Quantification of nanoparticle pesticide adsorption: computational approaches based on experimental data

Quantitative analysis of the interactions between nanomaterials and environmental contamINANts, such as pesticides, in natural water systems and food residuals is crucial for the application of nanomaterials-based tools for the detection of the presence of toxic substances, monitoring pollution levels and environmental remediation. Previously, the Biological Surface Adsorption Index (BSAI) has demonstrated promising capabilities of interaction characterization and prediction based on experimental data from small organic molecules. In this article, the first attempt of the application of such quantitative measures toward environmental endpoints by analyzing the interactions of a selected group of nanomaterials with a variety of pesticides was made. Statistical modeling was conducted on the experimental obtained adsorption data based on polynomial BSAI models, as well as models with the incorporation of artificial neural network methodologies. Finally, clustering analyzes were performed for the categorization of nanomaterials based on surface physicochemical properties using both polynomial indices and physical adsorption modeling parameters. These quantitative computational approaches support the application of BSAI modeling in the area of environmental contamINANt detection and remediation.