Exploring the Effects of Wetting and Free Fatty Acid Deposition on an Atomistic Hair Fiber Surface Model Incorporating Keratin-Associated Protein 5-1.

The complex development of cosmetic and medical formulations relies on an ever-growing accuracy of predictive models of hair surfaces. Hitherto, modeling efforts have focused on the description of 18-methyl eicosanoic acid (18-MEA), the primary fatty acid covalently attached to the hair surface, without explicit modeling of the protein layer. Herein, the molecular details of the outermost surface of the human hair fiber surface, also called the F-layer, were studied using molecular dynamics (MD) simulations. The F-layer is composed primarily of keratin-associated proteins KAP5 and KAP10, which are decorated with 18-MEA on the outer surface of a hair fiber. In our molecular model, we incorporated KAP5-1 and evaluated the surface properties of 18-MEA through MD simulations, resulting in 18-MEA surface density, layer thickness, and tilt angles in agreement with previous experimental and computational studies. Subsequent models with reduced 18-MEA surface density were also generated to mimic damaged hair surfaces. Response to wetting of virgin and damaged hair showed rearrangement of 18-MEA on the surface, allowing for water penetration into the protein layer. To demonstrate a potential use case for these atomistic models, we deposited naturally occurring fatty acids and measured 18-MEA's response in both dry and wet conditions. As fatty acids are often incorporated in shampoo formulations, this work demonstrates the ability to model the adsorption of ingredients on hair surfaces. This study illustrates, for the first time, the complex behavior of a realistic F-layer at the molecular level and opens up the possibility of studying the adsorption behavior of larger, more complex molecules and formulations.

Colloidal assembly by directional ice templating.

We investigate directional ice templating of dilute aqueous colloidal particle dispersions and examine the nature of the assemblies that result. We coat micron-size polystyrene colloids with cross-linkable polymer (polyethyleneimine, PEI), add cross-linker, and subject this dispersion to unidirectional freezing. We work at sufficiently low colloid concentrations, such that the particles do not percolate on freezing. When the aqueous dispersion freezes, ice crystals force polymer-coated particles and cross-linker into close proximity. This results in the formation of cross-linked clusters of particles at ice crystal boundaries. We vary the particle volume fraction from φ ∼ 2.5 × 10-3 to φ ∼ 5.0 × 10-2 and observe that there is a transition from isolated single particles to increasingly large sized clusters. Most of the clusters formed under these conditions are either linear, two-particle wide chains, or sheet-like aggregates. The probability (Pn) of clusters containing n particles (n > 2) obeys a power law Pn ∼ n-η, where η strongly depends on the particle concentration in the dispersion, varying from 2.10 (for φ ∼ 5.0 × 10-2) to 3.03 (for φ ∼ 2.5 × 10-3). This change in η is qualitatively different from the case of isotropic freezing, where η is particle concentration-independent and depends only on the ice nucleation density. To understand the differences between isotropic and directional ice templating, we performed lattice simulations of a highly simplified model, where ice crystals grow at a constant rate to force clustering. We ignore hydrodynamic interactions and ice growth instabilities. Despite ignoring these experimental details, the simulations capture the experimental results, nearly quantitatively. As the ice crystals grow and the space available to the colloids "closes up" so that the particles cluster to form aggregates, crystallization protocol-induced differences in the geometry of these "closed up" spaces determine the scaling behaviour of Pn.

Characterizing moisture uptake and plasticization effects of water on amorphous amylose starch models using molecular dynamics methods.

Dynamics and thermophysical properties of amorphous starch were explored using molecular dynamics (MD) simulations. Using the OPLS3e force field, simulations of short amylose chains in water were performed to determine force field accuracy. Using well-tempered metadynamics, a free energy map of the two glycosidic angles of an amylose molecule was constructed and compared with other modern force fields. Good agreement of torsional sampling for both solvated and amorphous amylose starch models was observed. Using combined grand canonical Monte Carlo (GCMC)/MD simulations, a moisture sorption isotherm curve is predicted along with temperature dependence. Concentration-dependent activation energies for water transport agree quantitatively with previous experiments. Finally, the plasticization effect of moisture content on amorphous starch was investigated. Predicted glass transition temperature (Tg) depression as a function of moisture content is in line with experimental trends. Further, our calculations provide a value for the dry Tg for amorphous starch, a value which no experimental value is available.

Thermal Stability of π-Conjugated n-Ethylene-Glycol-Terminated Quaterthiophene Oligomers: A Computational and Experimental Study.

This work represents a joint computational and experimental study on a series of n-ethylene glycol (PEOn)-terminated quaterthiophene (4T) oligomers for 1 < n < 10 to elucidate their self-assembly behavior into a smectic-like lamellar phase. This study builds on an earlier study for n = 4 that showed that our model predictions were consistent with experimental data on the melting behavior and structure of the lamellar phase, with the latter consisting of crystal-like 4T domains and liquid-like PEO4 domains. The present study aims to understand how the length of the terminal PEOn chains modulates the disordering temperature of the lamellar phase and hence the relative stability of the ordered structure. A simplified bilayer model, where the 4T domains are not explicitly described, is put forward to efficiently estimate the disordering effect of the PEO domains with increasing n; this method is first validated by correctly predicting that layers of alkyl (PE)-capped 4T oligomers (for 1 < n < 10) stay ordered at room temperature. Both 4T-domain implicit and explicit model simulations reveal that the order-disorder temperature decreases with the length of the PEO capping chains, as the associated increase in conformational entropy drives a tendency toward disorder that overtakes the cohesive energy, keeping the ordered packing of the 4T domains.

Framework for Inverse Mapping Chemistry-Agnostic Coarse-Grained Simulation Models into Chemistry-Specific Models.

Coarse-grained (CG) models have allowed molecular simulations to access large enough time and length scales to elucidate relationships between macroscale properties and microscale molecular interactions. However, an unaddressed inverse-design problem concerns the identification of an optimal chemistry-specific (CS) molecule that the generic CG model represents. This has been addressed here by introducing new tools for automatically generating and refining the mapping of CS-molecule candidates to the constraints of a CG model, based on representative optimization criteria. With these tools, for each CS-molecule from a candidate group, the best mapping of that molecule onto the CG model is found and their fit is assessed by an objective function designed to emphasize matching key properties of the CG model. We employ this methodology to a range of CG models from small solvent molecules up to block copolymer systems to show its ability to find optimal candidates and to uncover the underlying length scale of some of the CG models. For instances where the identity of the CG model is known a priori, the methodology identifies the correct AA chemistry. For instances where the identity is unknown and a pool of candidates is provided, the method selects a chemistry that aligns well with physical intuition. The best candidate chemistry is also found to be sensitive to changes to the CG model.

Structure Control of a π-Conjugated Oligothiophene-Based Liquid Crystal for Enhanced Mixed Ion/Electron Transport Characteristics.

Developing soft materials with both ion and electron transport functionalities is of broad interest for energy-storage and bioelectronics applications. Rational design of these materials requires a fundamental understanding of interactions between ion and electron conducting blocks along with the correlation between the microstructure and the conduction characteristics. Here, we investigate the structure and mixed ionic/electronic conduction in thin films of a liquid crystal (LC) 4T/PEO4, which consists of an electronically conducting quarterthiophene (4T) block terminated at both ends by ionically conducting oligoethylenoxide (PEO4) blocks. Using a combined experimental and simulation approach, 4T/PEO4 is shown to self-assemble into smectic, ordered, or disordered phases upon blending the materials with the ionic dopant bis(trifluoromethane)sulfonimide lithium (LiTFSI) under different LiTFSI concentrations. Interestingly, at intermediate LiTFSI concentration, ordered 4T/PEO4 exhibits an electronic conductivity as high as 3.1 × 10-3 S/cm upon being infiltrated with vapor of the 2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane (F4TCNQ) molecular dopant while still maintaining its ionic conducting functionality. This electronic conductivity is superior by an order of magnitude to the previously reported electronic conductivity of vapor co-deposited 4T/F4TCNQ blends. Our findings demonstrate that structure and electronic transport in mixed conduction materials could be modulated by the presence of the ion transporting component and will have important implications for other more complex mixed ionic/electronic conductors.

Molecular dynamics simulation of thermotropic bolaamphiphiles with a swallow-tail lateral chain: formation of cubic network phases.

T-shaped bolaamphiphiles (TBA) with a swallow-tail lateral chain have been found to provide a fertile platform to produce complex liquid crystalline phases that are accessible through changes of temperature and lateral chain length and design. In this work, we use molecular simulations of a simple coarse-grained model to map out the phase behavior of this type of molecules. This model is based on the premise that the crucial details of the fluid structure stem from close range repulsions and the strong directional forces typical of hydrogen bonds. Our simulations confirm that TBAs exhibit a rich phase behavior upon increasing the length of their lateral chain. The simulations detect a double gyroid phase and an axial-bundle columnar phase which bear some structural resemblance to those found in the experiment. In addition, simulations predict two cocontinuous phases with 3D-periodicity: the "single" diamond and the "single" plumber's nightmare phase. Our analysis of energetic and entropic contributions to the free energy of phases formed by TBA with either swallow-tail or linear side-chains suggest that the 3D-periodic network phases formed by the former are stabilized by the large conformation entropy of the side-chains.

Using Time-Temperature Superposition for Determining Dielectric Loss in Functionalized Polyethylenes.

Using Molecular Dynamics simulations, we probe the effect of various pendant polar groups on the dielectric loss of polyethylene copolymers. The dielectric loss was computed using the autocorrelation function of the total dipole moment of a completely relaxed PE-X sample. Since this calculation is computationally expensive (wall time ≥ 4200 h), we explore the use of the time-temperature superposition (tTS) principle to make it more tractable. An important point is that short time MD simulations do not allow the dipole autocorrelation function to decay completely to zero. However, we find that the tTS method performed well in determining dielectric losses in the system as long as these unrelaxed components are not included in the calculation. This methodology, which provides us with a significantly faster and reliable pathway for calculation of dielectric loss, allows us to identify the role of polar side groups on the behavior of nonpolar polymeric dielectrics.

Critical role of morphology on the dielectric constant of semicrystalline polyolefins.

A particularly attractive method to predict the dielectric properties of materials is density functional theory (DFT). While this method is very popular, its large computational requirements allow practical treatments of unit cells with just a small number of atoms in an ordered array, i.e., in a crystalline morphology. By comparing DFT and Molecular Dynamics (MD) simulations on the same ordered arrays of functional polyolefins, we confirm that both methodologies yield identical estimates for the dipole moments and hence the ionic component of the dielectric storage modulus. Additionally, MD simulations of more realistic semi-crystalline morphologies yield estimates for this polar contribution that are in good agreement with the limited experiments in this field. However, these predictions are up to 10 times larger than those for pure crystalline simulations. Here, we show that the constraints provided by the surrounding chains significantly impede dipolar relaxations in the crystalline regions, whereas amorphous chains must sample all configurations to attain their fully isotropic spatial distributions. These results, which suggest that the amorphous phase is the dominant player in the context, argue strongly that the proper polymer morphology needs to be modeled to ensure accurate estimates of the ionic component of the dielectric constant.