Nanomaterials (Mar 2023)

Machine Learning to Predict the Adsorption Capacity of Microplastics

  • Gonzalo Astray,
  • Anton Soria-Lopez,
  • Enrique Barreiro,
  • Juan Carlos Mejuto,
  • Antonio Cid-Samamed

DOI
https://doi.org/10.3390/nano13061061
Journal volume & issue
Vol. 13, no. 6
p. 1061

Abstract

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Nowadays, there is an extensive production and use of plastic materials for different industrial activities. These plastics, either from their primary production sources or through their own degradation processes, can contaminate ecosystems with micro- and nanoplastics. Once in the aquatic environment, these microplastics can be the basis for the adsorption of chemical pollutants, favoring that these chemical pollutants disperse more quickly in the environment and can affect living beings. Due to the lack of information on adsorption, three machine learning models (random forest, support vector machine, and artificial neural network) were developed to predict different microplastic/water partition coefficients (log Kd) using two different approximations (based on the number of input variables). The best-selected machine learning models present, in general, correlation coefficients above 0.92 in the query phase, which indicates that these types of models could be used for the rapid estimation of the absorption of organic contaminants on microplastics.

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