Computational characterisation of metal oxide nanoparticles for hazard screening and risk assessment

Identifier:  TDX:3094

Handle: https://hdl.handle.net/20.500.11797/TDX3094

Authors:  Escorihuela Martí, Laura

Abstract:
Given the intrinsic properties, metal oxide nanoparticles (MeO) NPs are the cornerstone of a wide range of technologically advanced applications in areas such as electronics, pharmacy or medicine. However, there is still an important knowledge gap regarding how size influences their physicochemical properties and the risk to human health. Toxicity assessment of NMs is a daunting task involving multiple testing conditions. Computed based methods, in silico methods, based on theoretical and statistical domain, evaluate, determine and predict processes or even substance properties. Furthermore, the legislation urgency for risk assessment exits given that the data for the environmental risk assessment found in literature is uncertain and present knowledge gaps, though is not useful for the risk assessment for nanoparticles. The most popular in silico method based on quantum mechanics for chemistry is Density Functional Theory (DFT). In this thesis we performed a strict and deep study of best methods to evaluate the band gap and the solubility of MeO NP. The use of periodical-DFT methods has allowed us to optimise the ground state energy for surfaces, nanotubes and spherical nanoparticles. To get more reliability for band gap determination, the exchange-correlation functional has been improved to DFT+U. After that, to reach to large systems up to 3000 atoms in order to simulate more realistic biological systems, we used DFTB methodology for band gap prediction; we also coupled DFTB with Molecular Dynamics to compute NP solubility. The computational results obtained with the methodology developed in this thesis for the ZnO case have been promising and, in order to make more robust the method employed, it has been tested for TiO2 too, showing an excellent efficiency in the results. Finally, the data obtained from the prediction models of band gap and solubility models have been used to create nano-QSAR (Quantity-Structure-Activity-Relationship) models.