Predictive model of cationic surfactant binding to humic substances

Abstract

The humic substances (HS) have a high reactivity with other components in the natural environment. An important factor for the reactivity of HS is their negative charge. Cationic surfactants bind strongly to HS by electrostatic and specific interaction. Therefore, a surfactant binding model is developed that takes both the specific and electrostatic interactions explicitly into account. The model is analogous to that of ion binding to HS with the NICA-Donnan model, but competition for the binding sites is not taken into account and the NICA-Donnan model reduces to the Langmuir–Freundlich–Hill–Donnan (LFH-D) model. The parameters of the LFH equation are the maximum binding, the affinity constant and the non-ideality constant. The non-ideality parameter accounts for both the site heterogeneity and the cooperativity due to hydrophobic interaction between surfactant molecules. The affinity constant incorporates the specific (e.g., hydrophobic) interactions between surfactant and HS. The Donnan part of the model accounts in a simple way for the electrostatic interactions by assuming that for a given set of conditions there is only one electrostatic potential that governs the behavior in the Donnan phase. The separation between the specific interactions (LHF) and the electrostatic interactions (D) is based on the so-called master curve (MC) procedure in which the binding is replotted as a function of the “free” surfactant concentration in the Donnan phase. The MC depends only on the specific interactions. Once the MC is obtained it can be fitted to the LFH equation to obtain the model parameters. Subsequently, the surfactant isotherms can be calculated with the LFH-D model. The model is tested using previously obtained data on dodecyl pyridinium chloride (DPC) and cetyl pyridinium chloride (CPC) binding to some purified humic and fulvic acids at pH about 5. The LFH-D model is well suited to describe the surfactant binding to HAs from very low concentrations up to the iso-electric point (IEP). The affinity of DPC for the different HAs allows ranking of the HAs according to their hydrophobicity. Prediction of DPC binding at other pH values also leads to good results for HA. For FAs the model can only describe the surfactant binding up to an adsorbed amount of 0.5 mol/kg. For higher binding values the LFH-D model underestimates the binding.