Oil/water transfer is partly driven by molecular shape, not just size

Abstract

We present a new approach to computer modeling of solvation free energies of oil in water. In Semi-Explicit Assembly, we first precompute structural and thermal properties of TIP3P waters around different Lennard-Jones spheres. This tabulated information is then used to compute the nonpolar solvation properties of arbitrary solutes. By accumulating interactions from whole regions of the solute molecule, Semi-Explicit Assembly more properly accounts for effects of solute shape and solves problems that appear as nonadditivities in traditional gammaA approaches. Semi-Explicit Assembly involves little parameter fitting because the solute and water properties are taken from existing force fields. We tested the predictions on alkanes, alkynes, linear and planar polyaromatic hydrocarbons, and on a diverse set of 504 molecules previously explored by explicit solvent simulations. We found that not all hydrocarbons are the same. Hydrocarbons have "hot spots", places where first-shell waters interact more strongly with the molecule than at other locations. For example, waters are more attracted to hover over hydrocarbon rings than at the edges. By accounting for these collective regional effects, Semi-Explicit Assembly approaches the physical accuracies of explicit solvent models in computing nonpolar solvation free energies, but because of the precomputations and the regional additivities, it is nearly as fast to compute as gammaA methods.

Figure 1

Nonpolar solvation free energy (ΔG) of single LJ spheres in TIP3P water at 300 K as a function of their σ and ε parameters. Unfavorable ΔG values are red. Favorable ΔG values are blue.

Figure 2

The process for incorporating non-additive environmental effects on the solute surface atoms. (a) Sample LJ spheres in explicit water and build a map of water distances (r_w) as a function of σ and ε. (b) Construct the solvent accessible surface (SAS) using the distances from the explicit solvent map. (c) Probe the LJ potential of the solute along the line connecting each SAS dot to its surface atom. Average these potentials for each surface atom, and extract new “effective” LJ parameters (σ_ra and ε_ra) from this curve. (d) Use these effective potential parameters when calculating the solvation free energy. Note that edge atoms will have more attractive ε_ra values than corner atoms because of the greater number of atoms near to the probe particle.

Figure 3

The nonpolar solvation free energy for a series of a) linear alkanes, b) linear alkynes, c) polyaromatic hydrocarbons (PAHs) in a linear arrangement, and d) PAHs in a planar arrangement calculated using γA + b, Semi-Explicit Assembly, and explicit solvent. For γA + b, the traditional (0.00542 × SA_tot) + 0.92 was used, and the TIP3P results are those obtained through explicit free energy calculations. Experimental comparisons to ΔG cannot be drawn with the linear alkynes or PAHs series, because they have a substantial polar term to the overall solvation.

Figure 4

Maps of the collective dispersion attraction about the solvent accessible surface (SAS) of a) n-pentane, b) cyclopentane, c) pent-1-yne, d) benzene, and e) pyrene. The color of the surface indicates the LJ well-depth, with blue starting at 0 kcal/mol and red lowering to deeper than 5 kcal/mol. Note the red “hot spots” around the triple bond in pent-1-yne and in the center of the benzene and pyrene ring planes. These indicate a significant enhancement of dispersion attraction with the surroundings. As these regions grow with increasing molecule size, these collective dispersion attractions will offset the cost of cavity formation in surrounding solvent. With a simple γA, all these surfaces would be a uniform blue.

Figure 5

Correlation plots of ΔG values comparing a) γA + b and b) our Semi-Explicit Assembly technique with the ΔG values from explicit solvent free energy calculations. A detailed incorporation of dispersion interactions takes what was originally a flat correlation and brings it much more in line with explicit solvent results. This results in a correlation coefficient improvement from 0.15 to 0.91 and an RMS deviation decrease from 1.2 kcal/mol down to 0.3 kcal/mol over the entire set.