skip to main content

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: 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.  more » « less
Award ID(s):
2029375 1753207
PAR ID:
10447275
Author(s) / Creator(s):
; ; ; ;
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
More Like this
  1. ; ; ; ; ; ( , ECS Sensors Plus)

    An accurate molecular identification of plastic waste is important in increasing the efficacy of automatic plastic sorting in recycling. However, identification of real-world plastic waste, according to their resin identification code, remains challenging due to the lack of techniques that can provide high molecular selectivity. In this study, a standoff photothermal spectroscopy technique, utilizing a microcantilever, was used for acquiring mid-infrared spectra of real-world plastic waste, including those with additives, surface contaminants, and mixed plastics. Analysis of the standoff spectral data, using Convolutional Neural Network (CNN), showed 100% accuracy in selectively identifying real-world plastic waste according to their respective resin identification codes. Standoff photothermal spectroscopy, together with CNN analysis, offers a promising approach for the selective characterization of waste plastics in Material Recovery Facilities (MRFs).

     
    more » « less
  2. ; ; ; ; ; ; ( , ArXivorg)
    Mid-infrared (MIR) spectrometers are invaluable tools for molecular fingerprinting and hyper-spectral imaging. Among the available spectroscopic approaches, GHz MIR dual-comb absorption spectrometers have the potential to simultaneously combine the high-speed, high spectral resolution, and broad optical bandwidth needed to accurately study complex, transient events in chemistry, combustion, and microscopy. However, such a spectrometer has not yet been demonstrated due to the lack of GHz MIR frequency combs with broad and full spectral coverage. Here, we introduce the first broadband MIR frequency comb laser platform at 1 GHz repetition rate that achieves spectral coverage from 3 to 13 {\mu}m. This frequency comb is based on a commercially available 1.56 {\mu}m mode-locked laser, robust all-fiber Er amplifiers and intra-pulse difference frequency generation (IP-DFG) of few-cycle pulses in \c{hi}(2) nonlinear crystals. When used in a dual comb spectroscopy (DCS) configuration, this source will simultaneously enable measurements with {\mu}s time resolution, 1 GHz (0.03 cm-1) spectral point spacing and a full bandwidth of >5 THz (>166 cm-1) anywhere within the MIR atmospheric windows. This represents a unique spectroscopic resource for characterizing fast and non-repetitive events that are currently inaccessible with other sources. 
    more » « less
  3. ; ; ; ; ; ; ( , Light: Science & Applications)
    Abstract

    Mid-infrared (MIR) spectrometers are invaluable tools for molecular fingerprinting and hyper-spectral imaging. Among the available spectroscopic approaches, GHz MIR dual-comb absorption spectrometers have the potential to simultaneously combine the high-speed, high spectral resolution, and broad optical bandwidth needed to accurately study complex, transient events in chemistry, combustion, and microscopy. However, such a spectrometer has not yet been demonstrated due to the lack of GHz MIR frequency combs with broad and full spectral coverage. Here, we introduce the first broadband MIR frequency comb laser platform at 1 GHz repetition rate that achieves spectral coverage from 3 to 13 µm. This frequency comb is based on a commercially available 1.56 µm mode-locked laser, robust all-fiber Er amplifiers and intra-pulse difference frequency generation (IP-DFG) of few-cycle pulses inχ(2)nonlinear crystals. When used in a dual comb spectroscopy (DCS) configuration, this source will simultaneously enable measurements with μs time resolution, 1 GHz (0.03 cm−1) spectral point spacing and a full bandwidth of >5 THz (>166 cm−1) anywhere within the MIR atmospheric windows. This represents a unique spectroscopic resource for characterizing fast and non-repetitive events that are currently inaccessible with other sources.

     
    more » « less
  4. ; ; ; ( , ACS Sustainable Chemistry & Engineering)
    Subramaniam, B. Executive Editor (Ed.)
    This research presents pioneering work on transforming a variety of waste plastic into synthetic graphite of high quality and purity. Six recycled plastics in various forms were obtained – including reprocessed polypropylene, high-density polyethylene flakes, shredded polyethylene films, reprocessed polyethylene (all obtained from Pennsylvania Recycling Markets Center), polystyrene foams and polyethylene terephthalate bottles (both sourced from a local recycling bin). The waste plastics were carbonized in sealed tubing reactors. The study shows that this versatile process can be used on a mix of waste plastics in a variety of recycled forms to obtain a uniform graphitic carbon phase, hence addressing the challenges of separation and transportation faced by the plastic recycling industry. The conversion yield to elemental carbon for recycled plastics was improved by up to 250% by using graphene oxide (GO) additives. Five different grades of GO and graphene were used to gain insights into the interaction mechanisms between plastics and GO during pyrolysis. The effect of GO additives on carbonization was analyzed using thermogravimetric analysis / differential scanning calorimetry and ReaxFF-based reactive molecular dynamics simulations. The obtained cokes were graphitized at 2500 ℃ and the graphitic quality of the synthetic graphites was analyzed using X-ray diffraction, transmission electron microscopy, and Raman spectroscopy. The plastic waste-derived synthetic graphites exhibit remarkable graphitic quality with crystallite sizes comparable with a model graphitizable material – anthracene coke. The thin, flake-like morphology and nanostructure featuring well-stacked contiguous lamellae make these graphitic carbons highly promising candidates for energy storage applications. Based on our experiments and atomistic-scale simulations we propose interaction mechanisms between the plastic polymers and the graphenic additives that explain the chemical conversion pathways for GO-assisted waste plastic carbonization and graphitization. 
    more » « less
  5. ; ; ( , Recycling)

    Plastics recycling is an important component of the circular economy. In mechanical recycling, the recovery of high-quality plastics for subsequent reprocessing requires plastic waste to be first sorted by type, color, and size. In chemical recycling, certain types of plastics should be removed first as they negatively affect the process. Such sortation of plastic objects at Materials Recovery Facilities (MRFs) relies increasingly on automated technology. Critical for any sorting is the proper identification of the plastic type. Spectroscopy is used to this end, increasingly augmented by machine learning (ML) and artificial intelligence (AI). Recent developments in the application of ML/AI in plastics recycling are highlighted here, and the state of the art in the identification and sortation of plastic is presented. Commercial equipment for sorting plastic recyclables is identified from a survey of publicly available information. Automated sorting equipment, ML/AI-based sorters, and robotic sorters currently available on the market are evaluated regarding their sensors, capability to sort certain types of plastics, primary application, throughput, and accuracy. This information reflects the rapid progress achieved in sorting plastics. However, the sortation of film, dark plastics, and plastics comprising multiple types of polymers remains challenging. Improvements and/or new solutions in the automated sorting of plastics are forthcoming.

     
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email