Real Electrolyte Solutions in the Functionalized Mean Spherical Approximation: A Density Functional Theory for Simple Electrolyte Solutions.

The equation of state based on the mean spherical approximation (MSA) can describe electrolyte solutions as a primitive model, where the ions are charged hard-sphere particles and the solvent is a continuum medium. In recent years, many propositions of the classical density functional theory (cDFT) for electrolyte solutions have been presented. One of these is the functionalized MSA (fMSA) which has proven to be a great functional approach of MSA to calculate the electric double layer structures. This work demonstrates how the fMSA theory can describe real electrolyte solutions (e.g., NaCl, KI, and LiBr) where hydration and solvent concentration effects are present. Experimental data of the mean activity coefficients of different simple salts were successfully reproduced. When the hydrated diameter and the electrolyte solution electric permittivity are used, the fMSA predicts a charge inversion on the electrostatic potential near a charged surface at high salt concentrations.

Quantifying Pore Width Effects on Diffusivity via a Novel 3D Stochastic Approach with Input from Atomistic Molecular Dynamics Simulations.

The increased production of unconventional hydrocarbons emphasizes the need to understand the transport of fluids through narrow pores. Although it is well-known that confinement affects fluids structure and transport, it is not yet possible to quantitatively predict properties such as diffusivity as a function of pore width in the range of 1-50 nm. Such pores are commonly found not only in shale rocks but also in a wide range of engineering materials, including catalysts. We propose here a novel and computationally efficient methodology to obtain accurate diffusion coefficient predictions as a function of pore width for pores carved out of common materials, such as silica, alumina, magnesium oxide, calcite, and muscovite. We implement atomistic molecular dynamics (MD) simulations to quantify fluid structure and transport within 5 nm-wide pores, with particular focus on the diffusion coefficient within different pore regions. We then use these data as input to a bespoke stochastic kinetic Monte Carlo (KMC) model, developed to predict fluid transport in mesopores. The KMC model is used to extrapolate the fluid diffusivity for pores of increasing width. We validate the approach against atomistic MD simulation results obtained for wider pores. When applied to supercritical methane in slit-shaped pores, our methodology yields data within 10% of the atomistic simulation results, with significant savings in computational time. The proposed methodology, which combines the advantages of MD and KMC simulations, is used to generate a digital library for the diffusivity of gases as a function of pore chemistry and pore width and could be relevant for a number of applications, from the prediction of hydrocarbon transport in shale rocks to the optimization of catalysts, when surface-fluid interactions impact transport.