Zeta potentials (ζ) of metal oxide nanoparticles: A meta-analysis of experimental data and predictive neural networks modeling

Abstract Zeta potential is usually measured to estimate the surface charge and stability of nanomaterials, as changes in these characteristics directly influence the biological activity of a given nanoparticle. Nowadays, theoretical methods are commonly used for pre-screening safety assessments of nanomaterials. At the same time, the consistency of data on zeta potentials measurements in the context of environmental impact is an important challenge. The inconsistency of data measurements leads to inaccuracies in predictive modeling. In this article, we report a curated dataset of zeta potentials measured for 208 silica- and metal oxide nanoparticles in different media. We discuss the data curation framework for zeta potentials designed to assess the quality and usefulness of the literature data for further computational modeling. We also provide an analysis of specific trends for datapoints harvested from different literature sources. In addition to that, for the curated dataset, we present for the first time a structure-property relationship (nano-SPR) model that predicts values of zeta potentials for nanoparticles measured in different environmental conditions (biological media and pH).