value materials. This research contributes to the Nation's advanced manufacturing capabilities and helps meet both consumer demand for and corporate commitments to incorporating a high fraction of recycled plastic into their products. A broad range of focused activities are organized to facilitate the participation of a diverse student cohort in the research tasks and the sharing of the knowledge generated with the local community.

The objective of this fundamental engineering research project is to enable a significant increase in both the fraction of waste plastic that is being recycled and in the value of plastic recovered for use in new products. Two aims will be pursued toward this objective: (Aim 1): high-throughput multi-modal sensor recognition and autonomous sorting of plastic waste and (Aim 2) physical and chemical molecular valorization of recovered polyolefins. Aim 1 focuses on developing novel optical sensors and advanced machine learning for the real-time, high-throughput molecular-level detection of different types of plastics. The detection capabilities will be coupled with vision-based recognition and tracking. The integration of the newly developed capabilities with existing technologies will result in an advanced sorting process that is deployable in existing materials recovery facilities, adaptable to variable plastic waste composition, scale-able, upgradable, and transferable to other industries. Aim 2 will develop physical and chemical methodologies for the upcycling of plastics. Environmentally responsible solvents will be used for the recovery of pristine polymers via dissolution/precipitation processing of polyolefin-based plastics that incorporate other polymers, additives, fillers, and/or impurities. From a polyolefin starting material, telechelic short-chain polyethylene waxes are developed, which can be used as building blocks for high-value materials such as long-chain aliphatic polyester, cross-linked polyolefins, and resins for additive manufacturing technologies. The fundamental and applied knowledge generated in this project is poised to have a transformative impact on addressing the grand challenge of significantly increasing the global plastic recycling rates. This grand challenge is tackled here in a manner that is environmentally responsible, technically feasible, economically competitive, and societally beneficial.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

National Science Foundation (NSF)
Emerging Frontiers (EF)
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Christina Payne
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Suny at Buffalo
United States
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