Mid-infrared spectroscopy and machine learning for postconsumer plastics recycling

Stavinski, Nicholas; Maheshkar, Vaishali; Thomas, S.G.; Dantu, Karthik; Velarde, Luis

doi:10.1039/d3va00111c

Cited by 4 publications

(1 citation statement)

References 82 publications

(116 reference statements)

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“…[8] On the other hand, research on machine learning of spectra, particularly in the field of plastic recycling, has been increasing significantly in recent years. [9][10][11] Meanwhile, in order to evaluate degradation for coatings, many studies have focused on changes in specific bands in the mid-infrared spectrum. [5,[12][13][14][15][16][17][18][19] However, to our best knowledge, there is no example of analyzing the spectrum of the entire wide mid-infrared region for coating films by using modern statistical methods.…”

Section: Introductionmentioning

confidence: 99%

Mid‐Infrared Spectroscopy and Machine Learning for Nondestructive Detection of Inapparent Deterioration in Acrylic Waterborne Coatings for Wood

Teramoto,

Ito,

Yamamoto

et al. 2023

Advanced Sustainable Systems

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This study presents an approach for nondestructive detection of inapparent deterioration in waterborne acrylic coatings (containing cellulose nanofibers (CNFs)) for wood by using mid‐infrared spectroscopy and machine learning. The method evaluates films that mimic coatings before and after 500 h of accelerated weathering, equivalent to roughly 1 year of outdoor exposure. No noticeable transformation in film appearance is evident with a spectrophotometer following the accelerated weathering. Chemiluminescence analysis indicates oxidative degradation predominantly in the acrylic resin, an impact that the CNFs seem to mitigate. Whereas attenuated total reflectance (ATR)‐Fourier transform infrared (FTIR) spectroscopy commonly identifies chemical changes in visibly degraded coatings, it does not clearly discern prior, inapparent deterioration. In this context, machine learning algorithms (such as k‐nearest neighbors, decision tree, random forest (RF), and support vector machine (SVM)) categorize these nuanced changes by using the absorbance from 400 to 4000 cm−1 as explanatory variables. The SVM model exhibits the highest predictive accuracy, and the RF recognizes crucial variables in some wavenumber zones. This approach has the potential for enhancing recoating schedules, cutting costs, and encouraging sustainable use of wood.

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Section: Introductionmentioning

confidence: 99%

Mid‐Infrared Spectroscopy and Machine Learning for Nondestructive Detection of Inapparent Deterioration in Acrylic Waterborne Coatings for Wood

Teramoto,

Ito,

Yamamoto

et al. 2023

Advanced Sustainable Systems

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Machine learning to enhance sustainable plastics: A review

Guarda,

Caseiro,

Pires

2024

Journal of Cleaner Production

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Molecular Characterization of Plastic Waste Using Standoff Photothermal Spectroscopy

Zhao,

Chakraborty,

Meng

et al. 2023

ECS Sens. Plus

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The success of plastic recycling critically depends on the ability to sort plastics based on their resin identification codes to minimize cross-contamination and increase the quality of recycled material. However, achieving effective resin identification with high sensitivity and molecular-level selectivity remains challenging. In this study, a standoff detection technique that combines a highly sensitive micromechanical resonator (bi-material microcantilever) with mid-infrared spectroscopy, providing high selectivity, has been developed. By employing machine learning techniques, different types of plastics have been accurately detected and identified with 100% accuracy, even in the presence of additives and contaminants. This method offers a promising approach for rapidly and efficiently characterizing plastic waste, making it suitable for potential application in material recovery facilities.

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Mid-infrared spectroscopy and machine learning for postconsumer plastics recycling

Abstract: 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...

Cited by 4 publications

References 82 publications

Mid‐Infrared Spectroscopy and Machine Learning for Nondestructive Detection of Inapparent Deterioration in Acrylic Waterborne Coatings for Wood

Mid‐Infrared Spectroscopy and Machine Learning for Nondestructive Detection of Inapparent Deterioration in Acrylic Waterborne Coatings for Wood

Machine learning to enhance sustainable plastics: A review

Molecular Characterization of Plastic Waste Using Standoff Photothermal Spectroscopy

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