Circular economy projects related to polymer or plastic science.
Plastics made from recycled goods are often degraded compared to virgin polymers. We develop measurement methods that focus on the processing steps that occur when making recycled goods, with a focus on contamination by mixed plastics waste streams.
Improved sortation of comingled plastics improves the quality of feedstocks for recycling and as a result, improves the properties of post-consumer resins. This project describes a data analysis pipeline that enables researchers to utilize the rapid characterization technique of near-visible infrared spectroscopy (NIR), used in recycling facilities, to predict the physical properties of pure polyolefins, hydrocarbon-based plastics that can be especially challenging to sort with other methods. This has been accomplished by using a combination of chemometric analysis and correlations with physical measurements, such as size exclusion chromatography.
Core competencies for this work include machine learning, polymer analytical chemistry, polymer physics, chemometric analysis with team shared expertise among NIST Materials Science and Engineering Division’s Macromolecular Architectures and Polymer Analytics projects.