Astray, G.; Soria-Lopez, A.; Barreiro, E.; Mejuto, J.C.; Cid-Samamed, A. Machine Learning to Predict the Adsorption Capacity of Microplastics. Nanomaterials2023, 13, 1061.
Astray, G.; Soria-Lopez, A.; Barreiro, E.; Mejuto, J.C.; Cid-Samamed, A. Machine Learning to Predict the Adsorption Capacity of Microplastics. Nanomaterials 2023, 13, 1061.
Nowadays, there is extensive production and use of plastic materials for different industrial activities. These plastics, either from their primary production sources or through degradation processes of the plastics themselves, can contaminate the ecosystem 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) to predict different microplastic/water partition coefficients (log Kd) were developed using two different approximations (based on the number of input variables). The best-selected machine learning models present, in general, correlation coefficients upper than 0.92 in the query phase, which indicate that these type of models could be used for a rapid estimation of the absorption of organic contaminants on microplastics.
microplastics; adsorption capacity; machine learning; random forest; support vector machine; artificial neural network; prediction
CHEMISTRY, Physical Chemistry
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