Development of a novelin silicomodel of zeta potential for metal oxide nanoparticles: a nano-QSPR approach

Abstract

Once released into the aquatic environment, nanoparticles (NPs) are expected to interact (e.g. dissolve, agglomerate/aggregate, settle), with important consequences for NP fate and toxicity. A clear understanding of how internal and environmental factors influence the NP toxicity and fate in the environment is still in its infancy. In this study, a quantitative structure-property relationship (QSPR) approach was employed to systematically explore factors that affect surface charge (zeta potential) under environmentally realistic conditions. The nano-QSPR model developed with multiple linear regression (MLR) was characterized by high robustness [Formula: see text] and external predictivity [Formula: see text] The results clearly showed that zeta potential values varied markedly as functions of the ionic radius of the metal atom in the metal oxides, confirming that agglomeration and the extent of release of free MexOy largely depend on their intrinsic properties. A developed nano-QSPR model was successfully applied to predict zeta potential in an ionized solution of NPs for which experimentally determined values of response have been unavailable. Hence, the application of our model is possible when the values of zeta potential in the ionized solution for metal oxide nanoparticles are undetermined, without the necessity of performing more time consuming and expensive experiments. We believe that our studies will be helpful in predicting the conditions under which MexOy is likely to become problematic for the environment and human health.

A computational structure–property relationship study of glass transition temperatures for a diverse set of polymers

“…Hence, the 7‐variable model was selected as the most accurate for the entire set. Its ratio of the number compounds and variables in the model ensured the avoidance of overfitting, which is still within the 5:1 rule …”

Towards the Development of Global Nano-Quantitative Structure–Property Relationship Models: Zeta Potentials of Metal Oxide Nanoparticles

“…We initially extracted more than 150 data points; however, after data curation (as is described in Materials and Methods section), we included in the reliable dataset 87 zeta potential measurements from 12 literature sources ( Table 1 ) [ 1 , 12 , 18 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ].…”

“…The QSPR has been widely applied over the last decade to predict nanoparticle properties [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. To date, only three research articles have reported QSPR modeling of zeta potentials [ 17 , 18 , 19 ]. These nano-QSPR models focused only on chemical composition-dependent descriptors [ 17 , 18 , 19 ].…”

“…To date, only three research articles have reported QSPR modeling of zeta potentials [ 17 , 18 , 19 ]. These nano-QSPR models focused only on chemical composition-dependent descriptors [ 17 , 18 , 19 ]. In fact, these models include a variety of core compositions but do not take into consideration size-dependent effects (namely, the dataset do not contain information about the same core composition nanoparticles of different sizes).…”

Development of a nano-QSPR model to predict band gaps of spherical metal oxide nanoparticles

“…[14][15][16][17] Wyrzykowska et al developed a nano-QSPR model to predict the zeta potential of MONs in KCl aqueous solution of MONs. 18 In this study, we developed a nano-QSPR model, considering different crystalline types and sizes, to predict band gaps of MONs. Besides, band gaps were also calculated by two other estimation methods to compare the predictive ability of different methods.…”