Closing the Loop via Molecular Polymer Rheology

Funder: Dutch Polymer Institute

Our society has a need and desire to significantly increase plastic recycling rates. However, a very significant challenge is the typical current quality of recycled plastic materials, which limits growth and use in advanced applications and end markets. A second problem is that molecules in plastic degrade (either breaking up or reacting together) each time the material is reused. As a result, recycled plastic is typically used in lower value applications, continuously downgrading the material. One reason for this is that we have limited knowledge on the characteristics of the recycled material. Advanced and detailed material characterization is time consuming and expensive, so it is not economically feasible to perform a complete set of measurements on any material. Instead, it is far more likely that only a small number of simple measurements on a given recycled material might be made, to determine its quality and possible uses. This project explores what are the best subset of measurements to make in order to understand as much about the material as possible, so that we can make best use of it. This is enabled by improving current models that predict properties from molecular structure and material composition for relevant polyolefin plastics, and by seeking to improve our prediction of the molecular degradation. We will use data science tools (similar to those used by Google or Netflix to make recommendations for users) to infer what structural information can be gleaned from a small number of measurements, combined with prior knowledge of the history of the materials. We aim to produce software tools that can be used by recyclers and material science companies to improve the economics of high quality and quantity polyolefin recycling.