9 research outputs found

    Molecular modeling of the interface of an egg yolk protein-based emulsion

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    Many food emulsions are stabilized by functional egg yolk biomolecules, which act as surfactants at the oil/water interface. Detailed experimental studies on egg yolk emulsifying properties have been largely hindered due to the difficulty in isolating individual chemical species. Therefore, this work presents a molecular model of an oil/water interfacial system where the emulsifier is one of the most surface-active proteins from the egg yolk low-density lipoproteins (LDL), the so-called Apovitellenin I. Dissipative particle dynamics (DPD) was here adopted in order to simulate large systems over long time scales, when compared with full-atom molecular dynamics (MD). Instead of a manual assignment of the DPD simulation parameters, a fully automated coarse-graining procedure was employed. The molecular interactions used in the DPD system were determined by means of a parameter calibration based on matching structural data from atomistic MD simulations. Despite the little availability of experimental data, the model was designed to test the most relevant physical properties of the protein investigated. Protein structural and dynamics properties obtained via MD and DPD were compared highlighting advantages and limits of each molecular technique. Promising results were achieved from DPD simulations of the oil/water interface. The proposed model was able to properly describe the protein surfactant behavior in terms of interfacial tension decrease at increasing protein surface concentration. Moreover, the adsorption time of a free protein molecule was estimated and, finally, an LDL-like particle adsorption mechanism was qualitatively reproduced

    Multiscale simulation of a high-shear mixer for food emulsion production

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    Food emulsions, such as mayonnaise, are made of a continuous water phase, a dispersed phase with a high content of oil, and a surfactant (i.e. the egg yolk for mayonnaise) that stabilizes the oil drops. The droplet size distribution (DSD) is the most important property of the emulsion since the structure, stability, taste, and color of the final product depend on the DSD. The DSD in turn depends on the emulsion composition, the type of process, and the operating conditions in which the production process operates. The production of emulsions is based on mixing the ingredients and applying enough mechanical energy to the emulsion, to reach the desired DSD. In particular, for the food emulsion investigated in this work, i.e. the mayonnaise, a typical mixing process is composed of two steps (Figure 1). First, the ingredients (mainly egg yolk, vinegar, oil, water, salt) are mixed together in large stirred vessels at a moderate rotational speed. Then, this premixed emulsion is finally fluxed into a high-shear device, commonly a cone mill mixer, where the oil droplets undergo breakage until the final size distribution is reached. This last step is crucial to fine-tune the DSD, in order to determine the properties of the final product. A typical cone mill is constituted of a solid conical frustum rotor inside a slightly larger stator of the same shape, forming a small gap in which the emulsion flows and experiences high shear stresses, due to the high rotational speed of the rotor. Within the multiscale framework, different time- and space- scales are investigated to describe the modeling approach for the macro-scale (cone mill) and the molecular scale (oil-water interface). Computational fluid dynamics (CFD) simulations are employed to properly describe the non-Newtonian dynamics of the emulsion, investigate the role of the pre- and post-mixing zones and clarify the importance of the type of flow, namely pure-shear versus elongational. In order to describe the evolution of the droplet size distribution, the Population Balance Modelling (PBM) is employed, in which coalescence and breakage of oil droplets are taken into account by appropriate kernels, which depend on local flow conditions. During the emulsification process, the interfacial properties between dispersed and continuous phases have an essential role in the formation and stabilization of the oil droplets. Once the chemical composition of mayonnaise is determined, especially the biomolecules acting as surfactants, the interfacial tension between the two phases is directly computed with the help of atomistic techniques, such as Molecular Dynamics (MD) and Dissipative Particle Dynamics (DPD). This mesoscale approach also provides the surfactant adsorption kinetics and its molecular conformation at the interface, paving the way for a better understanding of the breakage and coalescence events of the oil droplets occurring in the production process. This information can be eventually transferred to the CFD-PBM simulations thus achieving a complete, general, and multi-scale model of the food emulsion production process. This effort is carried out in the context of the VIMMP project (www.vimmp.eu), where the entire workflow will serve to devise a marketplace for generic multiscale and multiphysics simulations. The VIMMP project has received funding from the European Union’s Horizon 2020 Research Innovation Programme under Grant Agreement n. 760907

    Ab initio molecular dynamics study of liquid methanol

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    We present a density-functional theory based molecular-dynamics study of the structural, dynamical, and electronic properties of liquid methanol under ambient conditions. The calculated radial distribution functions involving the oxygen and hydroxyl hydrogen show a pronounced hydrogen bonding and compare well with recent neutron diffraction data, except for an underestimate of the oxygen-oxygen correlation. We observe that, in line with infrared spectroscopic data, the hydroxyl stretching mode is significantly red-shifted in the liquid. A substantial enhancement of the dipole moment is accompanied by significant fluctuations due to thermal motion. Our results provide valuable data for improvement of empirical potentials.Comment: 14 pages, 4 figures, accepted for publication in Chemical Physics Letter

    Method of Moments for Computational Microemulsion Analysis and Prediction in Tertiary Oil Recovery

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    We discuss the application of Helfrich’s surface torque density concept to microemulsion design and analysis from three different angles: (i) from the point of view of coarse-grained molecular simulations, using Dissipative Particle Dynamics, including charge interactions and added salt, (ii) using an approximate double-film model for the surface, and (iii) comparison with formulation approaches. The simulations use that the surface torque can be calculated unambiguously from the stress profile, provided the surface is tensionless. Very good agreement is found on predicting optimal salinity (or the absence of that) for a range of surfactants: dioctyl sodium sulfosuccinate, various twin-tailed sulfonates and sodium dodecyl sulfate. The simulations are very fast, on par with times for experiments, thus they could lead to a practical tool for discovery of more efficient surfactants, although much remains to be done with respect to other important variables: oil composition, surfactant mixtures, aggregation in solution, and so on. The microscopic model (second approach) is highly approximate: it is essentially based on two opposing swelling tendencies, that are both of osmotic nature. In accordance with the model, the tails are swollen by the oil and the charged head groups are confined in a salty layer in Donnan equilibrium with the salt solution. In this way, the surface interactions are purely entropic. The comparison of the film model with existing formulation approaches (third approach) covers the interfacial tension minimum, Winsor R theory, quantitative structure property relations (QSPR), hydrophilic–lipophilic deviation (HLD), HLD-net average curvature, and temperature coefficients. Using the surface torque analysis, we succeed in deriving in an ab initio way QSPR empirical coefficients that have been known for decades, but until now, have been obscure in origin

    Structure of Self-Aggregated Alamethicin in ePC Membranes Detected by Pulsed Electron-Electron Double Resonance and Electron Spin Echo Envelope Modulation Spectroscopies

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    PELDOR spectroscopy was exploited to study the self-assembled super-structure of the [Glu(OMe)7,18,19]alamethicin molecules in vesicular membranes at peptide to lipid molar ratios in the range of 1:70–1:200. The peptide molecules were site-specifically labeled with TOAC electron spins. From the magnetic dipole-dipole interaction between the nitroxides of the monolabeled constituents and the PELDOR decay patterns measured at 77 K, intermolecular-distance distribution functions were obtained and the number of aggregated molecules (n ≈ 4) was estimated. The distance distribution functions exhibit a similar maximum at 2.3 nm. In contrast to Alm16, for Alm1 and Alm8 additional maxima were recorded at 3.2 and ∌5.2 nm. From ESEEM experiments and based on the membrane polarity profiles, the penetration depths of the different spin-labeled positions into the membrane were qualitatively estimated. It was found that the water accessibility of the spin-labels follows the order TOAC-1 > TOAC-8 ≈ TOAC-16. The geometric data obtained are discussed in terms of a penknife molecular model. At least two peptide chains are aligned parallel and eight ester groups of the polar Glu(OMe)18,19 residues are suggested to stabilize the self-aggregate superstructure
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