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

Abstract

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.

• BSAI
• in situ characterization
• nanoparticles
• pesticide
• surface physicochemistry

Acknowledgements

The authors thank the nanoGEM collaboration for providing a portion of well-characterized nanomaterials tested in this study. R.C. thank Dr Majid Jaberi for discussion on data analysis and data fitting and Dr Jeffrey Comer for discussion on physical molecular interactions. The sponsors had no role in study design, data collection and analysis, decision to publish or preparation of this article.

Declaration of interest

The authors declare no competing fINANcial interest. This work was partially supported by the Kansas Bioscience Authority funds to the Institute of Computational Comparative Medicine (ICCM) at Kansas State University and funds from the Nanotechnology Innovation Center of Kansas State University (NICKS). No additional external funding was received for this study.

Supplementary material available online

Supplementary Tables S1–S4 and Figure S1