Reimagining Plastic Recycling: Data-Driven Dynamic Chemistry for Upcycling Mixed Waste

Plastic waste presents a major climate and energy challenge—contributing to persistent environmental pollution and consuming significant energy in both production and recycling processes. A central bottleneck is the incompatibility of mixed plastic waste streams, which leads to phase separation and degraded performance in recycled materials. Current recycling methods require costly sorting and often result in downcycled, low-value products. To overcome this, the researchers propose a transformative approach that leverages dynamic bonding chemistry to compatibilize chemically diverse plastics without sorting. These reversible bonds allow phase interfaces to reorganize and stabilize during processing, dramatically improving material properties. Combined with an AI-guided robotic experimentation platform, which rapidly screens and optimizes polymer formulations, the researchers will establish a scalable method for upcycling mixed plastic waste into high-performance, re-processable materials. This seed project will lay the groundwork for a generalizable framework in sustainable polymer design—enabling practical solutions to global plastic waste through data driven, chemistry-enabled innovation.

“Plastic waste is one of the most pressing climate and environmental challenges, in part due to the incompatibility of mixed plastic streams that limit recycling and lead to low-value products. Our project combines dynamic bond chemistry with AI-guided robotic experimentation to develop predictive design strategies that transform unsorted plastic waste into high-performance, reprocessable materials, offering a scalable pathway toward circular and value-added plastics.”

Jie Xu, Scientist, Argonne National Lab; CASE Affiliated Scientist, University of Chicago’s Pritzker School of Molecular Engineering.