Alicja Mikolajczyk

Development of a novel in silico model of zeta potential for metal oxide nanoparticles: a nano-QSPR approach

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.

Evaluating the toxicity of TiO2-based nanoparticles to Chinese hamster ovary cells and Escherichia coli: a complementary experimental and computational approach

Titania-supported palladium, gold and bimetallic nanoparticles (second-generation nanoparticles) demonstrate promising photocatalytic properties. However, due to unusual reactivity, second-generation nanoparticles can be hazardous for living organisms. Considering the ever-growing number of new types of nanoparticles that can potentially contaminate the environment, a determination of their toxicity is extremely important. The main aim of presented study was to investigate the cytotoxic effect of surface modified TiO2-based nanoparticles, to model their quantitative nanostructure–toxicity relationships and to reveal the toxicity mechanism. In this context, toxicity tests for surface-modified TiO2-based nanoparticles were performed in vitro, using Gram-negative bacteria Escherichia coli and Chinese hamster ovary (CHO-K1) cells. The obtained cytotoxicity data were analyzed by means of computational methods (quantitative structure–activity relationships, QSAR approach). Based on a combined experimental and computational approach, predictive models were developed, and relationships between cytotoxicity, size, and specific surface area (Brunauer–Emmett–Teller surface, BET) of nanoparticles were discussed.

Size-dependent electronic properties of nanomaterials: How this novel class of nanodescriptors supposed to be calculated?

In this study, the influence of the size on the electronic properties (e.g. electronic energy) of three nanometal oxides: ZnO, TiO2, and Al2O3 were investigated. The wurtzite, rutile and corundum type of clusters were selected to represent ZnO, TiO2, and Al2O3, respectively. To study the effect of the size on the property, we have build several molecular cluster models with different number of atoms and performed for those clusters quantum–mechanical calculations. For small clusters, up to 40 atoms, the calculations at different levels of theory, including: density functional theory (DFT), Hartree–Fock method, and the semi-empirical PM6 method were carried out. The results from ab initio and DFT calculations were utilized to validate the less time-consuming PM6 approach. The PM6 method was then employed for larger clusters. Linear regression models were developed to describe the relationships between size (number of atoms in cluster) and the electronic properties. The developed and validated methodology is transferable and could be applied for other type of nanosized clusters to calculate properties that are considered as potential nanodescriptors for nano-QSAR modelling.

Combined experimental and computational approach to developing efficient photocatalysts based on Au/Pd–TiO2 nanoparticles

Surface modified TiO2-based nanoparticles (the so-called second generation nanoparticles) have unique semiconducting properties. They act as efficient photocatalysts, demonstrating catalytic activity under UV-vis and light-emitting diode (mix-LED) light. Consequently, they can be used as versatile, low-cost, clean and environmentally benign components in many innovative solutions, e.g. treatment technology for a wide range of environmental pollutants. However, for commercial application of TiO2-based systems, it is crucial to develop nano-powders that can absorb light in the visible spectrum. Our investigation has demonstrated the potential benefits of using a chemoinformatics approach to obtaining knowledge on structural features responsible for the photocatalytic activity of second generation NPs under visible light. Through a combination of multiple linear regression (MLR) and a genetic algorithm (GA), we have developed a quantitative structure–properties relationship (Nano-QSPR) model (R2 = 0.89, RMSEC = 1.67, QLOO2 = 0.82, RMSECV = 2.18, QEXT2= 0.80, RMSEP = 1.46) based on the most relevant physicochemical properties that characterized selected Au/Pd–TiO2 NPs. According to the developed Nano-QSPR model, the anatase phase and palladium content are the main factors responsible for the higher activity of Au/Pd–TiO2 photocatalysts under visible light. It should be noted that the methodology presented here can serve as an important starting point for further design of new nanomaterials with enhanced functionality, supported by chemoinformatics methods.

Nano-QSAR modeling for ecosafe design of heterogeneous TiO2-based nano-photocatalysts

The human health and environmental risk assessment of engineered nanomaterials (NPs) is nowadays of high interest. It is important to assess and predict the biological activity, toxicity, physicochemical properties, fate and transport of NPs. In this work, a combined experimental and computational study is performed in order to estimate the toxicity and develop a predictive model for heterogeneous NPs (i.e. modified NPs, created from more than one type of NPs). Quantitative structure–activity relationship (QSAR) methods have not been yet adopted for predicting the toxicity/physicochemical properties of modified heterogeneous nanoparticles (so-called heterogeneous NPs). Since the main problem for nano-QSAR/nano-QSPR modeling of heterogeneous NPs was a lack of appropriate descriptors that are able to express the specific characteristics of 2nd generation NPs, we developed here a novel approach. The novel approach to encode heterogeneous NPs is based on the idea of additive descriptors for mixture systems previously applied only to mixtures of organic/inorganic compounds. Thus, based on the proposed novel approach, we have performed experimental and theoretical studies to develop nano-QSAR models describing the cytotoxicity of 34 TiO2-based NPs modified by (poly)metallic clusters (Au, Ag, Pt) to the Chinese hamster ovary cell line. The models showed a good predictive ability and robustness. This approach can be used as an efficient tool for assessing the toxicity as well as physicochemical properties of unexplored heterogeneous NPs.