Challenge to Reconcile Experimental Micellar Properties of the CnEm Nonionic Surfactant Family

Abstract

We wished to compile a data set of results from the experimental literature to support the development and validation of accurate computational models (force fields) for an important class of micelle-forming nonionic surfactant compounds, the poly(ethylene oxide) alkyl ethers, usually denoted C n E m . However, careful examination of the experimental literature exposed a striking degree of variation in values reported for critical micelle concentrations (cmc) and mean aggregation numbers (N agg ). This variation was so large that it masked important trends known to exist within this family of molecules, thereby rendering most of the literature data to be of limited utility for force field development. In this work, we describe some reasons for the wide variability in the experimental literature, and we present a set of cmc and aggregation number data for 12 C n E m compounds that we feel is appropriate to use for the construction of and validation of computational models. The cmc values we selected are from the existing experimental literature and represent a carefully chosen and consistent subset that conveys important trends seen by many of the experimental studies. However, for a corresponding and consistent set of weight-averaged aggregation numbers, we needed to perform new dynamic light scattering (DLS) experiments. The results of these experiments were carefully analyzed to obtain not just mean aggregation numbers but also the underlying micelle size distribution functions. Several trends observed in the cmc and N agg observables are highlighted and serve as challenges for developers of force field and simulation methodology. The analysis of the DLS experiments accounts for the fact that a broad distribution of micelle sizes exists for many of these compounds and that one must be careful to use the appropriate weighted averages (e.g., mass-weighted vs number-weighted averages) in comparing results from different types of experiments and in comparing results from experiments with those from simulations.

What Can Digitization Do For Formulated Product Innovation and Development

“…It needs to be capable of providing useful predictions of the quantities of interest, for example liquid densities, octanol-water partition coefficients (log 10 P ), or in the case of surfactants, Critical Micelle Concentration (CMC) and micelle mean aggregation number (N agg ). [38,18,40,19,41] Assuming the physical model is appropriate for the system under consideration, the main factor affecting accuracy is the quality of the models parameters. Improving these parameters requires acquisition of suitable experimental data for model tuning and validation, a process termed parameterisation.…”

“…[42] Here we will briefly outline some examples of these issues. Interested readers can find a detailed account in Swope et al and its supplementary information [40]. Falling into the first category a problem we found frequently in the literature was tabulating data as comparable when it is not, by failure to recognize the difference between weight and number averaging in determining N agg number in micelles.…”

“…Motivated by these issues and the need for a consistent data set of micelle sizes for model development, we recently experimentally determined a small consistent dataset for non-ionic surfactants which can provide a useful starting point for parameterising surfactant models. [40]. In particular this data set contains abundant information on the experimental conditions and the data processing pipeline applied, information which is crucial for parmaeterisation effort.…”

“…A test set of 12 molecules were chosen from the 87 molecules and is comprised of previously validated data by the authors. [40] The model was generated by training on 75 molecules and predicting the remaining 12 molecules as an unseen test set. The results of the test set predictions are presented in table 1 and figure 4.…”

Utilizing Machine Learning for Efficient Parameterization of Coarse Grained Molecular Force Fields

“…58,59 The difficulties in assessing the accuracy of experimental data for force field parameterization has been expounded for other properties of industrial interest. 60 This places bounds on the accuracy our force field can realistically achieve. Given that the target data used here comes from a variety of experimental sources we would consider a good result to have errors in the region of ≈ ±0.7 log units.…”