We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Self-Consistent Field Modelling of Microplastic Particle Formation and Adsorption of Macromolecular Pollutants
AI summary Read the abstract
Researchers applied a self-consistent field modeling approach to simulate microplastic particle formation and pollutant adsorption, finding that softer microplastic particles with broader interfacial layers adsorb greater amounts of polymer pollutants, with adsorption increasing as pollutant concentration in solution rises.
Accumulation of microplastics (MPs) in aqueous environments poses a serious ecological problem nowadays. MP particles are able to adsorb pollutants of different kinds and to transport them to living organisms, leading to biotoxicity. Hence, investigation of the adsorption of pollutants of different molecular weights onto MP particles is an important task. We employed the numerical Scheutjens–Fleer self-consistent field method to study (i) the formation of MP particles consisting of homopolymer macromolecules and (ii) the adsorption of pollutant homopolymer chains onto the MP particles. Under poor solvent conditions, the polymer macromolecules were shown to form MPs with a constant density inside the particle and with an interfacial layer at its periphery. The size of the MP particles and the thickness of the interfacial layer were controlled by the solvent quality. MP particles were shown to adsorb pollutant polymer chains from the surrounding liquid due to higher compatibility of the MP particle with the pollutant polymer chains as compared to the solvent. The amount of adsorbed polymer pollutant increased with the increase of its concentration in solution. Softer MP particles were shown to adsorb larger amounts of pollutants due to a broader interfacial layer. The conformational characteristics of the adsorbed polymer chains (trains, loops, and tails) were studied in detail.
More Papers Like This
Developing Predictive Models for Carrying Ability of Micro-Plastics towards Organic Pollutants
AI summary Read the abstract
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.
Insights into adsorption mechanisms of nitro polycyclic aromatic hydrocarbons on common microplastic particles: Experimental studies and modeling
AI summary Read the abstract
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.
Effects of Polymer Morphology on Humic Acid Adsorption onto Model Microplastics and Nanoplastics
AI summary Read the abstract
Computer simulations modeled how natural organic material (humic acid) sticks to tiny plastic particles with different surface structures. Smooth, ordered plastic surfaces attracted humic acid through flat contact points, while disordered surfaces created stronger or weaker binding depending on the plastic type, showing that particle surface structure affects how coatings form on microplastics.
Sorption kinetics of metallic and organic contaminants on micro- and nanoplastics: remarkable dependence of the intraparticulate contaminant diffusion coefficient on the particle size and potential role of polymer crystallinity
AI summary Read the abstract
Researchers developed a mathematical model to describe how metallic and organic contaminants bind to micro- and nanoplastics over time, accounting for how particle size affects the process. They found that the rate at which pollutants diffuse into plastic particles depends strongly on particle size, with smaller particles absorbing contaminants much faster. The model provides a more accurate framework for predicting how microplastics act as carriers of environmental pollutants.
Rapidly Predicting Aqueous Adsorption Constants of Organic Pollutants onto Polyethylene Microplastics by Combining Molecular Dynamics Simulations and Machine Learning
AI summary Read the abstract
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.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.