A state-of-the-art review of multilayer packaging recycling: Challenges, alternatives, and outlook

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 447, P. 141403 - 141403

Published: Feb. 28, 2024

Language: Английский

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Deep learning-based construction and demolition plastic waste classification by resin type using RGB images

Resources Conservation and Recycling, Journal Year: 2024, Volume and Issue: 212, P. 107937 - 107937

Published: Oct. 4, 2024

Language: Английский

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Raman spectroscopy integrated with machine learning techniques to improve industrial sorting of Waste Electric and Electronic Equipment (WEEE) plastics

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 373, P. 123897 - 123897

Published: Jan. 1, 2025

Current industrial separation and sorting technologies struggle to efficiently identify classify a large part of Waste Electric Electronic Equipment (WEEE) plastics due their high content certain additives. In this study, Raman spectroscopy in combination with machine learning methods was assessed develop classification models that could improve the identification Polystyrene (PS), Acrylonitrile Butadiene Styrene (ABS), Polycarbonate (PC) blend PC/ABS contained WEEE streams, including black plastics, increase recycling rate, enhance circularity. spectral analysis carried out two lasers different excitation wavelengths (785 nm 1064 nm) varying setting parameters (laser power, integration time, focus distance) aim at reducing fluorescence. data were used train test Discriminant Analysis (DA) Support Vector Machine (SVM) algorithms an iterative procedure assess performance identifying classifying real plastics. settings optimized considering industry requirements, such as process productivity (classification short measuring time for fast identification) product quality (purity sorted polymers). Classification trained, first approach, only on target plastics; second all polymers expected stream, leading realistic overview potential scalability advanced limitations. The best models, based DA obtained laser 500 mW 1.0 s, led PS ABS purity up 80 %.

Language: Английский

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Comparison of data augmentation and classification algorithms based on plastic spectroscopy

Analytical Methods, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

We propose a C-GAN-based model for generating plastic spectroscopy data, enhancing classification accuracy by 3%+. Preprocessing improves accuracy, and deep learning excels on large datasets, while SVM RF are reliable smaller datasets.

Language: Английский

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Investigating the “Stickiness” of Waste-Inducing Behaviors Before and During the COVID-19 Pandemic in Massachusetts, USA: A Geospatial and Machine Learning Analysis

Journal of Cleaner Production, Journal Year: 2025, Volume and Issue: unknown, P. 145259 - 145259

Published: March 1, 2025

Language: Английский

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Enhanced spectral signatures with Ag nanoarrays in hyperspectral microscopy for CNN-based microplastics classfication

Frontiers in Chemistry, Journal Year: 2025, Volume and Issue: 13

Published: March 21, 2025

Microplastics are a pervasive pollutant in aquatic ecosystems, raising critical environmental and public health concerns driving the need for advanced detection technologies. Microscopic hyperspectral imaging (micro-HSI), known its ability to simultaneously capture spatial spectral information, has shown promise microplastic analysis. However, widespread application is hindered by limitations such as low signal-to-noise ratios (SNR) reduced sensitivity smaller particles. To address these challenges, this study investigates use of Ag nanoarrays reflective substrates micro-HSI. The localized surface plasmon resonance (LSPR) effect enhances resolution suppressing background reflections isolating reflection bands from interference. This improvement results significantly increased SNR more distinct features. When analyzed using 3D-2D convolutional neural network (3D-2D CNN), integration improved classification accuracy 90.17% 98.98%. These enhancements were further validated through Support Vector Machine (SVM) analyses, demonstrating robustness reliability proposed approach. demonstrates potential combining with CNN models enhance micro-HSI performance, offering novel effective solution precise microplastics advancing chemical analysis, monitoring, related fields.

Language: Английский

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Quantitative analysis of plastic blends based on virtual mid-infrared spectroscopy combined with chemometric methods

Talanta, Journal Year: 2025, Volume and Issue: 292, P. 128006 - 128006

Published: March 24, 2025

Language: Английский

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A Review of Materials for the Removal of Micro- and Nanoplastics from Different Environments

Micro, Journal Year: 2025, Volume and Issue: 5(2), P. 17 - 17

Published: April 9, 2025

Microplastics (MPs) and nanoplastics (NPs) have emerged as persistent environmental pollutants, posing significant ecological human health risks. Their widespread presence in aquatic, terrestrial, atmospheric ecosystems necessitates effective removal strategies. Traditional methods, including filtration, coagulation, sedimentation, demonstrated efficacy for larger MPs but struggle with nanoscale plastics. Advanced techniques, such adsorption, membrane photocatalysis, electrochemical shown promising results, yet challenges remain scalability, cost-effectiveness, impact. Emerging approaches, functionalized magnetic nanoparticles, AI-driven detection, laser-based remediation, present innovative solutions tackling MP NP contamination. This review provides a comprehensive analysis of current emerging strategies, evaluating their efficiency, limitations, future prospects. By identifying key research gaps, this study aims to guide advancements sustainable scalable microplastic technologies, essential mitigating implications.

Language: Английский

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A Plastic Classification Model Based on Simulated Data

Recycling, Journal Year: 2025, Volume and Issue: 10(2), P. 65 - 65

Published: April 8, 2025

Plastic recycling holds significant potential to reduce global carbon emissions. Despite advances in technologies, challenges such as limited data availability, contamination sorted materials, and the complexity of real-world material flows continue hinder progress. This study addresses these issues by introducing a novel approach plastic classification, leveraging simulated spectral reliance on large datasets improve classification accuracy. Using near-infrared spectroscopy deep learning models, framework integrates augmentation techniques simulation augment with synthetic spectra based sample 25 granules. The proposed achieves excellent recall robust balanced accuracy for both binary multi-target polymer minimal input (only 50 per class). Thus, measurement effort is drastically reduced while maintaining an equally high model significantly outperforms conventional unsupervised approaches. By overcoming limitations supervised provides scalable efficient solution plastics recycling.

Language: Английский

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Laser‐Induced Plasma Effects on Bond Breaking in High‐Density Polyethylene Pyrolysis

Advanced Materials Interfaces, Journal Year: 2025, Volume and Issue: unknown

Published: April 16, 2025

Abstract The conventional use of Laser‐Induced Breakdown Spectroscopy (LIBS) for elemental analysis in high‐density polyethylene (HDPE) limits the exploration bond behavior Physics and Chemistry. A suitable combination process parameters, exceeding dissociation threshold, enables LIBS to break HDPE bonds, facilitating laser‐induced pyrolysis. However, understanding post‐breakage, yield formation pathways, role plasma ionization across laser harmonics is crucial. An experiment conducted using three (1064, 532, 266 nm) at 20 Hz with pulse energies ranging from 3 100 mJ. intense Hα peak 656.3 nm suggests breaking due extensive C‐H hydrogen production. Interestingly, lower photon 1.17 2.3 eV 1064 532 broke attributed effects. Numerical models are used calculate temperatures electron density, classifying types. Plasma parameters such as cooling time, rate, energy expansion velocity analyzed. Results show that all contributed breaking: induced field‐induced plasma, favored intermediate multiphoton dominated by photon‐induced plasma. These findings help optimize

Language: Английский

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