We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Papers
20 resultsShowing papers similar to An Open-Source Computer Vision-Based Method for Microplastic Settling Velocity Calculation
ClearAn Open-Source Computer-Vision-Based Method for Spherical Microplastic Settling Velocity Calculation
Researchers developed an open-source computer vision method to measure the settling velocity of spherical microplastics, replacing subjective manual methods with automated image analysis. The tool provides a standardized, accessible approach for predicting microplastic transport and fate in aquatic environments.
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.
Particle tracking algorithm and additional data for "Optimized and Validated Settling Velocity Measurement for Small Microplastic Particles (10–400 µm)"
This dataset and code repository accompanies a study on measuring the settling velocity of small microplastic particles (10–400 µm) in water. The materials include image processing routines and particle tracking algorithms designed to improve measurement accuracy for tiny plastic fragments. Accurate settling data helps predict how microplastics distribute in water bodies.
A Handy Open-Source Application Based on Computer Vision and Machine Learning Algorithms to Count and Classify Microplastics
An open-source computer vision application was developed to automatically count and classify microplastics in microscopy images, achieving accuracy comparable to manual counting while processing samples orders of magnitude faster, offering the scientific community a free tool to reduce the bottleneck of tedious visual microplastic enumeration.
Settling velocities of microplastics and tire and road wear particles
Researchers developed a high-precision optical imaging method to measure how fast small microplastics (10–400 micrometers) and tire-and-road wear particles sink through water, filling a critical data gap needed to predict where these pollutants accumulate in aquatic environments.
Optimized and Validated Settling Velocity Measurement for Small Microplastic Particles (10–400 μm)
This study developed and validated a precise laboratory method for measuring how fast small microplastic particles (10–400 µm) sink in water — a key parameter for predicting where microplastics accumulate in aquatic environments. The setup uses a temperature-controlled settling column with optical particle tracking and achieves high accuracy across a range of particle sizes and densities. Accurate settling velocity data for small microplastics is essential for modeling their transport and fate in rivers, lakes, and oceans, which informs risk assessments for aquatic organisms that live at different depths.
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.
Towards A universal settling model for microplastics with diverse shapes: Machine learning breaking morphological barriers
Researchers developed a machine learning model to predict the settling velocity of microplastics across different shapes, including fragments, films, and fibers. Unlike existing models limited to specific morphologies, this approach works universally across all three particle types. The study provides a more reliable tool for modeling how microplastics move through and deposit in aquatic environments.
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 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.
Machine 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.
Impact of the Reynolds Numbers on the Velocity of Floating Microplastics in Open Channels
Researchers experimentally tracked the motion of nearly spherical polyethylene, polypropylene, and polystyrene microplastics in open channel flow using video analysis, establishing quantitative relationships among Reynolds number, MP density, and floating velocity to better predict horizontal transport behavior.
Particle tracking algorithm and additional data for "Optimized and Validated Settling Velocity Measurement for Small Microplastic Particles (10–400 µm)"
Researchers developed and published a particle tracking algorithm and supplementary datasets supporting validated settling velocity measurements for small microplastic particles in the 10-400 µm size range. The repository includes image processing routines, single-particle raw settling data, empirical model results for particle interaction effects, and supporting videos.
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.
An experimental study on microplastic settling velocities in different water environments: Which factors shape the settling process?
Researchers experimentally investigated how biofilm formation and weathering processes affect the settling velocities of microplastics across different water matrices, identifying the key physical and biological factors shaping how particles sink in aquatic environments.
Additional data for "Settling Velocities of Small Microplastic Fragments and Fibers"
This data repository provides raw settling velocity measurements for small microplastic fragments and fibers, supporting a publication on their transport behavior in water. Settling velocity data is critical for modeling where microplastics deposit in rivers, lakes, and ocean sediments.
Three-Dimensional Settling Dynamics of Environmental Microplastics
Researchers measured the three-dimensional settling dynamics of environmental microplastic particles in water, including lateral drift, settling paths, and horizontal velocities—dimensions poorly understood beyond simple vertical settling rates. The findings are essential for developing accurate models of how MPs distribute across river channels and water columns.
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.
A Machine Learning Approach To Microplastic Detection And Quantification In Aquatic Environments
This study developed a machine learning approach for detecting and quantifying microplastics in aquatic environments, demonstrating that automated image analysis can improve throughput and accuracy compared to manual microscopic counting for environmental monitoring applications.
Settling behaviors of microplastic disks in acceleration fall
Researchers studied the settling behavior of disk-shaped microplastics during free-fall in water, using high-speed imaging to track the orientation and velocity of particles as they descended. Disk-shaped particles exhibited oscillating and tumbling motions that slowed settling compared to spheres of equivalent mass, with implications for predicting microplastic transport and deposition in aquatic environments.