Theoretical study of the surface properties of poly(dimethylsiloxane) and poly(tetrafluoroethylene).

Molecular dynamics (MD) simulations of poly(dimethylsiloxane) (PDMS) and poly(tetrafluoroethylene) (PTFE) were carried out to determine their surface properties and energies. This study helps to gain better insight into the molecular modeling of PDMS and PTFE, in particular how different approaches affect calculations of surface energy. Current experimental and theoretical data were used to further understand the surface properties of PDMS and PTFE as well as to validate and verify results obtained from the combination of density functional theory (DFT) calculations (including periodic boundary conditions) and MD simulations. Detailed analysis of the structure and electronic properties (by calculation of the projected density of states) of the bulk and surface models of PDMS and PTFE was performed. The sensitivity of the surface energy calculation of these two polymers to the chemistry and model preparation was indicated. The balance between the molecular density, weight (which also reflects bond orientation in the surface region), bond flexibility, and intramolecular interactions including bond stretching was revealed to govern the results obtained. In modeling, the structural organization of polymer near a given surface (types and number of end groups and broken bonds due to application of different cut offs of the periodic structure) also significantly affects the final results. Besides the structural differences, certain simulation parameters, such the DFT functionals and simulation boxes utilized, play an important role in determining surface energy. The models used here were shown to be sufficient due to their good agreement with experimental and other theoretical data related to surface properties and surface energies.

Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles.

It is expected that the number and variety of engineered nanoparticles will increase rapidly over the next few years, and there is a need for new methods to quickly test the potential toxicity of these materials. Because experimental evaluation of the safety of chemicals is expensive and time-consuming, computational methods have been found to be efficient alternatives for predicting the potential toxicity and environmental impact of new nanomaterials before mass production. Here, we show that the quantitative structure-activity relationship (QSAR) method commonly used to predict the physicochemical properties of chemical compounds can be applied to predict the toxicity of various metal oxides. Based on experimental testing, we have developed a model to describe the cytotoxicity of 17 different types of metal oxide nanoparticles to bacteria Escherichia coli. The model reliably predicts the toxicity of all considered compounds, and the methodology is expected to provide guidance for the future design of safe nanomaterials.

Effect of charge distribution on RDX adsorption in IRMOF-10.

Quantum mechanical (QM) calculations, classical grand canonical Monte Carlo (GCMC) simulations, and classical molecular dynamics (MD) simulations are performed to test the effect of charge distribution on hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) adsorption and diffusion in IRMOF-10. Several different methods for mapping QM electron distributions onto atomic point charges are explored, including the electrostatic potential (ESP) method, Mulliken population analysis, Lowdin population analysis, and natural bond orbital analysis. Classical GCMC and MD simulations of RDX in IRMOF-10 are performed using 15 combinations of charge sources of RDX and IRMOF-10. As the charge distributions vary, interaction potential energies, the adsorption loading, and the self-diffusivities are significantly different. None of the 15 combinations are able to quantitatively capture the dependence of the energy of adsorption on local configuration of RDX as observed in the QM calculations. We observe changes in the charge distributions of RDX and IRMOF-10 with the introduction of an RDX molecule into the cage. We also observe a large dispersion contribution to the interaction energy from QM calculations that is not reproduced in the classical simulations, indicating that the source of discrepancy may not lie exclusively with the assignment of charges.