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

Abstract

<jats:p>&lt;div&gt;&lt;div&gt;&lt;div&gt;&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;The experimental data used for training and validation were reported earlier and produced in our laboratory using dynamic light scattering (DLS) measurements per- formed on twelve members of the CnEm compound family yielding micelle size dis- tribution 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 es- sential 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 num- ber weighted averages) over this distribution are required for the proper comparison of simulation and experimental results.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;The resulting DPD force field reproduces several important trends seen in the exper- imental 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.&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;</jats:p>

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 21 and 22, approximately representing the long alkyl sidechain in ACULYN and Carbomer copolymers. The forcefield is coarser than the forcefield of Anderson et al 26 and the recently derived forcefield for the CE surfactant family by Johnston et al 27 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

“…The coarse graining strategy shown in Figure 1 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 23 It should be noted 28,29 and then developed by Groot and Warren 30 to link the DPD parameters with the interaction parameter, χ, in Flory− Huggins theory, which makes it possible to simulate the mesoscale system with the parameters derived from atomistic scale simulations and experimental results.…”

“…There are existing force field optimization frameworks, like force-balance, 40 which can be used for automated DPD parameter optimization, and Johnston et al 23 developed a DPD force field to reproduce the critical micelle concentration (CMC) of 12 ethoxylate nonionic surfactants using the force-balance framework. Machine learning has also been applied for parameterization of coarse-grained force fields, and McDonagh et al 41 utilized the machine learning approach to optimize DPD interaction parameters against experimental partition coefficients by employing Bayesian optimization.…”

“…Here, the oil−oil interaction parameter (OO = TT = 37.325) and the head−head interaction parameter (HH = HH e = H e H e = 34.193) are leveraged from the work by Johnston et al 23 The head−oil interaction parameter, HO, can be calculated by fitting the PEG and alkane interfacial tension (see the Supporting Information for the detailed procedure), as shown in Figure 12; to reproduce the interfacial tension of dodecane (C 12 )/tri-ethylene glycol (10.8 mN/m 47 ), the HO (= HT) should be 41.75, this value is close but slightly higher than that (HT = 40.45) from Johnston et al 23 With OO = TT = TO = 37.325, HH = 34.193, and HO = 41.75, the possible HW and H e W interaction parameters giving zero torque density for the C 10 E 4 (H 4 T 5 )−octane (O 4 )−water system can be instantly computed using the ANN model, which are listed in Table 6; here, the smallest step size of 0.1 DPD unit was used to fine tune the interaction parameters, and no significant improvement for PACN estimation was observed with even smaller step size. It should be noted that the selection of OO (OO = TT = TO) and HH interaction parameters here is only used as an example of our procedure, and the ANN model and the method for PACN estimation still apply if other interaction parameter values are chosen, which does not affect the validity of the procedure we proposed.…”

Virtual Issue on Surfactants

“…With the same theoretical perspective, Johnston et al 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 C n E m , using a comprehensive set of recently published experimental results. 18 The model was found to be successful in investigating several queries regarding micelle sizes and shapes and their dependence on surfactant concentration for this important class of nonionic surfactants. On the other hand, aiming toward the application to the alkyl ethoxylate surfactant self-assembly, Lavagnini et al developed a first-principles computational method so that bead interaction parameters can be calculated directly from ab initio gas-phase molecular electrostatic potential surfaces of the molecular fragments that represent the beads.…”