In this paper, the predictive power of molecular dynamics methods is demonstrated for the cases of model paraffinic and aromatic lubricant liquids at pressures up to 400 MPa. The shear viscosity and self-diffusion coefficients are calculated for 2,2,4-trimethylpentane (C₈H₁₈) at 298 K and 1,1-diphenylethane (C₁₄H₁₄) at 333 K. Three force fields with different levels of accuracy are compared by the ability to predict the experimental data. The Stokes–Einstein correlation between viscosity and self-diffusion is demonstrated for both compounds.

Computational predictions of the high-pressure viscosity of hydrocarbon mixtures could help to accelerate the development of fuels and lubricants with improved performance. In this study, we use molecular dynamics simulations to study the viscosity and density of methylcyclohexane, 1-methylnaphthalene, and their binary mixtures at 323 K and pressures of up to 500 MPa. The simulation results are in excellent agreement with previous experiments available up to 100 MPa for both pure compounds (200 MPa for 1-methylnaphthalene) and the binary mixtures. For 1-methylnaphthalene, the viscosity initially increases slower-than-exponential with pressure before it reaches an inflection point and then increases faster-than-exponential. The inflection point (300 MPa) occurs at a pressure slightly below the one at which 1-methylnaphthalene is expected to enter the supercooled phase (400 MPa). For methylcyclohexane, the increase in viscosity with pressure is slower-than-exponential over the entire pressure range studied. The binary mixtures show intermediate pressure–viscosity responses between the two pure cases. The applicability of equations commonly used to describe the pressure dependence of viscosity, as well as the viscosity of binary mixtures, is evaluated against the computational predictions.

The nitrite admixtures in cement and concrete are used as corrosion inhibitors for steel reinforcement and also as anti-freezing agents. The characterization of the protective properties should account for the decrease in the concentration of free NO₂⁻ ions in the pores of cement concretes due to their adsorption. Here we applied the classical molecular dynamics computer simulation approach to quantitatively study the molecular scale mechanisms of nitrite adsorption from NaNO₂ aqueous solution on a portlandite surface. We used a new parameterization to model the hydrated NO₂⁻ ions in combination with the recently upgraded ClayFF force field (ClayFF-MOH) for the structure of portlandite. The new NO₂⁻ parameterization makes it possible to reproduce the properties of hydrated NO₂⁻ ions in good agreement with experimental data. In addition, the ClayFF-MOH model improves the description of the portlandite structure by explicitly taking into account the bending of Ca-O-H angles in the crystal and on its surface. The simulations showed that despite the formation of a well-structured water layer on the portlandite (001) crystal surface, NO₂⁻ ions can be strongly adsorbed. The nitrite adsorption is primarily due to the formation of hydrogen bonds between the structural hydroxyls on the portlandite surface and both the nitrogen and oxygen atoms of the NO₂⁻ ions. Due to that, the ions do not form surface adsorption complexes with a single well-defined structure but can assume various local coordinations. However, in all cases, the adsorbed ions did not show significant surface diffusional mobility. Moreover, we demonstrated that the nitrite ions can be adsorbed both near the previously-adsorbed hydrated Na⁺ ions as surface ion pairs, but also separately from the cations.