Evaluation of interfacial affinity between surface-modified metal oxide and solvent mixture using molecular dynamics simulations

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Abstract
The industrial application of surface-modified nanoparticles requires controlling their colloidal stability by selecting suitable surface modifiers and solvents. Solvent mixtures can control the interaction between the nanoparticles and solvents, but the detailed mechanism is still unclear. In this study, molecular dynamics simulations were performed for an interface between a decanoic acid-modified Al2O3 and a binary mixture of organic solvents. Cyclohexane and ethanol were selected as a good solvent and a poor solvent for the modifier, respectively, and their volume fraction for preparation was varied. For any volume fraction of ethanol, ethanol was adsorbed onto the Al2O3 surface patches without modifiers. Cyclohexane was preferentially distributed around the modifier. The work of adhesion was calculated as a measure of interfacial affinity using thermodynamic integration. For systems with ethanol, the work of adhesion was significantly larger than for those with cyclohexane, owing to the strong interaction of the Al2O3 surface with the adsorbed ethanol; however, this result, that is, the obtained affinity, is not consistent with the experimental dispersion results of surface-modified nanoparticles. To focus on the interaction between modifiers and surrounding solvents, the work required to strip solvents from the modifier was calculated, excluding the contribution of adsorbed ethanol. The required work decreased after the ethanol fraction surpassed a specific threshold, indicating that the affinity can be related to the distribution of solvent species near the modifier layer. This work provides an evaluation method and insights into the effect of a solvent mixture on the interfacial affinity at the molecular level.
Funding Information
  • Ministry of Education, Culture, Sports, Science and Technology (JPMXP0219192801)
  • Japan Society for the Promotion of Science (JP23KJ0116, JP24K22919)
  • Core Research for Evolutional Science and Technology (JPMJCR23L3)

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