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61,005 resultsShowing papers similar to Machine Learning-Driven Prediction of Organic Compound Adsorption onto Microplastics in Freshwater
ClearPredicting 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 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 Prediction of Adsorption Behavior of Xenobiotics on Microplastics under Different Environmental Conditions
Researchers developed a machine learning model to predict how different xenobiotic chemicals adsorb onto microplastics under varying environmental conditions, providing a computational tool to assess microplastics as vectors for pollutant transport without requiring extensive laboratory experiments.
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.
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.
Machine Learning to Predict the Adsorption Capacity for Microplastics
Researchers developed and compared three machine learning models — random forest, support vector machine, and artificial neural network — to predict microplastic/water partition coefficients (log Kd) for chemical pollutant adsorption, addressing the limited experimental data available on microplastic adsorption capacity in aquatic environments.
Artificial Intelligence-Powered Prediction of Microplastic–Pollutant Adsorption Coefficients Enables Scalable Risk Prioritization in Aquatic Environments
Researchers developed an AI-powered neural network called MPAP to predict how microplastics adsorb hazardous pollutants in aquatic environments. Trained on over 1,100 adsorption records covering 403 compounds and six microplastic types, the model integrates molecular fingerprints, microplastic features, and water chemistry parameters to enable scalable risk prioritization for microplastic-pollutant interactions.
Projecting the sorption capacity of heavy metal ions onto microplastics in global aquatic environments using artificial neural networks
Machine learning models accurately predicted how much heavy metals like cadmium, lead, and zinc would adsorb onto microplastics in rivers, lakes, and oceans, based on factors like metal concentration and water salinity. Aged microplastics showed higher metal sorption capacity than virgin plastics, and predicted values matched real-world field measurements.
Rapidly Predicting Aqueous Adsorption Constants of Organic Pollutants onto Polyethylene Microplastics by Combining Molecular Dynamics Simulations and Machine Learning
Researchers developed a computational method combining molecular simulations with machine learning to rapidly predict how organic pollutants adsorb onto polyethylene microplastics in water. The approach accurately predicted adsorption behavior across different conditions including particle size, water salinity, and pH without requiring time-consuming laboratory experiments. The tool could help environmental scientists more quickly assess how microplastics interact with and transport chemical contaminants in aquatic environments.
Predicting adsorption capacity of pharmaceuticals and personal care products on long-term aged microplastics using machine learning
Researchers found that long-term aged microplastics adsorb 7-13 times more pharmaceuticals and personal care products than pristine microplastics, and developed machine learning models using infrared spectroscopy that predicted adsorption capacity with over 96% accuracy.
Adsorption behavior and mechanism of heavy metals onto microplastics: A meta-analysis assisted by machine learning
A machine learning-assisted meta-analysis of 3,340 records found that polyamide microplastics had the highest heavy metal adsorption capacity due to their C=O and N-H surface groups, while PVC showed the strongest adsorption strength from its halogen atoms. Lead was the most readily adsorbed metal, and random forest modeling identified heavy metal concentration, microplastic concentration, specific surface area, and pH as the dominant factors governing adsorption.
Fe(III) Adsorption onto Microplastics in Aquatic Environments: Interaction Mechanism, Influencing Factors, and Adsorption Capacity Prediction
This study investigated how iron (Fe III) attaches to different types of microplastics in both freshwater and saltwater, finding that aged and weathered microplastics absorb significantly more iron than new ones. Machine learning models were used to predict how much iron different microplastics can carry under various conditions. This is relevant to health because iron-loaded microplastics may be more toxic and more easily absorbed by organisms in the food chain.
Investigating the adsorption of organic compounds onto microplastics via experimental, simulation, and prediction methods
This review systematically examined experimental, simulation, and predictive modeling approaches for studying the adsorption of organic compounds onto microplastics, synthesizing findings on how molecular interactions, environmental conditions, and plastic aging affect microplastic vector behavior for organic pollutants.
Developing Predictive Models for Carrying Ability of Micro-Plastics towards Organic Pollutants
Researchers developed predictive models for microplastic adsorption of organic pollutants, using quantitative structure-activity relationships to estimate how different polymer types and pollutant properties influence sorption capacity.
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.
Interactions of microplastics with contaminants in freshwater systems: a review of characteristics, bioaccessibility, and environmental factors affecting sorption
This review examined how microplastics act as vectors for environmental contaminants in freshwater systems, analyzing the characteristics, bioaccessibility, and environmental factors that influence pollutant sorption onto plastic particles and their potential transfer to organisms including humans.
Elucidating microplastic adsorption mechanisms in biomass composite materials through interpretable machine learning
Researchers used interpretable machine learning to study how biomass composite materials adsorb microplastics from water. They found that initial microplastic concentration and surface electrical potential were the most important factors determining adsorption effectiveness. The study demonstrates that data-driven approaches can help design more efficient and sustainable materials for removing microplastics from contaminated water.
Prediction of organic compounds adsorbed by polyethylene and chlorinated polyethylene microplastics in freshwater using QSAR
Researchers used QSAR modeling to predict the adsorption behavior of 13 organic compounds onto polyethylene and chlorinated polyethylene microplastics under freshwater conditions, finding that most chemicals exhibited higher adsorption to chlorinated polyethylene than to standard polyethylene.
Current applications and future impact of machine learning in emerging contaminants: A review
This review examines how machine learning is being applied to emerging contaminant research including microplastics, covering identification, environmental behavior prediction, bioeffect assessment, and removal optimization of these pollutants.
[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.
Assessment of machine learning-based methods predictive suitability for migration pollutants from microplastics degradation
Researchers assessed the usefulness of machine learning methods for predicting the migration of chemical pollutants from microplastics. The study found that artificial neural networks and support vector methods showed strong potential for modeling and predicting the leaching of plasticizers and other contaminants, which could reduce the need for extensive laboratory analyses.
In-situ quantitative monitoring the organic contaminants uptake onto suspended microplastics in aquatic environments
Researchers developed an in-situ quantitative method for monitoring how organic contaminants adsorb onto suspended microplastics in aquatic environments, addressing the challenge of measuring weak and reversible interactions that form organic coronas on plastic particles.
Data-driven machine learning modeling reveals the impact of micro/nanoplastics on microalgae and their key underlying mechanisms
Researchers used machine learning to predict how micro- and nanoplastics affect freshwater algae, training models on a decade of published experimental data. The best-performing model identified plastic concentration, exposure time, and particle size as the most important factors determining toxicity. The study offers a data-driven framework that could reduce the need for time-consuming laboratory experiments when assessing microplastic risks to aquatic organisms.
Machine learning approaches for predicting microplastic pollution in peatland areas
Researchers used machine learning models to predict microplastic quantities in peatland sediments in Vietnam from easily measurable environmental parameters. The study found that pH, total organic carbon, and salinity were the most influential factors, and that Least-Square Support Vector Machines and Random Forest models could effectively predict microplastic contamination levels.