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61,005 resultsShowing papers similar to Assessing the adsorption coefficient of diverse chemicals on polyethylene microplastics through a QSPR approach
ClearAssessing 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.
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.
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.
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.
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.
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.
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.
Adsorption of naphthalene and its derivatives onto high-density polyethylene microplastic: Computational, isotherm, thermodynamic, and kinetic study
Researchers investigated how naphthalene and its methyl and hydroxyl derivatives adsorb onto high-density polyethylene microplastics, finding that functional group type significantly influences adsorption behavior through different thermodynamic and kinetic mechanisms.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Surface functional groups determine adsorption of pharmaceuticals and personal care products on polypropylene microplastics
Researchers found that surface functional groups on aged polypropylene microplastics determined their adsorption capacity for pharmaceuticals and personal care products, with aged plastic showing much higher pollutant uptake than fresh plastic due to weathering-induced surface changes.
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.
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.
Characterization of sorption properties of high-density polyethylene using the poly-parameter linearfree-energy relationships
Researchers characterized the sorption properties of high-density polyethylene (HDPE) water pipe material using a poly-parameter linear free-energy relationship (ppLFER) model, providing a framework that predicts how organic pollutants partition onto HDPE microplastics more accurately than simpler single-parameter approaches.
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.
Rapid and high-throughput analysis of PAHs and pesticides adsorbed on microplastics using SPME-MS through a microfluidic open interface coupled to liquid electron ionization mass spectrometry
Researchers developed a rapid, low-waste analytical method to measure how well common pesticides and industrial chemicals stick to microplastic particles in water. They found that plastic type and chemical structure both influence adsorption strength, with the pesticide chlorpyrifos clinging especially tightly to polyethylene — a concern given its known toxicity.