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Papers
61,005 resultsShowing papers similar to Towards A universal settling model for microplastics with diverse shapes: Machine learning breaking morphological barriers
ClearMachine learning-based prediction for settling velocity of microplastics with various shapes
Researchers developed machine learning models to predict the settling velocity of microplastics based on their size, density, and shape. They classified microplastic shapes into fiber, film, and fragment categories and identified the optimal shape parameter for each, achieving significantly better prediction accuracy than existing theoretical models. The study reveals that particle size has the greatest influence on settling velocity, which is important for understanding how microplastics move and distribute in water environments.
Prediction of Settling Velocity of Microplastics by Multiple Machine-Learning Methods
Researchers developed machine learning models to predict the settling velocity of microplastics in water, using particle shape, size, and density as inputs. The models outperformed traditional empirical equations, providing a more accurate tool for modeling microplastic transport and sedimentation.
A new modeling approach for microplastic drag and settling velocity
Researchers developed a novel machine learning-based modelling framework to predict drag coefficients and settling velocities for microplastics of varying shapes (1D, 2D, 3D, and mixed) in aquatic environments. The framework achieved coefficient of determination values of 0.86-0.95 for drag models, outperforming traditional theoretical and data-fitting approaches in both speed and accuracy.
Towards better predicting the settling velocity of film-shaped microplastics based on experiment and simulation data
Researchers combined experimental and simulation data to better predict how film-shaped microplastics settle through water, since most existing models are based on spherical particles. They found that the particle definition approach was more suitable than equivalent spherical diameter for characterizing flat, irregular microplastics. The improved settling velocity predictions could help scientists better understand how film-shaped microplastics travel and accumulate in aquatic environments.
Settling velocity of microplastic particles having regular and irregular shapes
Researchers measured how quickly microplastic particles of various shapes settle through water, testing 66 different particle types including spheres, cylinders, fibers, and irregular fragments. They found that particle shape significantly affects settling speed, with fibers and flat shapes sinking more slowly than spheres of the same size. The study provides new equations for predicting where microplastics end up in oceans and waterways based on their shape.
Dynamic prediction of large spherical and cylindrical microplastic deposition: a machine learning approach for transport and deposition
Researchers developed a machine learning model combined with dimensionless analysis to predict the deposition patterns of spherical and cylindrical microplastics in aquatic environments. The model accounts for varied flow conditions and particle shapes to improve predictions of where microplastics settle in water bodies. The study offers a practical tool for pollution monitoring efforts by helping predict microplastic accumulation hotspots in rivers and oceans.
Improved Settling Velocity for Microplastic Fibers: A New Shape-Dependent Drag Model
A new shape-dependent drag model was developed to improve the accuracy of settling velocity predictions for microplastic fibers, addressing a major limitation of existing drag models that significantly underpredict fiber settling in aquatic environments.
A new model for the terminal settling velocity of microplastics
A new empirical model for the terminal settling velocity of microplastics was developed and validated using 1,343 experimental measurements covering a range of particle shapes and materials. The model improves predictions of microplastic sedimentation rates, which are critical for understanding how plastic particles are transported and deposited in water bodies.
Modeling Microplastic Transport in the Marine Environment: Testing Empirical Models of Particle Terminal Sinking Velocity for Irregularly Shaped Particles
Researchers tested multiple drag models for predicting the terminal settling velocity of irregularly shaped microplastic particles in seawater, identifying three high-precision models and demonstrating that settling velocity is largely stable across ocean depths and independent of initial particle velocity, improving the accuracy of marine microplastic transport simulations.
Identification and velocity measurement of microplastics based on machine learning
Researchers developed a machine learning framework to simultaneously track multiple microplastics in water and measure their terminal settling velocities, capturing particle interaction dynamics that conventional single-particle tracking methods miss.
Settling Velocities of Small Microplastic Fragments and Fibers
Researchers precisely measured the settling speeds of over 4,000 small microplastic particles in water and found that existing prediction models designed for larger microplastics do not work well for these tiny fragments and fibers. The settling speed depends on each particle's size, density, and shape, with the smallest particles sinking extremely slowly. Understanding how quickly microplastics settle in water is important because it determines how far they travel and how long they remain available to be consumed by aquatic organisms that humans may eventually eat.
A settling velocity formula for irregular shaped microplastic fragments based on new shape factor: Influence of secondary motions
Researchers developed a new shape factor for irregular microplastic fragments and derived a settling velocity formula based on it, using numerical modeling to show that fragment shape governs whether particles sink stably or oscillate — providing more accurate predictions of microplastic transport in rivers and lakes than existing methods.
Modeling the settling and resuspension of microplastics in rivers: Effect of particle properties and flow conditions
Researchers developed a mathematical model to simulate how microplastics of different shapes settle and resuspend in rivers, moving beyond the common assumption that all particles are spherical. They found that turbulence has a complex effect, sometimes keeping particles suspended longer and sometimes accelerating their settling, depending on flow conditions. The model reveals that particle shape significantly influences where microplastics end up in river systems.
Settling velocities of microplastics with different shapes in sediment-water mixtures
Researchers studied how the shape of microplastic particles affects how quickly they sink in water containing suspended sediment. They found that fibers and films settle much more slowly than fragments and pellets, and that sediment in the water significantly slows the settling of all microplastic types. These findings are important for predicting where microplastics accumulate in lakes, rivers, and oceans.
Quantifying the influence of size, shape, and density of microplastics on their transport modes: A modeling approach
Researchers developed a computer model that predicts how microplastics of different sizes, shapes, and densities move through ocean water. The model accurately simulates whether particles float on the surface, stay suspended in the water column, or settle to the bottom. Understanding how microplastics travel through marine environments is important for predicting where contamination accumulates and which seafood sources are most likely to be affected.
Predicting the toxicity of microplastic particles through machine learning models
Researchers developed machine learning models to predict microplastic particle toxicity from physical and chemical descriptors, addressing the classification challenge posed by the enormous diversity of particle types that cannot be characterized using conventional chemical hazard methods. The models provided accurate toxicity predictions across diverse microplastic types, offering a practical screening tool for the field.
Settling velocity of microplastic particles of regular shapes
This study measured the sinking velocities of spherical, cylindrical, and filament-shaped microplastic particles ranging from 0.5 to 5 mm, finding that shape strongly determines how quickly particles settle through the water column. Understanding settling behavior is essential for modeling how microplastics are transported and deposited in marine environments.
Effects of Shape and Size on Microplastic Atmospheric Settling Velocity
Researchers measured atmospheric settling and horizontal drift velocities of various microplastic shapes and sizes in controlled settling chambers, providing empirical data needed to improve atmospheric transport models that explain how microplastics reach remote environments.
Sinking velocity of sub-millimeter microplastic
Researchers measured the sinking velocities of irregularly shaped microplastic particles (polyamide, PMMA, and PET, 6–251 μm) and found they sink considerably slower than theoretical predictions for spheres of equivalent size, developing a predictive model based on particle size and excess density to better represent how real-world microplastics settle through the water column.
Coupled CFD-DEM modelling to assess settlement velocity and drag coefficient of microplastics
Researchers used computational fluid dynamics coupled with particle simulations to model how the size, shape, and density of microplastics affect their settling velocity and drag in water. Accurate physical models of microplastic behavior are essential for predicting where particles accumulate in rivers, lakes, and the ocean.
Predicting the toxicity of microplastic particles through machine learning models
Researchers applied machine learning models to predict the toxicity of microplastic particles from their physical and chemical properties, addressing the challenge that microplastics lack the standardized identifiers used for chemical hazard classification. The models successfully predicted toxicity outcomes from particle descriptors, offering a framework for hazard screening of the diverse and complex microplastic contaminant class.
Settling model to predict microplastics removal efficiency in wastewater treatments
A mathematical settling model was built to predict how efficiently wastewater treatment plants remove microplastics based on particle density, size, shape, and surface loading rates. The model shows that dense, large, spherical particles settle most readily, while light fibers and films are far harder to remove — providing treatment plant operators and engineers with a practical tool for optimizing processes to reduce the discharge of microplastics into rivers and coastal waters.
Empirical Shape-Based Estimation of Settling Microplastic Particles Drag Coefficient
This study experimentally measured the settling behavior of flat square microplastic particles in water, finding that shape significantly affects sinking speed and drag compared to spherical particles. Understanding how microplastic shapes influence settling is essential for modeling where plastics accumulate in rivers and ocean sediments.
Settling velocity of irregularly shaped microplastics under steady and dynamic flow conditions
The settling velocities of irregularly shaped microplastics were measured under both still water and dynamic flow conditions, finding that shape strongly affected settling speed and that turbulence caused non-spherical particles to orient and settle differently than spheres, with implications for predicting microplastic vertical transport in rivers and coastal waters.