Complexation Mechanisms of Aqueous Amylose: Molecular Dynamics Study Using 3-Pentadecylphenol.

Molecular dynamics (MD) simulations of linear amylose fragments containing 10 to 40 glucose units were used to study the complexation of the prototypical compound, 3-pentadecylphenol (PDP)─a natural product with surfactant-like properties─in aqueous solution. The amylose-PDP binding leverages mainly hydrophobic interactions together with excluded volume effects. It was found that while the most stable complexes contained PDP inside the helical structure of the amylose in the expected guest-host (inclusion) complexation manner, at higher temperatures, the commonly observed PDP-amylose complexes often involved more nonspecific interactions than inclusion complexation.

Nanoscale Simulation of Plastic Contaminants Migration in Packaging Materials and Potential Leaching into Model Food Fluids.

Polymers are the most commonly used packaging materials for nutrition and consumer products. The ever-growing concern over pollution and potential environmental contamination generated from single-use packaging materials has raised safety questions. Polymers used in these materials often contain impurities, including unreacted monomers and small oligomers.

Drug Aggregation of Sparingly-Soluble Ionizable Drugs: Molecular Dynamics Simulations of Papaverine and Prostaglandin F2α.

Aggregation in aqueous solution can have important implications on both the exposure of a drug and its pharmaceutical manufacturability. However, the drug aggregates formed can be very small and, thus, difficult to interrogate experimentally. On the other hand, at higher supersaturations where larger aggregates are supported, the chemical system is inherently metastable and therefore likewise challenging to study from an experimental standpoint.

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.

Shearing friction behaviour of synthetic polymers compared to a functionalized polysaccharide on biomimetic surfaces: models for the prediction of performance of eco-designed formulations.

The substitution of natural, bio-based and/or biodegradable polymers for those of petrochemical origin in consumer formulations has become an active area of research and development as the sourcing and destiny of material components becomes a more critical factor in product design. These polymers often differ from their petroleum-based counterparts in topology, raw material composition and solution behaviour. Effective and efficient reformulation that maintains comparable cosmetic performance to existing products requires a deep understanding of the differences in frictional behaviour between polymers as a function of their molecular structure.

Obtaining and Characterizing Stable Bicontinuous Cubic Morphologies and Their Nanochannels in Lyotropic Liquid Crystal Membranes.

Amphiphilic monomers in polar solvents can self-assemble into lyotropic liquid crystal (LLC) bicontinuous cubic structures under the right composition and temperature conditions. After cross-linking, the resulting polymer membranes with three-dimensional (3D) continuous uniform channels are excellent candidates for filtration applications. Designing such membranes with the desired physical and chemical properties requires molecular-level understanding of the structure, which can be obtained through molecular modeling.

Statistical Inference of Transport Mechanisms and Long Time Scale Behavior from Time Series of Solute Trajectories in Nanostructured Membranes.

Appropriate time series modeling of complex diffusion in soft matter systems on the microsecond time scale can provide a path toward inferring transport mechanisms and predicting bulk properties characteristic of much longer time scales. In this work we apply nonparametric Bayesian time series analysis, more specifically the sticky hierarchical Dirichlet process autoregressive hidden Markov model (HDP-AR-HMM) to solute center-of-mass trajectories generated from long molecular dynamics (MD) simulations in a cross-linked inverted hexagonal phase lyotropic liquid crystal (LLC) membrane in order to automatically detect a variety of solute dynamical modes. We can better understand the mechanisms controlling these dynamical modes by grouping the states identified by the HDP-AR-HMM into clusters based on multiple metrics aimed at distinguishing solute behavior based on their fluctuations, dwell times in each state, and positions within the inhomogeneous membrane structure.

Capturing Subdiffusive Solute Dynamics and Predicting Selectivity in Nanoscale Pores with Time Series Modeling.

Fitting mathematical models with a direct connection to experimental observables to the outputs of molecular simulations can be a powerful tool for extracting important physical information from them. In this study, we present two new approaches that use stochastic time series modeling to predict long-time-scale behavior and macroscopic properties from molecular simulation, which can be generalized to other molecular systems where complex diffusion occurs. In our previous work, we studied long molecular dynamics (MD) simulation trajectories of a cross-linked H phase lyotropic liquid crystal (LLC) membrane, where we observed subdiffusive solute transport behavior characterized by intermittent hops separated by periods of entrapment.

Chemically Selective Transport in a Cross-Linked H Phase Lyotropic Liquid Crystal Membrane.

The uniform size and complex chemical topology of the pores formed by self-assembled amphiphilic molecules such as liquid crystals make them promising candidates for selective separations. In this work, we observe the transport of water, sodium ions, and 20 small polar solutes within the pores of a lyotropic liquid crystal (LLC) membrane using atomistic molecular simulations. We find that the transport of a species is dependent not only on molecular size but also on chemical functionality.

Understanding the Nanoscale Structure of Inverted Hexagonal Phase Lyotropic Liquid Crystal Polymer Membranes.

Periodic, nanostructured porous polymer membranes made from the cross-linked inverted hexagonal phase of self-assembled lyotropic liquid crystals (LLCs) are a promising class of materials for selective separations. In this work, we investigate an experimentally characterized LLC polymer membrane using atomistic molecular modeling. In particular, we compare simulated X-ray diffraction (XRD) patterns with experimental XRD data to quantify and understand the differences between simulation and experiment.