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61,005 resultsShowing papers similar to Rapidly Predicting Aqueous Adsorption Constants of Organic Pollutants onto Polyethylene Microplastics by Combining Molecular Dynamics Simulations and Machine Learning
ClearVersatile 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 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.
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-Driven Prediction of Organic Compound Adsorption onto Microplastics in Freshwater
Seven machine learning algorithms were trained on 173 published measurements to predict how strongly organic contaminants adsorb onto different types of microplastics in freshwater. Accurate adsorption predictions are essential for assessing environmental risk, because microplastics that strongly bind pollutants become vectors that concentrate and transport toxic chemicals through aquatic food webs.
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.
Adsorption in Action: Molecular Dynamics as a Tool to Study Adsorption at the Surface of Fine Plastic Particles in Aquatic Environments
Researchers used molecular dynamics simulations to study how pollutants attach to the surface of microscopic plastic particles in water at the atomic level. They found that the type of plastic material and the specific pollutant involved significantly influenced the strength and nature of the adsorption process. The study demonstrates that computer simulations can complement traditional lab experiments to better understand how microplastics interact with contaminants in aquatic environments.
In Silico Models for Predicting Adsorption of Organic Pollutants on Atmospheric Nanoplastics by Combining Grand Canonical Monte Carlo/Density Functional Theory and Quantitative Structure Activity Relationship Approach
This computational study used molecular simulations and machine learning to build predictive models for how 48 different organic pollutants — including flame retardants — adsorb onto 12 types of atmospheric nanoplastics. The resulting models revealed that van der Waals and electrostatic forces dominate these interactions, and a single multi-dimensional model can rapidly screen thousands of pollutant-nanoplastic combinations at once. Accurate adsorption predictions are crucial for assessing how nanoplastics carry toxic chemicals through the atmosphere and into ecosystems or human lungs.
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.
Machine Learning-Optimized Microplastic Adsorption Kinetics in Marine Environments with Edge Computing
Researchers developed a machine learning-optimized framework using edge computing and a population balance equation model to predict adsorption dynamics of microplastics toward persistent organic pollutants in marine environments, enabling distributed real-time monitoring of microplastic-contaminant interactions.
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.
QSPR and q-RASPR predictions of the adsorption capacity of polyethylene, polypropylene and polystyrene microplastics for various organic pollutants in diverse aqueous environments
Quantitative structure-property relationship (QSPR) and q-RASPR models were developed using experimental adsorption data to predict how organic pollutants adsorb onto polyethylene, polypropylene, and polystyrene microplastics in different aqueous environments. The models provide computational tools to assess microplastic-contaminant interactions without extensive laboratory testing.
Assessing the adsorption coefficient of diverse chemicals on polyethylene microplastics through a QSPR approach
Researchers developed a quantitative structure-property relationship (QSPR) model to predict the adsorption coefficients of diverse organic chemicals onto polyethylene microplastics in water, compiling a larger and more rigorously screened dataset than prior models and applying applicability domain assessments to improve reliability.
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.
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.
Insights into the adsorption of ibuprofen onto polyethylene microplastics using molecular dynamic simulation
Researchers used molecular dynamics simulations combined with laboratory experiments to study how ibuprofen adsorbs onto polyethylene microplastics in water. The study found that van der Waals forces dominate the interaction, with microplastics achieving an adsorption capacity of 0.41 mg/g for ibuprofen, suggesting that microplastics can act as carriers for pharmaceutical pollutants in aquatic environments.
Molecular level insight into the different interaction intensity between microplastics and aromatic hydrocarbon in pure water and seawater
Researchers found that microplastics have stronger affinity for aromatic hydrocarbons in seawater than in pure water, with molecular dynamics simulations and density functional theory revealing that salinity-induced changes in surface characteristics and ionic interactions drive enhanced pollutant sorption.
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.
QSPR models for predicting the adsorption capacity for microplastics of polyethylene, polypropylene and polystyrene
Researchers developed quantitative structure-property relationship (QSPR) models to predict the adsorption capacity of polyethylene, polypropylene, and polystyrene microplastics for organic pollutants, providing computational tools to estimate microplastic-mediated contaminant transport without requiring extensive experimental measurements for each compound.
Machine learning algorithm for modeling oxytetracycline adsorption kinetics on microplastics in marine environments
Microplastics in the ocean don't just float — they actively adsorb and concentrate other pollutants like antibiotics, potentially acting as vectors that deliver these chemicals into marine organisms. This study built a mathematical model of how the antibiotic oxytetracycline adsorbs onto microplastics in seawater, and used quantum machine learning to dramatically speed up the computational modeling of these complex, multi-component interactions. Faster and more accurate models of microplastic-pollutant binding behavior could improve our ability to assess the true toxicological risk that plastic-chemical combinations pose to marine life.
A new modeling approach for microplastic drag and settling velocity
Researchers developed a novel machine learning-based modelling framework to predict drag coefficients and settling velocities for microplastics of varying shapes (1D, 2D, 3D, and mixed) in aquatic environments. The framework achieved coefficient of determination values of 0.86-0.95 for drag models, outperforming traditional theoretical and data-fitting approaches in both speed and accuracy.
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.
Hybrid Ensemble Machine Learning Models with SHAP Explainability for Robust Prediction of Suspended Particle Attachment Efficiency in Complex Environmental Systems
Scientists developed a new computer model that can better predict how tiny particles—including microplastics—clump together and move through the environment. The model found that salt levels in water are the main factor controlling how single particles stick together, while electrical charge differences matter most when different types of particles interact. This research could help us better understand how microplastics and other harmful particles spread through water systems and potentially affect human health.