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Related Experiment Video

Updated: Aug 5, 2025

Sampling, Identification and Characterization of Microplastics Release from Polypropylene Baby Feeding Bottle during Daily Use

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Published on: July 24, 2021

Machine Learning to Predict the Adsorption Capacity of Microplastics.

Gonzalo Astray¹, Anton Soria-Lopez¹, Enrique Barreiro²

¹Universidade de Vigo, Departamento de Química Física, Facultade de Ciencias, 32004 Ourense, Spain.

Nanomaterials (Basel, Switzerland)

|March 29, 2023

Summary

Machine learning models accurately predict organic contaminant absorption on microplastics. This research aids in understanding microplastic pollution and its environmental impact.

Keywords:

adsorption capacity artificial neural network machine learning microplastics prediction random forest support vector machine

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Sampling, Sorting, and Characterizing Microplastics in Aquatic Environments with High Suspended Sediment Loads and Large Floating Debris

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Published on: July 28, 2018

Area of Science:

Environmental Chemistry
Polymer Science
Computational Chemistry

Background:

Extensive plastic production leads to microplastic and nanoplastic contamination in ecosystems.
Microplastics in aquatic environments facilitate the adsorption and dispersal of chemical pollutants.

Purpose of the Study:

To address the lack of data on microplastic adsorption of chemical pollutants.
To develop predictive models for microplastic/water partition coefficients (log Kd).

Main Methods:

Development of three machine learning models: random forest, support vector machine, and artificial neural network.
Utilized two approximations based on the number of input variables for model training.
Validated model performance using correlation coefficients in the query phase.

Main Results:

The developed machine learning models achieved high predictive accuracy.
Correlation coefficients above 0.92 were observed in the query phase for the best models.
Demonstrated the capability of machine learning to estimate organic contaminant absorption on microplastics.

Conclusions:

Machine learning models offer a rapid and reliable method for estimating organic contaminant absorption on microplastics.
These models can significantly contribute to assessing the environmental risks associated with microplastic pollution.
Further research can refine these models for broader applications in environmental monitoring.