![]() The findings show that the proposed model is a useful and efficient tool for identifying unknown polar liquids.
Nowadays machine learning techniques are being used widely to assist the measurement techniques and make predictions with great accuracy and less human effort. It requires a long time and effort to compare the measured values with the available standard values to identify the unknown liquid. 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.ĭiv align="left">The dispersive nature of polar liquids creates ambiguity in their identification process. 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 K d) using two different approximations (based on the number of input variables). 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. These plastics, either from their primary production sources or through their own degradation processes, can contaminate ecosystems with micro-and nanoplastics. Nowadays, there is an extensive production and use of plastic materials for different industrial activities.
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