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20 resultsShowing papers similar to Predicting the toxicity of microplastic particles through machine learning models
ClearPredicting 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.
Potential threat of microplastics to humans: toxicity prediction modeling by small data analysis
Researchers developed a toxicity prediction model for microplastics using small data analysis techniques, enabling the anticipation of varying toxic effects depending on microplastic types and compositions found in nature.
Machine learning-driven QSAR models for predicting the cytotoxicity of five common microplastics
Researchers used machine learning to predict the toxicity of five common microplastic types on human lung cells, finding that particle size, plastic type, and exposure concentration were the most important factors determining harm. This computational approach could help assess the health risks of different microplastics more efficiently than traditional lab testing alone.
Machine-Learning-Accelerated Prediction of Water Quality Criteria for Microplastics
Researchers developed a machine learning framework to predict microplastic toxicity in aquatic organisms and derive water quality criteria for five common polymer types. The random forest model outperformed other algorithms, with particle size, density, and aquatic species group accounting for 72% of prediction variability. The study found that polystyrene and PET exhibited the greatest toxicity, and that microplastics were generally more toxic in freshwater than saltwater environments.
[Overview of the Application of Machine Learning for Identification and Environmental Risk Assessment of Microplastics].
This review examines the application of machine learning (ML) methods for identifying microplastics and assessing their environmental risks, covering techniques for improving the accuracy and reliability of microplastic detection across different environmental media. Researchers highlight how ML can systematically analyse pollution characteristics and support ecological risk evaluation of microplastic contamination.
Versatile in silico modelling of microplastics adsorption capacity in aqueous environment based on molecular descriptor and machine learning
Researchers developed machine learning models using molecular descriptors to predict the adsorption capacity of microplastics for organic pollutants in aqueous environments, achieving high accuracy across multiple polymer types and enabling faster environmental risk assessment.
Combining machine learning with meta-analysis for predicting cytotoxicity of micro- and nanoplastics
This meta-analysis used machine learning to predict how toxic different types of micro- and nanoplastics are to cells. By analyzing data from many studies, it identified that particle size, concentration, and exposure time are key factors determining toxicity — smaller particles and longer exposures tend to cause more cell damage.
Transfer learning enables robust prediction of cellular toxicity from environmental micro- and nanoplastics
Researchers developed a transfer learning approach to predict cellular toxicity from micro- and nanoplastics, overcoming the challenge of limited experimental data. By pre-training a model on a large nanoparticle dataset and fine-tuning it on plastic-specific data, they achieved strong predictive accuracy. The tool allows researchers to estimate the toxicity of various plastic particles based on their physical and chemical properties without extensive new experiments.
Unraveling the ecotoxicity of micro(nano)plastics loaded with environmental pollutants using ensemble machine learning.
Researchers developed an ensemble machine learning algorithm to predict the ecotoxicity of micro(nano)plastics loaded with environmental pollutants, addressing a key knowledge gap where most studies examine plastic particles alone. The model revealed that co-pollutant loading substantially amplifies toxicity and that particle characteristics govern outcomes.
Predictive modeling of microplastic adsorption in aquatic environments using advanced machine learning models
Scientists used advanced machine learning models to predict how microplastics interact with and absorb organic pollutants in water. The results showed that microplastics with certain chemical properties attract more toxic compounds, which matters because contaminated microplastics in waterways can concentrate harmful chemicals that may eventually reach humans through drinking water and seafood.
Prediction of the cytotoxicity of micro- and nanoplastics using machine learning combined with literature data mining
Researchers developed a machine learning framework using decision tree ensemble classifiers trained on 1,824 literature-derived data points to predict the cytotoxicity of micro- and nanoplastics based on nine physicochemical and experimental features. The full-feature model achieved 95% accuracy with 86% recall and precision, and feature selection identified six key predictors, providing a tool to guide experimental design and harmonize MNP toxicity research.
Machine Learning to Predict the Adsorption Capacity of Microplastics
Researchers developed machine learning models to predict the adsorption capacity of microplastics for chemical pollutants, providing a computational tool to better understand how microplastics act as vectors for contaminant dispersal in aquatic environments.
Machine learning-driven QSAR models for predicting the mixture toxicity of nanoparticles
Researchers used machine learning to predict how toxic different mixtures of metal nanoparticles are to bacteria. Their models outperformed traditional methods at predicting combined toxicity effects. While focused on engineered nanoparticles rather than microplastics, the computational approach could be adapted to predict health risks from the complex mixtures of nano-sized pollutants people encounter.
Prediction of Settling Velocity of Microplastics by Multiple Machine-Learning Methods
Researchers developed machine learning models to predict the settling velocity of microplastics in water, using particle shape, size, and density as inputs. The models outperformed traditional empirical equations, providing a more accurate tool for modeling microplastic transport and sedimentation.
Efficient Prediction of Microplastic Counts from Mass Measurements
Scientists developed machine learning models to estimate the number of microplastic particles from aggregate weight measurements, potentially offering a faster and cheaper alternative to manual counting. Efficient quantification methods are critical for large-scale monitoring of microplastic contamination in environmental samples.
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.
Towards A universal settling model for microplastics with diverse shapes: Machine learning breaking morphological barriers
Researchers developed a machine learning model to predict the settling velocity of microplastics across different shapes, including fragments, films, and fibers. Unlike existing models limited to specific morphologies, this approach works universally across all three particle types. The study provides a more reliable tool for modeling how microplastics move through and deposit in aquatic environments.
Predicting aqueous sorption of organic pollutants on microplastics with machine learning
Researchers developed machine learning models to predict how organic pollutants bind to microplastics in water, using data from 475 published experiments. The models outperformed traditional approaches by accounting for properties of both the microplastics and the pollutants simultaneously. The study provides a more universal tool for understanding how microplastics can transport and concentrate harmful chemicals in freshwater systems.
Machine Learning Method for Microplastic Identification Using a Combination of Machine Learning and Raman Spectroscopy
Researchers developed a machine learning method for identifying microplastics using a combination of multiple spectroscopic techniques, improving classification accuracy beyond single-method approaches and enabling automated polymer identification.
Quantifying the influence of micro and nanoplastics characteristics on cytotoxicity in caco-2 cells through machine learning modelling.
This systematic review uses machine learning to determine which properties of micro and nanoplastics drive toxicity in human intestinal cell models. The findings reveal that smaller particles and higher concentrations cause more cell damage, which is important for understanding how the microplastics we swallow in food and water might harm our gut lining.