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61,005 resultsShowing papers similar to 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
ClearRapidly 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.
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.
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 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.
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.
Assessing the adsorption coefficient of diverse chemicals on polyethylene microplastics through a QSPR approach
Researchers developed a quantitative structure-property relationship (QSPR) model using 3D molecular descriptors to predict the adsorption coefficients of diverse organic chemicals — including persistent, mobile, and toxic compounds — onto polyethylene microplastics, finding that adsorption correlated positively with lipophilicity and negatively with hydroxyl groups and polarity, with strict external validation confirming model reliability.
Assessing the adsorption coefficient of diverse chemicals on polyethylene microplastics through a QSPR approach
Researchers developed a quantitative structure-property relationship (QSPR) model using 3D molecular descriptors to predict the adsorption coefficients of diverse organic chemicals — including persistent, mobile, and toxic compounds — onto polyethylene microplastics, finding that adsorption correlated positively with lipophilicity and negatively with hydroxyl groups and polarity, with strict external validation confirming model reliability.
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.
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.
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.
An Atomic‐Level Perspective on the interactions between Organic Pollutants and PET particles: A Comprehensive Computational Investigation
Using advanced computational methods, researchers studied how organic pollutants interact with PET microplastic particles at the atomic level. The study found that pollutants bind to PET surfaces mainly through weak intermolecular forces, and that the specific chemical structure of both the pollutant and the plastic surface determines how strongly they attach.
Uptake/release of organic contaminants by microplastics: A critical review of influencing factors, mechanistic modeling, and thermodynamic prediction methods
This review critically examines the ability of microplastics to absorb and release organic chemical pollutants, evaluating the factors that influence this process and existing predictive models. Understanding whether microplastics act as significant vectors for pollutants into food chains requires better thermodynamic models that account for real-world complexity.
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.
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-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.
Benzo[a]pyrene and heavy metal ion adsorption on nanoplastics regulated by humic acid: Cooperation/competition mechanisms revealed by molecular dynamics simulations
Researchers used molecular dynamics simulations to investigate how humic acid regulates the adsorption of the carcinogen benzo[a]pyrene and copper ions onto nanoplastics. They found that polystyrene nanoplastics had the highest capacity for adsorbing the carcinogen, while humic acid formed eco-coronas on nanoplastic surfaces that both hindered direct pollutant binding and created new binding sites for metal ions. The study reveals cooperation and competition mechanisms that govern how nanoplastics interact with multiple contaminants in aquatic environments.
Insights into adsorption mechanisms of nitro polycyclic aromatic hydrocarbons on common microplastic particles: Experimental studies and modeling
Researchers investigated how nitro polycyclic aromatic hydrocarbons adsorb onto common microplastics, finding that the process is controlled by chemical adsorption and hydrophobic partitioning, with pollutant hydrophobicity being the dominant factor influencing adsorption capacity.
Understanding the co-adsorption mechanism between nanoplastics and neonicotinoid insecticides from an atomistic perspective
Researchers used quantum chemistry calculations to reveal that nanoplastics made of polyethylene terephthalate, polyethylene, and polystyrene bind neonicotinoid insecticides (imidacloprid and clothianidin) primarily through electrostatic and dispersion forces, accounting for ~90% of complex stability, with implications for how these particles may alter pesticide fate in the environment.
Surface functional group dependent enthalpic and entropic contributions to molecular adsorption on colloidal microplastics
This chemistry study measured how different organic molecules (charged and neutral) stick to the surface of various microplastic particles in water, finding that the plastic's surface chemistry strongly influences the strength and nature of these interactions. The work reveals that both electrostatic attraction and water structure at the plastic surface play a role in determining what contaminants microplastics can carry. This matters because microplastics act as "carriers" for other pollutants, and understanding the binding chemistry helps predict which toxins hitchhike with plastics into ecosystems and organisms.
A Thermodynamic Approach for Assessing the Environmental Exposure of Chemicals Absorbed to Microplastic
Researchers used thermodynamic and multimedia modeling to assess how microplastics influence the transport and bioavailability of persistent toxic substances in marine environments. The study suggests that chemicals with high hydrophobicity may partition to polyethylene microplastic, but overall, microplastic is likely of limited importance as a vector for delivering toxic substances to marine organisms compared to other exposure pathways.
Atmospheric microplastics and nanoplastics as vectors of primary air pollutants - A theoretical study on the polyethylene terephthalate (PET) case
First-principles calculations were used to model the adsorption of primary air pollutants including nitrogen oxides and ozone onto atmospheric PET microplastic and nanoplastic particles, revealing strong binding interactions. The results suggest airborne plastic particles may serve as vectors for transporting and transforming primary air pollutants, with implications for air quality and human inhalation exposure.
Sorption capacity of plastic debris for hydrophobic organic chemicals
This study measured the sorption of a suite of hydrophobic organic chemicals onto different types of marine plastic debris and found that sorption capacity varied widely by polymer type and chemical. The results provide a comparative dataset that helps predict which plastic types are most likely to act as significant vectors for toxic chemical transport in the ocean.
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.
The interaction mechanism of polystyrene microplastics with pharmaceuticals and personal care products
Computational chemistry methods including force field and density functional theory calculations were used to characterize how polystyrene microplastics interact with co-occurring pharmaceuticals and other organic water pollutants, revealing hydrophobic and pi-pi stacking interactions as dominant adsorption mechanisms. The modeling provides mechanistic insight into microplastics' role as vectors for organic contaminant transport in aquatic environments.