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ABSTRACT Elucidating the crystalline‐amorphous interface during decrystallization processes in semi‐crystalline polyethylene (PE) is crucial for the advancement of polymer theory and plastic‐to‐plastic recycling technologies. In this study, we carried out an in‐depth investigation of PE thin films undergoing melting or dissolution using a temperature‐controlled liquid flow‐cell experimental setup which provided in situ mid‐infrared (MIR, 4000–700 cm−1) and near‐infrared (NIR, 6000–4000 cm−1) spectra in real time. The spectroscopic results yielded molecular‐level information regarding PE decrystallization and chain disentanglement via fundamental vibrations, combination bands, and overtones which were correlated using hetero‐spectral two‐dimensional correlation spectroscopy (2D‐COS). A quantitative procedure for the calculation of PE degree of crystallinity was developed to track transformations of crystalline domains during melting and dissolution. This semi‐empirical model achieved a strong linear correlation of at least +0.93 in four spectral regions: 750–700 cm−1, 1500–1400 cm−1, 3000–2800 cm−1, and 4400–4200 cm−1. This analysis revealed important spectral trends about the interfacial solvation environment during these processes. Lastly, the time evolution of the unraveling, terminal methyl (CH3) groups of PE cilia was examined in relation to the decrystallization mechanism of PE. The insights obtained from this study advance the fundamental understanding necessary for developing new depolymerization and dissolution‐precipitation recycling strategies.
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Mid-infrared spectroscopy and machine learning for postconsumer plastics recycling
Materials recovery facilities (MRFs) require new automated technologies if growing recycling demands are to be met. Current optical screening devices use visible (VIS) and near-infrared (NIR) wavelengths, frequency ranges that can experience challenges during the characterization of postconsumer plastic waste (PCPW) because of the overly-absorbing spectral bands from dyes and other polymer additives. Technological bottlenecks such as these contribute to 91% of plastic waste never actually being recycled. The mid-infrared (MIR) region has attracted recent attention due to inherent advantages over the VIS and NIR. The fundamental vibrational modes found therein make MIR frequencies promising for high fidelity machine learning (ML) classification. To-date, there are no ML evaluations of extensive MIR spectral datasets reflecting PCPW that would be encountered at MRFs. This study establishes quantifiable metrics, such as model accuracy and prediction time, for classification of a comprehensive MIR database consisting of five PCPW classes that are of economic interest: polyethylene terephthalate (PET #1), high-density polyethylene (HDPE #2), low-density polyethylene (LDPE #4), polypropylene (PP #5), and polystyrene (PS #6). Autoencoders, an unsupervised ML algorithm, were applied to the random forest (RF), k-nearest neighbor (KNN), support vector machine (SVM), and logistic regression (LR) models. The RF model achieved accuracies of 100.0% in both the C–H stretching region (2990–2820 cm −1 ) and molecular fingerprint region (1500–650 cm −1 ). The C–H stretching region was found to be free from additives that were responsible for misclassification in other regions, making it a fruitful frequency range for future PCPW sorting technologies. The MIR classification of black plastics and polyethylene PCPW using ML autoencoders was also evaluated for the first time.
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- PAR ID:
- 10447275
- Date Published:
- Journal Name:
- Environmental Science: Advances
- Volume:
- 2
- Issue:
- 8
- ISSN:
- 2754-7000
- Page Range / eLocation ID:
- 1099 to 1109
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/d3va00111c
