We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Machine learning-based prediction for settling velocity of microplastics with various shapes
AI summary Read the abstract
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.
Microplastics can easily enter the aquatic environment and be transported between water bodies. The terminal settling velocity of microplastics, which affects their transport and distribution in the aquatic environment, is mainly influenced by their size, density, and shape. Due to the difficulty in accurately predicting the terminal settling velocity of microplastics with various shapes, this study focuses on establishing high-performance prediction models and understanding the importance and effect of each feature parameter using machine learning. Based on the number of principal dimensions, the shapes of microplastics are classified into fiber, film, and fragment, and their thresholds are identified. The microplastics of different shape categories have different optimal shape parameters for predicting the terminal settling velocity: Corey shape factor, flatness, elongation, and sphericity for the fragment, film, fiber, and mixed-shape MPs, respectively. By including the dimensionless diameter, relative density and optimal shape parameter in the input parameter combination, the machine learning models can well predict the terminal settling velocity for the microplastics of different shape categories and mixed-shape with R > 0.867, achieving significantly higher performance than the existing theoretical and regression models. The interpretable analysis of machine learning reveals the highest importance of the microplastic size and its marginal effect when the dimensionless diameter D* = d(g/v) > 80, where d is the equivalent diameter, g is the gravitational acceleration, and ν is the fluid kinematic viscosity. The effect of shape is weak for small microplastics and becomes significant when D* exceeds 65.
More Papers Like This
Prediction of Settling Velocity of Microplastics by Multiple Machine-Learning Methods
AI summary Read the abstract
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
AI summary Read the abstract
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.
A new modeling approach for microplastic drag and settling velocity
AI summary Read the abstract
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.
Settling velocity of microplastic particles having regular and irregular shapes
AI summary Read the abstract
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.
Towards better predicting the settling velocity of film-shaped microplastics based on experiment and simulation data
AI summary Read the abstract
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.
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.