Improving Composition Analysis and Sorting of Plastics

Increasing the quantity and quality of recycled plastics requires fast, accurate, and robust identification and sortation methods. Data and tools to improve the measurement of plastic composition also help to improve micro- and nanoplastic identification and quantification. The following outlines NIST’s research activities to advance the composition analysis and sorting of plastics. 

Research Activities

Time-gated Raman spectroscopy for plastics identification and characterization

While Raman spectroscopy is a powerful non-destructive technique for the identification and characterization of plastics, its effectiveness suffers because many common additives, such as pigments, are highly fluorescing. Time-gated Raman can effectively extract Raman peaks from fluorescent backgrounds of post-consumer plastics. This activity is examining the potential for quantifying plasticizers with this technique as well as exploring the potential as an in-line process measurement. Read more. Contact: Anthony Kotula.

Mass Spectrometry (MS) of plastics-related compounds

The Mass Spectrometry Data Center develops evaluated mass spectral libraries and software pipelines and interfaces for visualizing, organizing, and validating MS data. They have several efforts ongoing to advance composition analysis of polymers and plastics. Contact: Yamil Simon.

Examples of related activities include:

• Updating Mass Spectral Libraries: Expanding the number of plastics-related small molecule compounds in the Electron Ionization Mass Spectral and Tandem Mass Spectral libraries. Read a recent publication summarizing these efforts.
• Method Development: NIST is working to simplify the complexity of data acquisition and eliminate interferences or contamination in the chemical analysis of plastics-related compounds.
• Extractable and Leachable Identification: Testing the mass spectral libraries by developing techniques to identify extractable and leachable compounds from plastics and their degradation products. Additionally, imaging is being leveraged to study leaching from plastic particles.
• Software Development: NIST has developed a pyrolysis gas chromatography mass spectrometry (pyGC-MS) software tool for the identification of polymers by pyrolysis breakdown products (NIST-Polymer Pyrolysis Search).
• Correlation of MS and spectroscopy data:  NIST is working to generate data sets that combine mass spectrometry and Raman spectroscopy of polymer and plasticizer mixtures.

Design and Measurements of Model Polyolefins and Compatibilizers 

Recycling streams contain complex mixtures of different classes of polyolefins (e.g. high-density polyethylene (HDPE), low-density polyethylene (LDPE), and polypropylene (PP)), different molar mass and comonomer distributions within each class, and various additives/impurities which complicate their separation and recycling.  This effort focuses on (1) the synthesis of model polyolefins to facilitate rigorous characterization of polyolefin structure/property/processing relationships, (2) strategic labeling (isotopic, functional, and dynamic chemistries) of specific chain segments and blend components to understand crystallization and deformation in functional polyolefins and complex mixtures, and (3) mechanical testing and simulation of compatibilized polyolefin blends to connect interfacial structure to bulk properties. To learn more, visit the Macromolecular Architectures project page. Contact: Aaron Burkey, Sara Orski, and Debra Audus.

Property Prediction from NIR Spectroscopy for Improved Polyolefin Sortation

The combination of near-infrared (NIR) spectroscopy with machine learning can significantly enhance recyclers’ ability to sort polyolefins. This effort uses slower measurement techniques such as size exclusion chromatography (SEC) and differential scanning calorimetry (DSC) to generate training data for machine learning. This enables polyolefins to be sorted based on properties as opposed to resin codes. Key outputs include findable, accessible, interoperable and reuseable (FAIR) data sets and open-source software for ease of adoption. To learn more, read recent publications on correlating NIR with bulk properties and NIR and machine learning for sorting or visit the  Macromolecular Architectures project page. Contact: Sara Orski, and Debra Audus.

Advanced Physical Property-Based Strategies for Sorting Plastics

This work aims to enable novel physical property-based methods for sorting and separating waste plastics. These efforts include:

• Frictional Sorting: The friction coefficient of plastics varies strongly near (but still below) the melting temperature, which provides a potential strategy to sort polyolefins as described in this publication. Contact: Kalman Migler.
• Magnetic Separation: Magnetic levitation (MagLev) offers a promising method for separating plastics based on their density by levitating plastics in a magnetic field gradient. Contact: Anthony Kotula.

To learn more, visit the Polymer Advanced Manufacturing and Rheology project page.

Explore the rest of the Circular Economy program’s Polymer / Plastics actvities.