Physico-chemical characterization of nanoparticles in the context of theirs transport and fate in the environment is an important challenge for risk assessment of nanomaterials. One of the main characteristics that define the behavior of nanoparticles in solution is zeta potential (ζ). In this paper we have demonstrated the relationship between zeta potential and a series of intrinsic physico-chemical features of 15 metal oxide nanoparticles revealed by computational study. The developed here Quantitative Structure-Property Relationship model (nano-QSPR) was capable to predict ζ of metal oxide nanoparticles utilizing only two descriptors: (i) the spherical size of nanoparticlesa parameter from numerical analysis of Transmission Electron Microscopy (TEM) images and (ii) the energy of the highest occupied molecular orbital per metal atom -a theoretical descriptor calculated by quantum mechanics at semiempirical level of theory (PM6 method). The obtained consensus model is characterized by reasonably well predictivity (Q 2 ext =0.87). Therefore, the developed model can be utilized to in silico evaluation of properties of novel engineered nanoparticles. This study is a first step in developing a comprehensive and computationally-based system to predict physico-chemical properties that are responsible for aggregation phenomena in metal oxide nanoparticles.

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The human health and environmental risk assessment of engineered nanomaterials (NPs) is nowadays of high interest.

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In this study, photocatalytic properties and in vitro cytotoxicity of newly designed 29 hybrid TiO2-based nanomaterials were evaluated using a combination of the experimental testing and machine learning modeling.