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To keep pace with its rapid development an efficient approach for the risk assessment of nanomaterials is needed. Grouping concepts as developed for chemicals are now being explored for its applicability to nanomaterials. One of the recently proposed grouping systems is DF4nanoGrouping scheme. In this study, we have developed three structure-activity relationship classification tree models to be used for supporting this system by identifying structural features of nanomaterials mainly responsible for the surface activity. We used data from 19 nanomaterials that were synthesized and characterized extensively in previous studies. Subsets of these materials have been used in other studies (short-term inhalation, protein carbonylation, and intrinsic oxidative potential), resulting in a unique data set for modeling. Out of a large set of 285 possible descriptors, we have demonstrated that only three descriptors (size, specific surface area, and the quantum-mechanical calculated property 'lowest unoccupied molecular orbital') need to be used to predict the endpoints investigated. The maximum number of descriptors that were finally selected by the classification trees (CT) was very low-one for intrinsic oxidative potential, two for protein carbonylation, and three for NOAEC. This suggests that the models were wellconstructed and not over-fitted. The outcome of various statistical measures and the applicability domains of our models further indicate their robustness. Therefore, we conclude that CT can be a useful tool within the DF4nanoGrouping scheme that has been proposed before. ARTICLE HISTORY

Nowadays nanotechnology is one of the most promising areas of science. The number and quantity of synthesized nanomaterials increase exponentially, therefore it is reasonable to expect that comprehensive risk assessment based only on empirical testing of all novel engineered nanoparticles (NPs) will very soon become impossible. Hence, the development of computational methods complementary to experimentation is very important. Quantitative structure-property relationship (QSPR) and quantitative structure-activity relationship (QSAR) models widely used in pharmaceutical chemistry and environmental science can also be modified and adopted for nanotechnology to predict physico-chemical properties and toxicity of empirically untested nanomaterials. All QSPR/QSAR modelling activities are based on experimentally derived data. It is important that, within a given data set, all values should be consistent, of high quality and measured according to a standardized protocol. Unfortunately, the amount of such data available for engineered nanoparticles in various data sources (i.e. databases and the literature) is very limited and seldom measured with a standardized protocol. Therefore, we have proposed a framework for collecting and evaluating the existing data, with the focus on possible applications for computational evaluation of properties and biological activities of nanomaterials.

Environmental context. The present paper looks at the possible spreading of a new class of chemicals, namely, ionic liquids in soils. These ionic liquids have gained increasing attention, and their environmental impact and fate needs to be determined before accidental release. The paper specifically focusses on the adhesion mechanisms of these chemicals onto a type of clay, kaolinite. It was found that a multilayer adhesion mechanism occurs. Sorption of ionic liquids on kaolinite indicates that these chemicals can modify the clay surfaces, which may lead to changes in its natural geochemical cycles. Abstract. In the present study, the mechanism of sorption of ionic liquids onto kaolinite clay mineral has been investigated in detail. Results obtained indicate a multilayer type of adsorption. At final saturations, the highest distribution coefficients were found for the long alkyl chain molecules. The mean free energy values obtained are below values of a typical ion-exchange process, which thus suggests that the adsorption mechanism is a combination of electrostatic interaction and physical sorption. At the beginning of the binding process (formation of a monolayer), ion-exchange and van der Waals interactions are predominantly responsible for the process, whereas at higher concentrations of ionic liquids, dispersive interactions become dominant. Thermodynamic parameters were also calculated from graphical interpretation of experimental data. Negative values of ΔG indicate a spontaneous sorption process for ionic liquids. Standard heats of adsorption were found to be exothermic and entropy contributions were negative in all cases studied.

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