Morphology of Ionic Micelles as Studied by Numerical Solution of the Poisson Equation

Concentration Dependent Asymmetric Synergy in SDS-DDAO Mixed Surfactant Micelles

We investigate the structure and interactions of a model anionic/amphoteric mixed surfactant micellar system, namely, sodium dodecyl sulfate (SDS) and N,N-dimethyldodecylamine N-oxide (DDAO), employing SANS, FTIR, DLS, and pH measurements, in the range 0.1-100 mM total surfactant concentration and 0-100% DDAO. Increasing surfactant concentration is found to elongate the prolate ellipsoid micelles (RPolar ∼ 25-40 Å), accompanied by up to a 6-fold increase in micellar charge. The surfactant synergy, in terms of micellar charge and size, diffusion coefficient, solution pH, and headgroup interactions, was found to vary with concentration. At lower concentrations (≤50 mM), the SDS-DDAO ratio of maximum synergy is found to be asymmetric (at 65-85% DDAO), which is rationalized using regular solution theory, suggesting an equilibrium between Na+ dissociation, DDAO protonation, and counterion concentration. At higher concentrations, maximum synergy shifts toward the equimolar ratio. Overall, our study expands and unifies previous reports, providing a comprehensive understanding for this model, synergetic mixed micellar system.

Enhancing near-infrared fluorescence intensity and stability of PLGA-b-PEG micelles by introducing Gd-DOTA at the core boundary

Micelles have been extensively used in biomedicine as potential carriers of hydrophobic fluorescent dyes. Their small diameters can potentially enable them to evade recognition by the reticuloendothelial system, resulting in prolonged circulation. Nevertheless, their lack of stability in physiological environments limits the imaging utility of micelles. In particular, when a dye sensitive to water, such as IR-1061, is encapsulated in the micelle core, the destabilized structure leads to interactions between water and dye, degrading the fluorescence. In this study, we investigated a method to improve micelle stability utilizing the electrical effect of gadolinium (Gd3+ ) and tetraazacyclododecane tetraacetic acid (DOTA), introduced into the micelles. Three micellar structures, one containing a poly(lactic-co-glycolic acid)-block-poly(ethylene glycol) (PLGA-b-PEG) block copolymer, and two other structures, including PLGA-b-PEG with DOTA or Gd-DOTA introduced at the boundary of PLGA and PEG, were prepared with IR-1061 in the core. Structures that contained DOTA at the border of the PLGA core and PEG shell exhibited much higher fluorescence intensity than probes without DOTA. With Gd3+ ions at the DOTA center, fluorescence stability was enhanced remarkably in physiological environments. Most interesting is the finding that fluorescence is enhanced with increased Gd-DOTA concentrations. In conclusion, we found that overall fluorescence and stability are improved by introducing Gd-DOTA at the boundary of the PLGA core and PEG shell. Improving micelle stability is crucial for further biomedical applications of micellar probes such as bimodal fluorescence and magnetic resonance imaging.

Estimation of Nanoparticle's Surface Electrostatic Potential in Solution Using Acid-Base Molecular Probes I: In Silico Implementation for Surfactant Micelles

Surface electrostatic potential Ψ is a key characteristic of colloid particles. Since the surface of the particles adsorbs various compounds and facilitates chemical reactions between them, Ψ largely affects the properties of adsorbed reactants and governs the flow of chemical reactions occurring between them. One of the most popular methods for estimating Ψ in hydrophilic colloids, such as micellar surfactant solutions and related systems, is the application of molecular probes, predominantly acid-base indicator dyes. The Ψ value is calculated from the difference of the probe's indices of the apparent acidity constant between the examined colloid solution and, usually, some other colloid solution with noncharged particles. Here, we show how to implement this method in silico using alchemical free energy calculations within the framework of molecular dynamics simulations. The proposed implementation is tested on surfactant micelles and is shown to predict experimental Ψ values with quantitative accuracy depending on the kind of surfactant. The sources of errors in the method are discussed, and recommendations for its application are given.

Estimation of Nanoparticle's Surface Electrostatic Potential in Solution Using Acid-Base Molecular Probes II: Insight from Atomistic Simulations of Micelles

Exploiting acid-base indicators as molecular probes is one of the most popular methods for determining the surface electrostatic potential Ψ in hydrophilic colloids like micellar surfactant solutions and related systems. Specifically, the indicator's apparent acidity constant index is measured in the colloid solution of interest and, as a rule, in a nonionic surfactant solution; the difference between the two is proportional to Ψ. Despite the widespread use of this approach, a major problem remains unresolved, namely, the dissimilarity of Ψ values obtained with different indicators for the same system. The common point of view recognizes the effect of several factors (the choice of the nonionic surfactant, the probe's localization, and the degree of hydration of micellar pseudophase) but does not allow to quantitatively assess their impact and decide which indicator reports the most correct Ψ value. Here, based on the ability to predict the reported Ψ values in silico, we examined the role of these factors using molecular dynamics simulations for five probes and two surfactants. The probe's hydration in the Stern layer was found responsible for approximately half of the dissimilarity range. The probe's localization is found important but hard to quantify because of the irregular structure of the Stern layer. The most accurate indicators among the examined set were identified. Supplementing experiments on measuring Ψ with molecular dynamics simulation is proposed as a way of improving the efficacy of the indicator method: the simulations can guide the choice of the most suitable probe and nonionic surfactant for the given nanoparticles.