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Papers
20 resultsShowing papers similar to A Strategy for Dimensionality Reduction and Data Analysis Applied to Microstructure–Property Relationships of Nanoporous Metals
ClearMaterials Informatics for Mechanical Deformation: A Review of Applications and Challenges
This review covers machine learning methods applied to predicting and understanding mechanical properties of materials from large datasets. It is an engineering informatics paper and is not related to microplastics or environmental health.
Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials
Researchers used machine learning and Bayesian network analysis on 4D microscopy data from cracking metal samples to identify which microstructural features best predict how small fatigue cracks grow and in which direction. The resulting analytical model outperformed existing fatigue metrics, offering a more accurate tool for predicting when and how structural metal components will fail under repeated stress.
Assessment of machine learning-based methods predictive suitability for migration pollutants from microplastics degradation
Researchers assessed the usefulness of machine learning methods for predicting the migration of chemical pollutants from microplastics. The study found that artificial neural networks and support vector methods showed strong potential for modeling and predicting the leaching of plasticizers and other contaminants, which could reduce the need for extensive laboratory analyses.
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.
Design of an Efficient Model for Microplastic Removal in Wastewater using Advanced Filtration, Nanotechnology, and Bioremediation
This paper proposed an advanced machine learning model to design and optimize microplastic removal in wastewater treatment, using process parameters to predict removal efficiency. The intelligent model outperformed conventional design approaches in predicting treatment outcomes.
Introduction to data‐driven systems for plastics and composites manufacturing
Not relevant to microplastics — this is an introduction to a special issue on machine learning and data-driven methods for plastics and composites manufacturing.
Prediction of the cytotoxicity of micro- and nanoplastics using machine learning combined with literature data mining
Researchers developed a machine learning framework using decision tree ensemble classifiers trained on 1,824 literature-derived data points to predict the cytotoxicity of micro- and nanoplastics based on nine physicochemical and experimental features. The full-feature model achieved 95% accuracy with 86% recall and precision, and feature selection identified six key predictors, providing a tool to guide experimental design and harmonize MNP toxicity research.
Investigation Study of Structure Real Load Spectra Acquisition and Fatigue Life Prediction Based on the Optimized Efficient Hinging Hyperplane Neural Network Model
Not relevant to microplastics — this paper develops an optimized neural network model for predicting real-world load spectra and fatigue life of mechanical structures, achieving a fatigue life prediction accuracy of 93.56% for engineering applications.
Application of machine learning to assess the influence of microstructure on twin nucleation in Mg alloys
Researchers used machine learning to analyze what factors influence the formation of twin structures in magnesium alloys, studying over 3,000 individual grains. They found that grain boundary characteristics and loading conditions were the most important predictors of twin nucleation. The study demonstrates that machine learning can be a powerful tool for understanding complex microstructural behavior in metals.
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.
Potential threat of microplastics to humans: toxicity prediction modeling by small data analysis
Researchers developed a toxicity prediction model for microplastics using small data analysis techniques, enabling the anticipation of varying toxic effects depending on microplastic types and compositions found in nature.
Effects of Pore Morphology and Bone Ingrowth on Mechanical Properties of Microporous Titanium as an Orthopaedic Implant Material
This biomedical engineering study examined how pore size, shape, and bone ingrowth affect the mechanical properties of porous titanium used in orthopedic implants, using both experimental testing and computer simulations. This is a biomedical engineering study with no direct relevance to environmental microplastics.
Data-driven machine learning modeling reveals the impact of micro/nanoplastics on microalgae and their key underlying mechanisms
Researchers used machine learning to predict how micro- and nanoplastics affect freshwater algae, training models on a decade of published experimental data. The best-performing model identified plastic concentration, exposure time, and particle size as the most important factors determining toxicity. The study offers a data-driven framework that could reduce the need for time-consuming laboratory experiments when assessing microplastic risks to aquatic organisms.
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.
Recent advances in the application of machine learning methods to improve identification of the microplastics in environment
This review examined a decade of progress in applying machine learning algorithms to microplastic identification, finding that support vector machines and artificial neural networks significantly improve detection accuracy and efficiency when combined with spectroscopic techniques like FTIR and Raman.
Machine Learning Predictive Model for Permeability Alteration Induced by Microplastics Migration in Porous Media
Researchers developed a machine learning model (MLM) trained on a dataset of over 190,000 data points generated via combined Computational Fluid Dynamics and Discrete Element Method simulations to predict how microplastic particles clog pore throats and impair permeability in porous media. The three-component MLM achieved 95% accuracy in predicting clogged throats and an R-squared value of 0.99 for permeability impairment prediction, offering a new tool for assessing microplastic impacts on groundwater systems.
A Review of Damage, Void Evolution, and Fatigue Life Prediction Models
This engineering review summarizes models for predicting how damage, voids, and fatigue cause materials such as metals and composites to fail over time. This materials science paper is not related to microplastic environmental contamination.
Machine learning-driven QSAR models for predicting the mixture toxicity of nanoparticles
Researchers used machine learning to predict how toxic different mixtures of metal nanoparticles are to bacteria. Their models outperformed traditional methods at predicting combined toxicity effects. While focused on engineered nanoparticles rather than microplastics, the computational approach could be adapted to predict health risks from the complex mixtures of nano-sized pollutants people encounter.
Combining machine learning with meta-analysis for predicting cytotoxicity of micro- and nanoplastics
This meta-analysis used machine learning to predict how toxic different types of micro- and nanoplastics are to cells. By analyzing data from many studies, it identified that particle size, concentration, and exposure time are key factors determining toxicity — smaller particles and longer exposures tend to cause more cell damage.
Learning to Predict Crystal Plasticity at the Nanoscale: Deep Residual Networks and Size Effects in Uniaxial Compression Discrete Dislocation Simulations
Researchers demonstrated that a deep residual neural network can predict crystal plasticity size effects in nanoscale materials by learning from surface strain profiles, significantly outperforming traditional machine learning approaches in predicting mechanical behavior of nano- to micro-scale samples.