We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Papers
61,005 resultsShowing papers similar to Prediction of organic compounds adsorbed by polyethylene and chlorinated polyethylene microplastics in freshwater using QSAR
ClearAssessing 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.
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 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.
Modelling of the adsorption of chlorinated phenols on polyethylene and polyethylene terephthalate microplastic
This study modeled how chlorinated phenols — toxic water pollutants — stick to polyethylene and polyethylene terephthalate microplastics sourced from personal care products. The findings suggest microplastics can act as carriers for harmful chemicals in aquatic environments, potentially concentrating toxins and delivering them to organisms that ingest the particles.
A QSAR prediction model for adsorption of organic contaminants on microplastics: Dubinin-Astakhov plus linear solvation energy relationships
Researchers combined the Dubinin-Astakhov isotherm model with linear solvation energy relationships to build a QSAR model predicting the adsorption of pharmaceuticals and personal care products onto various microplastic polymer types.
Adsorption mechanisms of chlorobenzenes and trifluralin on primary polyethylene microplastics in the aquatic environment
Researchers investigated the adsorption mechanisms of six priority chlorinated and aromatic pollutants (including trichlorobenzenes and trifluralin) onto primary polyethylene microplastics, revealing how plastic type, surface area, and compound properties govern contaminant uptake in aqueous 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.
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.
Adsorption and Desorption Behaviour of Polychlorinated Biphenyls onto Microplastics’ Surfaces in Water/Sediment Systems
Researchers evaluated the adsorption and desorption behavior of polychlorinated biphenyls (PCBs) onto polystyrene, polyethylene, and polyethylene terephthalate microplastics of varying sizes in marine water/sediment systems. Results showed that polymer type and particle size influenced PCB binding capacity, with microplastics acting as potential vectors for transferring persistent organic pollutants to marine biota through the food chain.
Sorption of pesticides onto polyethylene microplastics in different aqueous matrices
This thesis examined how pesticides adsorb onto polyethylene microplastics in different aqueous matrices, finding that water chemistry significantly affects the binding behavior and potential for microplastics to carry agricultural chemicals.
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.
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.
A combined experimental and modeling study to evaluate pH-dependent sorption of polar and non-polar compounds to polyethylene and polystyrene microplastics
A combined experimental and modeling study assessed how pH affects the sorption of both polar and non-polar compounds to polyethylene and polystyrene microplastics, finding that pH significantly influenced sorption of ionizable pollutants. The results improve predictions of how microplastics accumulate and transport contaminants under varying environmental conditions.
Significance of Chlorinated Phenols Adsorption on Plastics and Bioplastics during Water Treatment
Microplastics and bioplastics both adsorb toxic chlorinated phenol compounds from freshwater, with adsorption rates depending on the plastic type and contaminant. This finding shows that even bioplastic alternatives to conventional plastic can act as carriers for toxic chemicals in aquatic environments.
Adsorption of fluoranthene and phenanthrene by virgin and weathered polyethylene microplastics in freshwaters
Researchers examined how virgin and weathered polyethylene microplastics adsorb fluoranthene and phenanthrene in freshwater, finding that weathering significantly increased adsorption capacity and that water chemistry influenced contaminant uptake.
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.
Sorption and dissipation of current-use pesticides and personal-care products on high-density polyethylene microplastics in seawater
Researchers characterized how three pesticides and three personal care products sorb onto high-density polyethylene microplastics in seawater. They found that more hydrophobic compounds accumulated more readily on the plastic, and that significant desorption (over 30%) occurred within 24 hours, especially at higher contaminant concentrations. The study confirms that microplastics can act as both carriers and releasers of chemical pollutants in marine environments.
Response surface methodology for modeling the adsorptive uptake of phenol from aqueous solution using adsorbent polyethylene terephthalate microplastics
Researchers used response surface methodology to model the adsorption of phenol from water using pristine, modified, and aged polyethylene terephthalate (PET) microplastics, finding that microplastics can act as vectors for organic pollutants in aquatic environments.
Adsorption of neutral organic compounds on polar and nonpolar microplastics: Prediction and insight into mechanisms based on pp-LFERs
Researchers measured adsorption of 18 neutral organic compounds on polar and nonpolar microplastics and found that polar microplastics such as polybutylene succinate and polycaprolactone showed greater adsorption capacity than nonpolar types, with hydrophobic partitioning dominating on all plastics and polar interactions providing additional uptake on polar polymers.
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.
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.
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.
Adsorption of PFAS onto secondary microplastics: A mechanistic study
Researchers investigated how PFAS (per- and polyfluoroalkyl substances) adsorb onto secondary microplastics under different water chemistry conditions. Results showed that PFAS adsorption depended on both the chemical structure of the PFAS compound and the ionic composition of the water. These findings help explain how microplastics in real-world aquatic environments can concentrate and transport PFAS, a group of persistent health-relevant pollutants.