We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Environmental degradation of consumer plastics into microplastics and nanoplastics and their classification using machine learning
Summary
Researchers studied the environmental degradation of consumer plastics — LDPE shopping bags, PET water bottles, and take-food trays — under UV radiation, abrasion, atmospheric oxidation, and freeze-thaw cycles, then trained and applied machine learning classifiers to identify and categorize the resulting microplastic and nanoplastic particles.
Consumer plastics such as plastic shopping bags, water bottles and take-food trays can be sources of secondary microplastics and nanoplastics (MNPs) when they undergo complex environmental degradation. Under the influence of UV radiation, abrasion, atmospheric oxidation and freeze-thaw cycles, bulk plastics degrade into smaller particles with significantly increased transportation in the aquatic environment. In this work, we studied the degradation of three classes of consumer plastics, namely, plastic shopping bags (low density polyethylene), water bottles (polyethylene terephthalate) and take-out food trays (polystyrene) under simulated environmental condition. The first MNPs emerged after at least 4 weeks of simulated environmental degradation. We characterized the transformation of the surface chemistry of plastics using Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS). Our results show that environmental degradation has a significant effect on alteration of the surface chemistry of polymers by inducing a plethora of oxygen containing groups. The morphology of the MNPs and post-degradation bulk plastics were also studied by scanning electron microscopy (SEM). A dataset of 101 annotated micrographs was generated for comprehensive data analysis. The dataset was structured to accommodate various applications, including MNP detection, categorization by polymer type, and classification based on size. The images were manually annotated, including masks, bounding boxes, area, diameter, and category information for each particle. The dataset exhibited diverse particle density and size distributions, with varying concentrations of particles across different images. The dataset's statistics encompassed the distribution of the number of particles in images and the distribution of particles according to particle diameter. Cutting-edge algorithms, such as U-net for semantic segmentation and Mask-RCNN for instance detection, were applied to the dataset for validation. These experiments highlight the dataset's value in addressing open problems in MNP and particle detection, particularly in scenarios with overlapping particles, crowded scenes, and clumped particles. Also see: https://micro2024.sciencesconf.org/559714/document
Sign in to start a discussion.
More Papers Like This
Environmental degradation of consumer plastics into microplastics and nanoplastics and their classification using machine learning
Researchers studied the environmental degradation of three common consumer plastics — low-density polyethylene shopping bags, polyethylene terephthalate water bottles, and take-food trays — under UV radiation, abrasion, atmospheric oxidation, and freeze-thaw cycles, then applied machine learning algorithms to classify the resulting microplastic and nanoplastic particles.
Analysis of microplastics and nanoplastics emerged from polyethylene bags and polyethylene terephthalate bottles by an artificial intelligence-enabled tool
Researchers subjected polyethylene bags and PET bottles to 12 weeks of simulated mechanical and photodegradation and used an AI-enabled annotation tool (FastSAM) to characterize the resulting micro- and nanoplastic particles by count, size, and morphology, finding PET generated far more particles than PE.
Predicting the toxicity of microplastic particles through machine learning models
Researchers applied machine learning models to predict the toxicity of microplastic particles from their physical and chemical properties, addressing the challenge that microplastics lack the standardized identifiers used for chemical hazard classification. The models successfully predicted toxicity outcomes from particle descriptors, offering a framework for hazard screening of the diverse and complex microplastic contaminant class.
Detection of Microplastics Using Machine Learning
Researchers reviewed and demonstrated machine learning approaches for detecting and classifying microplastics in environmental samples, finding that automated image analysis and spectral classification methods can improve the speed and accuracy of microplastic monitoring compared to manual methods.
Predicting the toxicity of microplastic particles through machine learning models
Researchers developed machine learning models to predict microplastic particle toxicity from physical and chemical descriptors, addressing the classification challenge posed by the enormous diversity of particle types that cannot be characterized using conventional chemical hazard methods. The models provided accurate toxicity predictions across diverse microplastic types, offering a practical screening tool for the field.