Near-infrared (NIR) spectroscopy is a rapid and commercially used technique for material identification by recycling facilities, however as currently used it does not provide complete identification of many recycled polymeric materials. Transmutation between IR and other measurement techniques to measure chain composition, topology and conformation would enhance the effectiveness of NIR. In this project, we aim to develop machine learning models to correlate the measurement signals from non-rapid measurements to IR and provide model datasets for improved commercial algorithms for advanced sorting and processing equipment of recycled polyolefins.
I am a chemical engineer with a background in polymer composites synthesis, characterization and degradation. Currently, I am working with the Macromolecular Architectures project team in Polymers and Complex Fluid Group to investigate effects of polyolefin architecture on infrared x-ray in post-consumer resins and correlate data between multiple measurement modalities using appropriate machine learning algorithms.
A list of select non-NIST publications is provided below. Complete list of all publications is available on my GoogleScholar