| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Leszczynski, Jerzy
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (4/4 displayed)
- 2021Zeta potentials (ζ) of metal oxide nanoparticles: a meta-analysis of experimental data and a predictive neural networks modelingcitations
- 2019Predicting Thermal Conductivity Enhancement of Al2O3/Water and CuO/Water Nanofluids Using Quantitative Structure-Property Relationship Approachcitations
- 2017Exploring Simple, Interpretable, and Predictive QSPR Model of Fullerene C60 Solubility in Organic Solventscitations
- 2015Zeta potential for metal oxide nanoparticles: a predictive model developed by a nano-quantitative structure-property relationship approachcitations
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article
Zeta potential for metal oxide nanoparticles: a predictive model developed by a nano-quantitative structure-property relationship approach
Abstract
Physico–chemical characterization of nanoparticles in the context of their transport and fate in the environment is an important challenge for risk assessment of nanomaterials. One of the main characteristics that defines 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 here-developed quantitative structure–property relationship model (nano-QSPR) was able to predict the ζ of metal oxide nanoparticles utilizing only two descriptors: (i) the spherical size of nanoparticles, a 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 good predictivity (QEXT2 = 0.87). Therefore, the developed model can be utilized for 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.