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Microplastics

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Machine learning outperforms humans in microplastic characterization and reveals human labelling errors in FTIR data.

Journal of hazardous materials
Microplastics are ubiquitous and appear to be harmful, however, the full extent to which these inflict harm has not been fully elucidated. Analysing environmental sample data is challenging, as the complexity in real data makes both automated and man...

Rapid detection of microplastics in chicken feed based on near infrared spectroscopy and machine learning algorithm.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
The main objective of this study was to evaluate the potential of near infrared (NIR) spectroscopy and machine learning in detecting microplastics (MPs) in chicken feed. The application of machine learning techniques in building optimal classificatio...

Unraveling Microplastic Effects on Gut Microbiota across Various Animals Using Machine Learning.

ACS nano
Microplastics, rapidly expanding and durable pollutant, have been shown to significantly impact gut microbiota across a spectrum of animal species. However, comprehensive analyses comparing microplastic effects on gut microbiota among these species a...

Towards A universal settling model for microplastics with diverse shapes: Machine learning breaking morphological barriers.

Water research
Accurately predicting the settling velocity of microplastics in aquatic environments is a prerequisite for reliably modeling their transport processes. An increasing number of settling models have been proposed for microplastics with fragmented, film...

Machine learning-based prediction and model interpretability analysis for algal growth affected by microplastics.

The Science of the total environment
Microplastics (MPs), the plastic debris smaller than 5 mm, are ubiquitous in waterbodies and have been shown to be toxic to aquatic organisms, especially to microalgae. The aim of this study is to use machine learning models to predict the effects of...

Machine learning based workflow for (micro)plastic spectral reconstruction and classification.

Chemosphere
With the advancement of artificial intelligence, it is foreseeable that computer-assisted identification of microplastics (MPs) will become increasingly widespread. Therefore, exploring a machine learning-based workflow to facilitate the identificati...

Machine learning-integrated droplet microfluidic system for accurate quantification and classification of microplastics.

Water research
Microplastic (MP) pollution poses serious environmental and public health concerns, requiring efficient detection methods. Conventional techniques have the limitations of labor-intensive workflows and complex instrumentation, hindering rapid on-site ...

Interpretable machine learning reveals transport of aged microplastics in porous media: Multiple factors co-effect.

Water research
Microplastics (MPs) easily migrate into deeper soil layers, posing potential risks to subterranean habitats and groundwater. However, the mechanisms governing the vertical migration of MPs in soil, particularly aged MPs, remain unclear. In this study...

Advancements and challenges in microplastic detection and risk assessment: Integrating AI and standardized methods.

Marine pollution bulletin
Microplastics (MPs) pose significant threats to ecosystems and human health due to their persistence and widespread distribution. This paper provides a comprehensive review of sampling methods for MPs in aquatic environments, soils, and biological sa...

Deep learning-driven behavioral analysis reveals adaptive responses in Drosophila offspring after long-term parental microplastic exposure.

Journal of environmental management
Microplastics are widely distributed in the environment and pose potential hazards to organisms. However, our understanding of the transgenerational effects of microplastics on terrestrial organisms remains limited. In this study, we focused on the m...