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Papers
61,005 resultsShowing papers similar to Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges
ClearA Strategy for Dimensionality Reduction and Data Analysis Applied to Microstructure–Property Relationships of Nanoporous Metals
This materials science study applied machine learning to predict the mechanical properties of nanoporous metals from their microstructural features, offering an efficient way to optimize material design. While focused on metals rather than plastics, similar data-driven approaches are being developed for predicting the environmental behavior of microplastics.
Exploring the Research on Utilizing Machine Learning in E-Learning Systems
Not relevant to microplastics — this systematic literature review surveys how machine learning techniques are applied in e-learning systems to improve educational outcomes and predict student performance.
Predicting the Composition and Mechanical Properties of Seaweed Bioplastics from the Scientific Literature: A Machine Learning Approach for Modeling Sparse Data
This paper is not relevant to microplastics research — it applies machine learning to predict the mechanical properties of seaweed-based bioplastic films, focusing on biodegradable material design rather than microplastic pollution or its health effects.
Current applications and future impact of machine learning in emerging contaminants: A review
This review examines how machine learning is being applied to emerging contaminant research including microplastics, covering identification, environmental behavior prediction, bioeffect assessment, and removal optimization of these pollutants.
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.
Thermo‐based fatigue life prediction: A review
Not relevant to microplastics — this review covers thermography-based methods for predicting the fatigue life of metals under cyclic stress, with no connection to plastic pollution or environmental health.
[Overview of the Application of Machine Learning for Identification and Environmental Risk Assessment of Microplastics].
This review examines the application of machine learning (ML) methods for identifying microplastics and assessing their environmental risks, covering techniques for improving the accuracy and reliability of microplastic detection across different environmental media. Researchers highlight how ML can systematically analyse pollution characteristics and support ecological risk evaluation of microplastic contamination.
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.
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.
Integrating Genomic and Proteomic Data Using Machine Learning for Plastic Biodegradation: A Systematic Review
This systematic review summarizes how machine learning and genomic data are being used to identify microbes and enzymes that can break down plastic waste. The research is significant for microplastic concerns because discovering more effective biological degradation pathways could provide a natural solution for reducing the microplastic pollution that accumulates in our environment and bodies.
Rapid estimation of fatigue limit for C45 steel by thermography and digital image correlation
This materials engineering study used thermography and digital image correlation to rapidly estimate the fatigue limit of steel, linking temperature and mechanical changes to the onset of microplastic deformation in metal. It is a mechanical engineering paper not related to environmental microplastics.
Recent Advances in Polymer Design through Machine Learning: A Short Review
This review examines recent advances in applying machine learning — including supervised learning, unsupervised learning, and artificial neural networks — to polymer informatics, covering property prediction, synthesis optimisation, and polymer classification across diverse applications from medicine to aerospace. The authors highlight how growing datasets and improving ML techniques are enabling more systematic and effective polymer design compared to traditional trial-and-error approaches.
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.
Artificial Intelligence Models for Predicting Ground Vibrations in Deep Underground Mines to Ensure the Safety of Their Surroundings
Not relevant to microplastics — this is a mining safety engineering study using artificial intelligence models to predict ground vibrations from underground mine blasting near surface structures.
Machine learning plastic deformation of crystals
Researchers used machine learning to predict how microscale crystals deform under stress, finding that predictability varies with strain level and crystal size — larger crystals behave more predictably. The study reveals that sudden, avalanche-like deformation events create fundamental limits on how well material failure can be forecast, with implications for engineering stronger microscale components.
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.
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.
Revealing factors influencing polymer degradation with rank-based machine learning
Researchers developed a machine learning platform using a ranking-based algorithm to predict and compare how easily different polymer materials biodegrade, integrating three different experimental datasets with varying conditions. Analysis revealed key molecular factors that control degradability, offering guidance for designing more environmentally friendly plastics.
Artificial Intelligence and Machine Learning Approaches for Automatic Microplastics Identification and Characterization
This review examines how artificial intelligence and machine learning algorithms are being applied to identify, characterize, and model microplastic pollution in the environment. The authors found that these tools can analyze large sensor datasets to detect microplastics in water bodies, predict transport patterns, and model adsorption behavior under various environmental conditions. The study highlights the growing role of computational approaches in understanding and mitigating microplastic contamination.
Impact of conformation and intramolecular interactions on vibrational circular dichroism spectra identified with machine learning
Not relevant to microplastics — this paper applies machine learning to predict vibrational circular dichroism spectra of organic molecules from their 3D geometry, a chemistry methods paper with no connection to microplastics.
Application of Machine Learning in Nanotoxicology: A Critical Review and Perspective
This review evaluates how machine learning and artificial intelligence are being used to predict the toxic effects of nanomaterials, including nanoplastics, on human health and the environment. These computational tools can help screen thousands of materials for potential hazards much faster than traditional lab experiments, though the authors note that better data quality and standardized methods are still needed.
Eco-Transformation of construction: Harnessing machine learning and SHAP for crumb rubber concrete sustainability
Researchers applied machine learning algorithms, including random forest and AdaBoost models, to predict the strength of concrete made with recycled crumb rubber from waste tires — a material that can reduce microplastic pollution from tire wear. The random forest model achieved strong accuracy (R² of 0.87), and found that rubber content and concrete age are the biggest factors influencing strength.
An integrated chemical engineering approach to understanding microplastics
Researchers proposed an integrated chemical engineering approach combining artificial intelligence, theoretical methods, and experimental techniques to better understand microplastic properties and behavior. The study suggests that the broad scope of chemical engineering makes it well-suited for characterizing microplastics and addressing the complexity of their environmental and health effects.
Machine LearningAdvancements and Strategies in Microplasticand Nanoplastic Detection
This systematic review summarizes how machine learning technology is being used to detect microplastics and nanoplastics in the environment. Better detection methods are important because understanding where these particles are and how much is present is the first step toward assessing risks to human health.