RT Journal Article
T1 Machine learning to predict the adsorption capacity of microplastics
A1 Astray Dopazo, Gonzalo
A1 Soria Lopez, Anton
A1 Barreiro Alonso, Enrique
A1 Mejuto Fernández, Juan Carlos
A1 Cid Samamed, Antonio
K1 2210 Química Física
K1 1203.17 Informática
K1 2391 Química Ambiental
AB 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.
PB Nanomaterials
SN 20794991
YR 2023
FD 2023-03-15
LK http://hdl.handle.net/11093/4697
UL http://hdl.handle.net/11093/4697
LA eng
NO Nanomaterials, 13(6): 1061 (2023)
NO Ministerio de Universidades | Ref. FPU2020/06140
DS Investigo
RD 14-ene-2025