Accurate Modeling of Ionic Surfactants at High Concentration

Salt- and pH-Dependent Viscosity of SDS/LAPB Solutions: Experiments and a Semiempirical Thermodynamic Model

We present novel data on the composition-, pH-, and salt-dependent zero shear viscosity of the commercially important mixture of anionic sodium dodecyl sulfate (SDS) and zwitterionic lauramidopropyl betaine (LAPB). We show via proton NMR experiments that the notionally zwitterionic LAPB exhibits a large pK_a shift in the presence of SDS and can become partially cationic at formulation-relevant pH ranges of 4.5-6.0-that is, the binary system is effectively a ternary system. This has a pronounced effect on the viscosity of the system at low pH, especially if the fraction of LAPB is high. We use theoretical arguments to motivate a semiempirical but practical approach to model the viscosity of the mixtures using thermodynamic parameters such as the excess chemical potentials or activity coefficients of the surfactants. We demonstrate this using an augmented regular solution theory-based mixed micelle thermodynamic model and develop robust regression models using Bayesian approaches. We also show how the pK_a shift from NMR experiments can be used to parameterize the thermodynamic model. This framework should be extensible to other arbitrary surfactant mixtures in the future and hence will be of broad interest for the development of surfactant formulations for household, personal care, and other applications.

Revealing the Origin of the Specificity of Calcium and Sodium Cations Binding to Adsorption Monolayers of Two Anionic Surfactants

The studied anionic surfactants linear alkyl benzene sulfonate (LAS) and sodium lauryl ether sulfate (SLES) are widely used key ingredients in many home and personal care products. These two surfactants are known to react very differently with multivalent counterions, including Ca²⁺. This is explained by a stronger interaction of the calcium cation with the LAS molecules, compared to SLES. The molecular origin of this difference in the interactions remains unclear. In the current study, we conduct classical atomistic molecular dynamics simulations to compare the ion interactions with the adsorption layers of these two surfactants, formed at the vacuum-water interface. Trajectories of 150 ns are generated to characterize the adsorption layer structure and the binding of Na⁺ and Ca²⁺ ions. We found that both surfactants behave similarly in the presence of Na⁺ ions. However, when Ca²⁺ is added, Na⁺ ions are completely displaced from the surface with adsorbed LAS molecules, while this displacement occurs only partially for SLES. The simulations show that the preference of Ca²⁺ to the LAS molecules is due to a strong specific attraction with the sulfonate head-group, besides the electrostatic one. This specific attraction involves significant reduction of the hydration shells of the interacting calcium cation and sulfonate group, which couple directly and form surface clusters of LAS molecules, coordinated around the adsorbed Ca²⁺ ions. In contrast, SLES molecules do not exhibit such specific interaction because the hydration shell around the sulfate anion is more stable, due to the extra oxygen atom in the sulfate group, thus precluding substantial dehydration and direct coupling with any of the cations studied.

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

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 C_nE_m. 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 12 members of the C_nE_m 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 analyzed 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.

The Relationship between Wormlike Micelle Scission Free Energy and Micellar Composition: The Case of Sodium Lauryl Ether Sulfate and Cocamidopropyl Betaine

The scission energy is the difference in free energy between two hemispherical caps and the cylindrical region of a wormlike micelle. This energy difference determines the logarithm of the average micelle length, which affects several macroscopic properties such as the viscosity of viscoelastic fluids. Here we use a recently published method by Wang et al. ( Langmuir, 2018, 34, 1564-1573) to directly calculate the scission energy of micelles composed of monodisperse sodium lauryl ether sulfate (SLES_nEO), an anionic surfactant. Utilizing dissipative particle dynamics (DPD), we perform a systematic study varying the number of ethoxyl groups (n) and salt concentration. The scission energy increases with increasing salt concentration, indicating that the formation of longer micelles is favored. We attribute this to the increased charge screening that reduces the repulsion between head groups. However, the scission energy decreases with increasing number of ethoxyl groups as the flexibility of the head group increases and the sodium ion becomes less tightly bound to the head group. We then extend the analysis to look at the effect of a common cosurfactant, cocamidopropyl betaine (CAPB), and find that its addition stabilizes wormlike micelles at a lower salt concentration.

Revisiting OPLS-AA Force Field for the Simulation of Anionic Surfactants in Concentrated Electrolyte Solutions

Hereby, we developed a set of nonbonded parameters within all-atom optimized potentials for liquid simulations (OPLS-AA) force field for the simulation of concentrated electrolyte solutions of anionic surfactants. More specifically, the aim of this paper is to assess the performance of five sets of atomic charges calculated using different population analyses (DDEC6, CHelpG, CHelpG-SMD, RESP, and CM5), as well as the original set of charges used in the literature for sodium dodecyl sulfate (SDS) simulation. Recently, Farafonov et al. have revised the SDS OPLS-AA force field; however, we were unable to obtain the experimental rodlike micelles using this parameter set on long time scale. In fact, the initial SDS bilayer micelle adopted a rodlike shape transiently and then broke down into spherical micelles. Updating OPLS-AA force field with DDEC6, CHelpG, and CHelpG-SMD charges resulted in stable rod micelles for a long simulation time (1 μs). The atomic charges of Farafonov (taken from Shelley et al.), RESP, and CM5 could not correctly describe SDS in concentrated electrolyte solutions. Analysis of the interaction of SDS with the counterions and solvent highlights the role of a balance of the intermolecular forces that must be met to describe adequately the anionic surfactant electrolyte solutions. Further, the optimization of the SDS Lennard-Jones parameters enabled the Farafonov set to properly reproduce the experimental rod micelle. In addition, we have examined the performance of different parameters of sodium ions: the first developed based on the Kirkwood-Buff integrals (KBI) and the second developed by Joung et al. The excessive ion pairing caused by KBI parameters screens significantly SDS-water interactions, which stabilize the rod micelle. Further, a tight interaction of the Na⁺-SDS head group resulted in stabilization of the bilayer micelle as observed in the case of Na⁺ parameters developed by Joung et al.

Scission Free Energies for Wormlike Surfactant Micelles: Development of a Simulation Protocol, Application, and Validation for Personal Care Formulations

We present a scheme to calculate wormlike micelle scission free energies from a potential of mean force (PMF) derived from a weighted histogram analysis method (WHAM) applied to coarse grained dissipative particle dynamics (DPD) simulations. In contrast to previous related work, we use a specially chosen external potential based on a reaction coordinate that reversibly drives surfactants out of the nascent scission location. For the application to a model body wash formulation, we predict how addition of NaCl and small molecules such as perfume raw materials (PRMs) affect scission energies. The results show qualitative agreement and correct trends compared to recently determined scission energies for the same system; however, a more rigorous parametrization of the underlying DPD potential is required for quantitative agreement.

Deep learning for computational chemistry

The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on multilayer neural networks. Within the last few years, we have seen the transformative impact of deep learning in many domains, particularly in speech recognition and computer vision, to the extent that the majority of expert practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties that distinguish them from traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including quantitative structure activity relationship, virtual screening, protein structure prediction, quantum chemistry, materials design, and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non-neural networks state-of-the-art models across disparate research topics, and deep neural network-based models often exceeded the "glass ceiling" expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a valuable tool for computational chemistry. © 2017 Wiley Periodicals, Inc.

The Size of AOT Reverse Micelles

Reverse micelles (RMs) made from water and sodium bis(2-ethylhexyl) sulfosuccinate (AOT) are commonly studied experimentally as models of aqueous microenvironments. They are small enough for individual RMs to also be studied by molecular dynamics (MD) simulation, which yields detailed insight into their structure and properties. Although RM size is determined by the water loading ratio (i.e., the molar ratio of water to AOT), experimental measurements of RM size are imprecise and inconsistent, which is problematic when seeking to understand the relationship between water loading ratio and RM size, and when designing models for study by MD simulation. Therefore, a systematic study of RM size was performed by MD simulation with the aims of determining the size of an RM for a given water loading ratio, and of reconciling the results with experimental measurements. Results for a water loading ratio of 7.5 indicate that the interaction energy between AOT anions and other system components is at a minimum when there are 62 AOT anions in each RM. The minimum is due to a combination of attractive and repulsive electrostatic interactions that vary with RM size and the dielectric effect of available water. Overall, the results agree with a detailed analysis of previously published experimental data over a wide range of water loading ratios, and help reconcile seemingly discrepant experimental results. In addition, water loss and gain from an RM is observed and the mechanism of water exchange is outlined. This kind of RM model, which faithfully reproduces experimental results, is essential for reliable insights into the properties of RM-encapsulated materials.

Multiscale Materials Modeling in an Industrial Environment

In this review, we sketch the materials modeling process in industry. We show that predictive and fast modeling is a prerequisite for successful participation in research and development processes in the chemical industry. Stable and highly automated workflows suitable for handling complex systems are a must. In particular, we review approaches to build and parameterize soft matter systems. By satisfying these prerequisites, efficiency for the development of new materials can be significantly improved, as exemplified here for formulation polymer development. This is in fact in line with recent Materials Genome Initiative efforts sponsored by the US government. Valuable contributions to product development are possible today by combining existing modeling techniques in an intelligent fashion, provided modeling and experiment work hand in hand.

Atomistic Simulation of Solubilization of Polycyclic Aromatic Hydrocarbons in a Sodium Dodecyl Sulfate Micelle

Solubilization of two polycyclic aromatic hydrocarbons (PAHs), naphthalene (NAP, 2-benzene-ring PAH) and pyrene (PYR, 4-benzene-ring PAH), into a sodium dodecyl sulfate (SDS) micelle was studied through all-atom molecular dynamics (MD) simulations. We find that NAP as well as PYR could move between the micelle shell and core regions, contributing to their distribution in both regions of the micelle at any PAH concentration. Moreover, both NAP and PYR prefer to stay in the micelle shell region, which may arise from the greater volume of the micelle shell, the formation of hydrogen bonds between NAP and water, and the larger molecular volume of PYR. The PAHs are able to form occasional clusters (from dimer to octamer) inside the micelle during the simulation time depending on the PAH concentration in the solubilization systems. Furthermore, the micelle properties (i.e., size, shape, micelle internal structure, alkyl chain conformation and orientation, and micelle internal dynamics) are found to be nearly unaffected by the solubilized PAHs, which is irrespective of the properties and concentrations of PAHs.

pH-Responsive Self-Assembly of Polysaccharide through a Rugged Energy Landscape

Self-assembling polysaccharides can form complex networks with structures and properties highly dependent on the sequence of triggering cues. Controlling the emergence of such networks provides an opportunity to create soft matter with unique features; however, it requires a detailed understanding of the subtle balance between the attractive and repulsive forces that drives the stimuli-induced self-assembly. Here we employ all-atom molecular dynamics simulations on the order of 100 ns to study the mechanisms of the pH-responsive gelation of the weakly basic aminopolysaccharide chitosan. We find that low pH induces a sharp transition from gel to soluble state, analogous to pH-dependent folding of proteins, while at neutral and high pH self-assembly occurs via a rugged energy landscape, reminiscent of RNA folding. A surprising role of salt is to lubricate the conformational search for the thermodynamically stable states. Although our simulations represent the early events in the self-assembly process of chitosan, which may take seconds or minutes to complete, the atomically detailed insights are consistent with recent experimental observations and provide a basis for understanding how environmental conditions modulate the structure and mechanical properties of the self-assembled polysaccharide systems. The ability to control structure and properties via modification of process conditions will aid in the technological efforts to create complex soft matter with applications ranging from bioelectronics to regenerative medicine.