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61,005 resultsShowing papers similar to Developing Predictive Models for Carrying Ability of Micro-Plastics towards Organic Pollutants
ClearQSPR 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.
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.
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.
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.
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.
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.
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.
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.
Adsorption behavior of organic pollutants on microplastics
This review summarizes the main mechanisms by which microplastics adsorb organic pollutants, including hydrophobic interactions, electrostatic forces, and hydrogen bonding. Researchers found that particle size, surface area, aging, and environmental factors like pH and temperature significantly influence how much pollution microplastics can carry. The study highlights the need for more field-based research to understand how microplastics behave as pollutant carriers in real environmental conditions.
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.
The chemical behaviors of microplastics in marine environment: A review
This review summarized interactions between microplastics and organic pollutants and metals in the marine environment, covering sorption behavior across polymer types, the role of degradation in altering sorption capacity, and global monitoring data on pollutant concentrations on marine plastics. The authors conclude that microplastic type, pollutant properties, and environmental conditions all strongly influence chemical accumulation on plastic surfaces.
Microplastics as a vector of hydrophobic contaminants: Importance of hydrophobic additives
This paper examines the role of hydrophobicity in determining whether organic pollutants sorbed to microplastics pose a meaningful additional risk beyond direct water exposure. The authors argue that for most scenarios, the contribution of microplastics to total pollutant exposure is smaller than commonly assumed and depends heavily on the properties of the specific chemical and polymer.
Strong influence of surfactants on virgin hydrophobic microplastics adsorbing ionic organic pollutants
Researchers found that surfactants cause hydrophobic microplastics to adsorb ionic organic pollutants at much higher rates than previously recognized, revealing that the typical assumption of minimal interaction between hydrophobic plastics and hydrophilic contaminants underestimates real-world pollutant uptake. The findings indicate that surfactant ubiquity in environmental waters substantially alters microplastic pollutant-carrying capacity.
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.
Transport of persistent organic pollutants: Another effect of microplastic pollution?
This review examines how microplastics act as vectors for persistent organic pollutants (POPs) in aquatic environments, covering the physical and chemical factors governing pollutant adsorption and desorption. The authors discuss how interactions between microplastics and POPs vary with polymer type, particle properties, and environmental conditions, and when these interactions may result in toxic effects on aquatic organisms.
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.
Microplastics and organic contaminants: Investigation of the sorption process on different polymer types
Researchers investigated sorption of organic contaminants onto microplastics collected from environmental samples, finding that real-world MPs had different sorption capacities than laboratory-prepared particles due to surface aging, biofouling, and co-sorption of natural organic matter.
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.
Interactions between microplastics and organic compounds in aquatic environments: A mini review
Researchers reviewed the mechanisms of interaction between microplastics and organic compounds in aquatic environments, examining factors related to the plastics themselves, the organic compounds, and environmental conditions. The study found that properties like crystallinity, surface area, and weathering state of microplastics all influence how they adsorb and transport organic pollutants, with implications for environmental and health risk assessments.
Adsorption of micropollutants onto realistic microplastics: Role of microplastic nature, size, age, and NOM fouling
Researchers measured adsorption of diclofenac and metronidazole onto four realistic microplastic types under varying size, aging, and natural organic matter conditions, finding that aged MPs with smaller size and without NOM fouling showed the highest pollutant adsorption capacity.
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.