Can Machine Learning Predict the Phase Behavior of Surfactants?

We explore the prediction of surfactant phase behavior using state-of-the-art machine learning methods, using a data set for twenty-three nonionic surfactants. Most machine learning classifiers we tested are capable of filling in missing data in a partially complete data set. However, strong data bias and a lack of chemical space information generally lead to poorer results for entire de novo phase diagram prediction. Although some machine learning classifiers perform better than others, these observations are largely robust to the particular choice of algorithm. Finally, we explore how de novo phase diagram prediction can be improved by the inclusion of observations from state points sampled by an analogy to commonly used experimental protocols. Our results indicate what factors should be considered when preparing for machine learning prediction of surfactant phase behavior in future studies.

Phase Behavior of Alkyl Ethoxylate Surfactants in a Dissipative Particle Dynamics Model.

We present a dissipative particle dynamics (DPD) model capable of capturing the liquid state phase behavior of nonionic surfactants from the alkyl ethoxylate (CnEm) family. The model is based upon our recent work [Anderson et al. J. Chem. Phys. 2017, 147, 094503] but adopts tighter control of the molecular structure by setting the bond angles with guidance from molecular dynamics simulations. Changes to the geometry of the surfactants were shown to have little effect on the predicted micelle properties of sampled surfactants, or the water-octanol partition coefficients of small molecules, when compared to the original work. With these modifications the model is capable of reproducing the binary water-surfactant phase behavior of nine surfactants (C8E4, C8E5, C8E6, C10E4, C10E6, C10E8, C12E6, C12E8, and C12E12) with a good degree of accuracy.

Modeling Alkyl Aromatic Hydrocarbons with Dissipative Particle Dynamics.

Building on previous work studying alkanes, we develop a dissipative particle dynamics (DPD) model to capture the behavior of the alkyl aromatic hydrocarbon family under ambient conditions of 298 K and 1 atmosphere. Such materials are of significant worldwide industrial importance in applications such as solvents, chemical intermediates, surfactants, lubricating oils, hydraulic fluids, and greases. We model both liquids and waxy solids for molecules up to 36 carbons in size and demonstrate that we can correctly capture both the freezing transition and liquid-phase densities in pure substances and mixtures. We also demonstrate the importance of including specialized bead types into the DPD model (rather than solely relying on generic bead types) to capture specific local geometrical constructs such as the benzene ring found in the benzyl chemical group; this can be thought of as representing subtle real-world many-body effects via customized pairwise non-bonded potentials.

Critical Micelle Concentrations in Surfactant Mixtures and Blends by Simulation.

We explore the use of coarse-grained dissipative particle dynamics simulations to predict critical micelle concentrations (CMCs) in polydisperse surfactant mixtures and blends. By fitting pseudo-phase separation models (PSMs) to aqueous solutions of binary surfactant mixtures at selected compositions above the CMC, we avoid the need for expensive simulations of more complex multicomponent mixtures performed as a function of dilution. The approach is demonstrated for sodium laureth sulfate (SLES) surfactants with polydispersity in the ethoxylate spacer. For this system, we find a modest degree of cooperativity in micelle formation, which we attribute to the reduced repulsion between charged headgroups for surfactants with dissimilar ethoxylate spacer lengths. However, this is insufficient to explain the lowered CMC often observed in commercial SLES samples, which we attribute to the presence of small amounts of unsulfated alkyl ethoxylates and/or traces of salt.

The Role of Chemical Heterogeneity in Surfactant Adsorption at Solid-Liquid Interfaces.

Chemical heterogeneity of solid surfaces disrupts the adsorption of surfactants from the bulk liquid. While its presence can hinder the performance of some formulations, bespoke chemical patterning could potentially facilitate controlled adsorption for nanolithography applications. Although some computational studies have investigated the impact of regularly patterned surfaces on surfactant adsorption, in reality, many interesting surfaces are expected to be stochastically disordered and this is an area unexplored via simulations. In this paper, we describe a new algorithm for the generation of randomly disordered chemically heterogeneous surfaces and use it to explore the adsorption behavior of four model nonionic surfactants. Using novel analysis methods, we interrogate both the global surface coverage (adsorption isotherm) and behavior in localized regions. We observe that trends in adsorption characteristics as surfactant size, head/tail ratio, and surface topology are varied and connect these to underlying physical mechanisms. We believe that our methods and approach will prove useful to researchers seeking to tailor surface patterns to calibrate nonionic surfactant adsorption.

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 (SLESnEO), 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.

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 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 12 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 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.

Wax Formation in Linear and Branched Alkanes with Dissipative Particle Dynamics.

We present a dissipative particle dynamics (DPD) model for wax formation (i.e., the freezing transition) in linear and branched alkanes at room temperature (298 K) and atmospheric pressure. We parametrize the model using pure liquid phase densities and the onset of wax formation as a function of alkyl chain length. Significant emphasis is placed on building an accurate representation of the underlying molecular architecture by careful consideration of bond lengths and angles, aided by distributions obtained from molecular dynamics simulation. Using the derived model, we observe wax formation in n-alkanes when the alkyl chain length is greater than 18 (n-octadecane), in excellent agreement with experimental observations. Further, we reproduce the behavior of branched alkanes and mixtures including solubilities of heavy alkanes in light alkane solvents.

Efficient Algorithm for the Topological Characterization of Worm-like and Branched Micelle Structures from Simulations.

Many surfactant-based formulations are utilized in industry as they produce desirable viscoelastic properties at low concentrations. These properties are due to the presence of worm-like micelles (WLMs), and as a result, understanding the processes that lead to WLM formation is of significant interest. Various experimental techniques have been applied with some success to this problem but can encounter issues probing key microscopic characteristics or the specific regimes of interest. The complementary use of computer simulations could provide an alternate route to accessing their structural and dynamic behavior. However, few computational methods exist for measuring key characteristics of WLMs formed in particle simulations. Further, their mathematical formulations are challenged by WLMs with sharp curvature profiles or density fluctuations along the backbone. Here, we present a new topological algorithm for identifying and characterizing WLMs in particle simulations, which has desirable mathematical properties that address shortcomings in previous techniques. We apply the algorithm to the case of sodium dodecyl sulfate micelles to demonstrate how it can be used to construct a comprehensive topological characterization of the observed structures.

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

We present a machine learning approach to automated force field development in dissipative particle dynamics (DPD). The approach employs Bayesian optimization to parametrize a DPD force field against experimentally determined partition coefficients. The optimization process covers a discrete space of over 40 000 000 points, where each point represents the set of potentials that jointly forms a force field. We find that Bayesian optimization is capable of reaching a force field of comparable performance to the current state-of-the-art within 40 iterations. The best iteration during the optimization achieves an R2 of 0.78 and an RMSE of 0.63 log units on the training set of data, these metrics are maintained when a validation set is included, giving R2 of 0.8 and an RMSE of 0.65 log units. This work hence provides a proof-of-concept, expounding the utility of coupling automated and efficient global optimization with a top down data driven approach to force field parametrization. Compared to commonly employed alternative methods, Bayesian optimization offers global parameter searching and a low time to solution.

Constructing the phase diagram of sodium laurylethoxysulfate using dissipative particle dynamics.

HYPOTHESIS: Sodium Laurylethoxysulfate (SLES) is a fundamental ingredient in a wide range of surfactant products and the mapping of its various mesophases is pivotal in predicting the liquid viscosity. Here we want to show that the use of properly parameterised coarse-grained molecular models can provide structural information of the surfactant solutions not easily achievable through experimental characterization. EXPERIMENTS: We use a novel set of Dissipative Particle Dynamics parameters specifically developed for surfactant molecules to construct the first phase diagram of pure SLES in sodium chloride/water solutions. FINDINGS: We found that our DPD model is able to reproduce the range of morphologies expected for these types of ionic surfactants and in agreement with recent rheological data and theoretical predictions based on the packing parameter. We calculated the structure factor for various salt concentrations and show that the change from spherical to worm-like micelles can be inferred also looking at the intensity of the peak at intermediate q-values which decreases in intensity as salt concentrations increase. Varying the ethoxyl groups we observe that the additional ethoxyl group increased the micellar radius and affected the micelles' shape polydispersity in the system. Finally, based on the contour length of worm-like micelles observed at intermediate salt concentrations, a closed mathematical formula is proposed capable of predicting the average micellar contour length given the salt and surfactant concentrations.

Genome sequencing and transcriptome analyses of the Siberian hamster hypothalamus identify mechanisms for seasonal energy balance.

Synthesis of triiodothyronine (T3) in the hypothalamus induces marked seasonal neuromorphology changes across taxa. How species-specific responses to T3 signaling in the CNS drive annual changes in body weight and energy balance remains uncharacterized. These experiments sequenced and annotated the Siberian hamster (Phodopus sungorus) genome, a model organism for seasonal physiology research, to facilitate the dissection of T3-dependent molecular mechanisms that govern predictable, robust, and long-term changes in body weight. Examination of the Phodopus genome, in combination with transcriptome sequencing of the hamster diencephalon under winter and summer conditions, and in vivo-targeted expression analyses confirmed that proopiomelanocortin (pomc) is a primary genomic target for the long-term T3-dependent regulation of body weight. Further in silico analyses of pomc promoter sequences revealed that thyroid hormone receptor 1ß-binding motif insertions have evolved in several genera of the Cricetidae family of rodents. Finally, experimental manipulation of food availability confirmed that hypothalamic pomc mRNA expression is dependent on longer-term photoperiod cues and is unresponsive to acute, short-term food availability. These observations suggest that species-specific responses to hypothalamic T3, driven in part by the receptor-binding motif insertions in some cricetid genomes, contribute critically to the long-term regulation of energy balance and the underlying physiological and behavioral adaptations associated with the seasonal organization of behavior.

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

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 nE 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 ( Nagg). 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 nE 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 Nagg 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.

Micelle Formation in Alkyl Sulfate Surfactants Using Dissipative Particle Dynamics.

We use dissipative particle dynamics (DPD) to study micelle formation in alkyl sulfate surfactants, with alkyl chain lengths ranging from 6 to 12 carbon atoms. We extend our recent DPD force field [ J. Chem. Phys. 2017 , 147 , 094503 ] to include a charged sulfate chemical group and aqueous sodium ions. With this model, we achieve good agreement with the experimentally reported critical micelle concentrations (CMCs) and can match the trend in mean aggregation numbers versus alkyl chain length. We determine the CMC by fitting a charged pseudophase model to the dependence of the free surfactant on the total surfactant concentration above the CMC and compare it with a direct operational definition of the CMC as the point at which half of the surfactant is classed as micellar and half as monomers and submicellar aggregates. We find that the latter provides the best agreement with experimental results. Finally, with the same model, we are able to observe the sphere-to-rod morphological transition for sodium dodecyl sulfate (SDS) micelles and determine that it corresponds to SDS concentrations in the region of 300-500 mM.

In Vivo Effects of Retrobulbar Bimatoprost Injection on Orbital Fat.

PURPOSE: Recent publications have reported the adverse effects of prostaglandin analogues on the periocular tissues. These medications may cause periorbital lipodystrophy, enophthalmos, and deepening of the superior sulcus deformity. While these effects may have adverse consequences for some patients, the atrophy of the periorbital fat may have a useful role in diseases that lead to orbital and periorbital fat hypertrophy such as thyroid eye disease. In this pilot study, the authors investigated the effects of retrobulbar bimatoprost injection on the intraocular pressure and orbital fat in a rat animal model. METHODS: Three rats were sedated and intraocular pressure was measured. A 0.1 ml aliquot of bimatoprost was injected into the right orbit of all rats. In the left orbit, 0.1 ml of phosphate-buffered saline was injected as a control. Three weeks later, all rats were sedated and intraocular pressure was measured before euthanizing. Routine histologic staining was performed and thin sections through the intraconal orbital fat were obtained. Density of intraconal adipocytes was measured and adipocyte heterogeneity was determined using a computer image analysis algorithm. RESULTS: The specimens injected with bimatoprost demonstrated atrophy of orbital fat with significantly increased adipocyte density (p = 0.009) and heterogeneity (p = 0.008) when compared with control. Intraocular pressure was not significantly decreased at 3 weeks after injection of retrobulbar bimatoprost. CONCLUSIONS: In this pilot study, orbital injection of bimatoprost demonstrated atrophy of intraconal adipocytes when compared with control orbits injected with saline. The orbits injected with bimatoprost were noted to have smaller, more heterogeneous adipocytes that were densely packed in the intraconal space. The study limitations include the small sample size, which limited the ability for us to make conclusions about the effect on intraocular pressure. Nevertheless, the findings presented suggest that retrobulbar bimatoprost may present a nonsurgical alternative to induce atrophy of the orbital fat without inducing inflammation or hypotony.

Dissipative particle dynamics: Systematic parametrization using water-octanol partition coefficients.

We present a systematic, top-down, thermodynamic parametrization scheme for dissipative particle dynamics (DPD) using water-octanol partition coefficients, supplemented by water-octanol phase equilibria and pure liquid phase density data. We demonstrate the feasibility of computing the required partition coefficients in DPD using brute-force simulation, within an adaptive semi-automatic staged optimization scheme. We test the methodology by fitting to experimental partition coefficient data for twenty one small molecules in five classes comprising alcohols and poly-alcohols, amines, ethers and simple aromatics, and alkanes (i.e., hexane). Finally, we illustrate the transferability of a subset of the determined parameters by calculating the critical micelle concentrations and mean aggregation numbers of selected alkyl ethoxylate surfactants, in good agreement with reported experimental values.

Body weight loss, effective satiation and absence of homeostatic neuropeptide compensation in male Sprague Dawley rats schedule fed a protein crosslinked diet.

Food structure contributes to the induction of satiation and the maintenance of satiety following intake of a meal. There is evidence from human studies that protein-crosslinking of a milk-protein based meal may enhance satiety, but the mechanism underpinning this effect is unknown. We investigated whether a rat model would respond in a similar manner and might provide mechanistic insight into enhanced satiety by structural modification of a food source. Rats were schedule fed a modified AIN-93M based diet in a liquid form or protein-crosslinked to produce a soft-solid form. This was compared to a modified AIN-93M solid diet. Average daily caloric intake was in the order solid > liquid > crosslinked. Body composition was unaltered in the solid group, but there was a loss of fat in the liquid group and a loss of lean and fat tissue in the crosslinked group. Compared to rats fed a solid diet, acute responses in circulating GLP-1, leptin and insulin were eliminated or attenuated in rats fed a liquid or crosslinked diet. Quantification of homeostatic neuropeptide expression in the hypothalamus showed elevated levels of Npy and Agrp in rats fed the liquid diet. Measurement of food intake after a scheduled meal indicated that reduced energy intake of liquid and crosslinked diets is not due to enhancement of satiety. When continuously available ad-libitum, rats fed a liquid diet showed reduced weight gain despite greater 24 h caloric intake. During the dark phase, caloric intake was reduced, but compensated for during the light phase. We conclude that structural modification from a liquid to a solidified state is beneficial for satiation, with less of a detrimental effect on metabolic parameters and homeostatic neuropeptides.

Osteolytic Sarcoidosis of the Orbital Roof Masquerading as a Malignant Orbital Lesion.

A 51-year-old man without a significant past medical history presented with 4 weeks of progressive swelling and drooping of his left upper eyelid. A CT of the left orbit revealed an osteolytic mass lesion in the area of the lacrimal gland. A left orbitotomy with excisional biopsy was performed. The excised tissue was sent for infectious workup and histopathological examination, which revealed osteolytic sarcoidosis. The patient was treated with systemic and local injection corticosteroids, and followed over 10 months without evidence of recurrence. Systemic workup with CT of his chest, abdomen, and pelvis revealed no further evidence of sarcoidosis. To the best of the authors knowledge, this is the first report of an otherwise healthy patient presenting with isolated osteolytic sarcoidosis of the orbit and a negative systemic workup.