The recent developments in nanotechnology have not only increased the number of nanoproducts on the market, but also raised concerns about the safety of engineered nanomaterials (ENMs) for human health and the environment. As the production and use of ENMs are increasing, we are approaching the point at which it is impossible to individually assess the toxicity of a vast number of ENMs. Therefore, it is desirable to use time-effective computational methods, such as the quantitative structure-activity relationship (QSAR) models, in order to predict the toxicity of ENMs. However, the accuracy of the nano-(Q)SARs is directly tied to the quality of the data from which the model is estimated.Although the amount of available nanotoxicity data is insufficient for generating robust nano-(Q)SAR models in most cases, there are a handful of studies that provide appropriate experimental data for (Q)SAR-like modelling investigations. The aim of this study is to review the available literature data that are particularly suitable for nano-(Q)SAR modelling.We hope that this paper can serve as a starting point for those who would like to know more about the current availability of experimental data on the health effects of ENMs for future modelling purposes.

A mathematical model has been applied to simulate a con ned, turbulent natural gas diVusion ame for which measurements were reported in the literature [7]. The combustion is modelled using both eddy dissipation [2] and non-equilibrium, mixedness-reactedness amelet [6,20] models. The latter model is based on a laminar amelet approach originally developed for premixed combustion. The turbulence is represented by a Reynolds stress model based on the diVerential transport equations for stresses. The computational results are compared with experimental data for gas temperature and species concentrations. The predictions obtained using the amelet model are found to be in good agreement with measurements, whereas the eddy dissipation combustion model fails to capture the measured trends in the near-burner region. EVorts have been made to improve the quality of the predictions of the latter model by incorporating an extinction criterion [25] and by adjusting the value of the model parameter. Both actions generate predictions similar to that of the amelet model.àFavre uctuating quantity C08597