Adsorption of single and mixed ionic surfactants at fluid interfaces

Two different approaches have been used in the literature to describe the effects of ionisation of surfactants on the surface pressure, Π. One approach is based on a molecular model for a charged monolayer, in which the mutual repulsion of the long-chain surfactant ions results in an additional surface pressure, Πel, calculated with the Gouy–Chapman theory for the formation of a diffuse electric double layer, and with counterion binding in the Stern–Helmholtz layer adjacent to the surfactant monolayer. The other approach regards the surface as a two-dimensional solution defined as a Gibbs dividing surface, which is electroneutral by definition. In this approach, the adsorption of any ion is the sum of its excesses in the monolayer and the electrical double layer; no assumptions are made about the spatial distribution of charges. It has been shown that both models can produce a reasonable description of experimental results obtained for solutions of a single ionic surfactant (RX) with or without inorganic electrolyte (XY). In many cases, measurements of Π vs. mean ionic activity at different salt concentrations (cXY) are found to coincide on a single curve, implying that at given mean ionic activity both adsorption and Π are independent of cXY, i.e. that double-layer contributions to the surface pressure are negligible. In addition, the electroneutral 2-D solution approach has resulted in a simple explanation of several typical features of mixed ionic surfactant solutions, in particular for mixtures of anionic and cationic surfactants. In mixed solutions too, double-layer effects appear to be negligible. We present arguments for such negligibility. One reason is a significant degree of binding between adsorbed surface active ions (R) and counterions (X); another is that for 1:1 electrolytes, the contribution of the diffuse double layer to the adsorption of the combination (RX) vanishes. As a result, it is possible to interpret the same experimental data in terms of both models.