Model for the Simulation of the C_nE_m Nonionic Surfactant Family Derived from Recent Experimental Results

Abstract

Using a comprehensive set of recently published experimental results for training and validation, we have developed computational models appropriate for simulations of aqueous solutions of poly(ethylene oxide) alkyl ethers, an important class of micelle-forming nonionic surfactants, usually denoted CnEm. These models are suitable for use in simulations that employ a moderate amount of coarse graining and especially for dissipative particle dynamics (DPD), which we adopt in this work. The experimental data used for training and validation were reported earlier and produced in our laboratory using dynamic light scattering (DLS) measurements performed on twelve members of the CnEm compound family yielding micelle size distribution functions and mass weighted mean aggregation numbers at each of several surfactant concentrations. The range of compounds and quality of the experimental results were designed to support the development of computational models. An essential feature of this work is that all simulation results were analysed in a way that is consistent with the experimental data. Proper account is taken of the fact that a broad distribution of micelle sizes exists, so mass weighted averages (rather than number weighted averages) over this distribution are required for the proper comparison of simulation and experimental results. The resulting DPD force field reproduces several important trends seen in the experimental critical micelle concentrations and mass averaged mean aggregation numbers with respect to surfactant characteristics and concentration. We feel it can be used to investigate a number of open questions regarding micelle sizes and shapes and their dependence on surfactant concentration for this important class of nonionic surfactants. File list (2) download file view on ChemRxiv DPDpaper-4.pdf (1.02 MiB) download file view on ChemRxiv DPDpaperSI-3.pdf (2.71 MiB)

Interactions of Crosslinked Polyacrylic Acid Polyelectrolyte Gels with Nonionic and Ionic Surfactants

“…n -Alkyl chains contain five hydrophobic T beads, each of which represents a fragment of four carbons, according to refs and , approximately representing the long alkyl sidechain in ACULYN and Carbomer copolymers. The forcefield is coarser than the forcefield of Anderson et al and the recently derived forcefield for the CE surfactant family by Johnston et al As mentioned, the alkyl chain length varies in different Carbopol-type products and, generally, it is not fixed. For example, in ACULYNs, the longest alkyl chains are stearyl, n -C 18 .…”

Estimating Preferred Alkane Carbon Numbers of Nonionic Surfactants in Normalized Hydrophilic–Lipophilic Deviation Theory from Dissipative Particle Dynamics Modeling

“…In DPD modeling, there are practices to represent C i E j nonionic surfactants as H m T n (H is the head bead and T is the tail bead) by treating internal and terminal beads identically; − other approaches differentiate terminal and internal beads by introducing additional bead types, like terminal hydrophobic beads, CH 3 – or CH 3 CH 2 –, and terminal hydrophilic beads, like −OH, −CH 2 OH, −OCH 2 CH 2 OH, and so on. Which practice should be chosen depends on the property to be studied, and the key is to keep the model simple without losing too much chemical detail to reproduce experimental results.…”

“…The coarse graining strategy shown in Figure was applied; in this strategy, one bead represents two water molecules or one ethyl group or one EO group. A similar coarse graining scheme was also used by Johnston et al It should be noted here that as there are no hydrogen bonding interactions between polyethylene glycol (PEG) and alkane, tri-ethylene glycol is represented by H 3 for interfacial tension fitting with dodecane (O 6 ), which will be discussed in detail later.…”

Towards an Approximation-Aware Computational Workflow Framework for Accelerating Large-Scale Discovery Tasks

“…For a number of years, ourselves and our collaborators have focused on computational workflows for materials design [1,11,22,23,26,29]. This involved designing and running large scale computational chemistry workflows on machines from BG/Q TM , to IBM POWER8 TM through to clusters managed by Kubernetes [8].…”