Oil binding capacities of triacylglycerol crystalline nanoplatelets: nanoscale models of tristearin solids in liquid triolein.

Polycrystalline particles composed of triacylglycerol (TAG) molecules, and their networks, in anhydrous TAG oils find extensive use as edible oils in the food industry. Although modelling studies of TAG systems, have been carried out, none have attempted to address a problem of central concern to food science and technology: the "oil binding capacity" of a system of such edible oils. Crystalline nanoparticles (CNPs) have recently been identified as the fundamental components of solid fats in oils. Oil binding capacity is an important concept regarding the ability of fats particles to retain oil, and the ability of these CNPs to bind oil is important in designing healthy foods. We have carried out atomic scale molecular dynamics computer simulations to understand the behavior of a triacylglycerol oil (triolein) in nanoscale confinements between tristearin CNPs. We define a nanoscale oil binding capacity function by utilizing the average oil number density, 〈Φ(d)〉, between two CNPs as a function of their separation, d. We modelled pure tristearin CNPs as well as tristearin CNPs in which the surfaces are covered with an interface comprising soft permanent coatings. Their surfaces are "hard" and "soft" respectively. We found that for a pair of hard-surface tristearin CNPs a distance d apart, (i) triolein exhibits number density, and therefore density, oscillations as a function of d, and (ii) the average number density between two such CNPs decreases as d decreases, viz. the oil binding capacity is lowered. When a soft layer of oil covers the CNP surfaces, we found that the oscillations are smeared out and that the average number density between the two CNPs remained approximately constant as d decreased indicating a high oil binding capacity. Our results might have identified important nanoscale aspects to aid in healthy food design.

Nanoscale characteristics of triacylglycerol oils: phase separation and binding energies of two-component oils to crystalline nanoplatelets.

Fats are elastoplastic materials with a defined yield stress and flow behavior and the plasticity of a fat is central to its functionality. This plasticity is given by a complex tribological interplay between a crystalline phase structured as crystalline nanoplatelets (CNPs) and nanoplatelet aggregates and the liquid oil phase. Oil can be trapped within microscopic pores within the fat crystal network by capillary action, but it is believed that a significant amount of oil can be trapped by adsorption onto crystalline surfaces. This, however, remains to be proven. Further, the structural basis for the solid-liquid interaction remains a mystery. In this work, we demonstrate that the triglyceride liquid structure plays a key role in oil binding and that this binding could potentially be modulated by judicious engineering of liquid triglyceride structure. The enhancement of oil binding is central to many current developments in this area since an improvement in the health characteristics of fat and fat-structured food products entails a reduction in the amount of crystalline triacylglycerols (TAGs) and a relative increase in the amount of liquid TAGs. Excessive amounts of unbound, free oil, will lead to losses in functionality of this important food component. Engineering fats for enhanced oil binding capacity is thus central to the design of more healthy food products. To begin to address this, we modelled the interaction of triacylglycerol oils, triolein (OOO), 1,2-olein elaidin (OOE) and 1,2-elaidin olein (EEO) with a model crystalline nanoplatelet composed of tristearin in an undefined polymorphic form. The surface of the CNP in contact with the oil was assumed to be planar. We considered pure OOO and mixtures of OOO + OOE and OOO + EEO with 80% OOO. The last two cases were taken as approximations to high oleic sunflower oil (HOSO). The intent was to investigate whether phase separation on a nanoscale took place. We defined an "oil binding capacity" parameter, B(Q,Q'), relating a state Q to a reference state Q'. We used atomic scale molecular dynamics in the NVT ensemble and computed averages over 1-5 ns. We found that the probability of the OOE phase separating into a layer on the surface of the CNP compared to being retained randomly in an OOO + OOE mix were approximately equal. However, we found that it was probable that the EEO component of an OOO + EEO mix would phase separate and coat the surface of the CNP. These results suggest a mechanism whereby many-component oils undergo phase separation on a nanoscale so as to create a transition oil region between the surface of the CNP and the bulk major oil component (OOO in the case considered here) so as to create the appropriate oil binding capacity for the use to which it is put.

Simulations of a lattice model of two-headed linear amphiphiles: influence of amphiphile asymmetry.

Using a 2D lattice model, we conduct Monte Carlo simulations of micellar aggregation of linear-chain amphiphiles having two solvophilic head groups. In the context of this simple model, we quantify how the amphiphile architecture influences the critical micelle concentration (CMC), with a particular focus on the role of the asymmetry of the amphiphile structure. Accordingly, we study all possible arrangements of the head groups along amphiphile chains of fixed length N = 12 and 16 molecular units. This set of idealized amphiphile architectures approximates many cases of symmetric and asymmetric gemini surfactants, double-headed surfactants, and boloform surfactants. Consistent with earlier results, we find that the number of spacer units s separating the heads has a significant influence on the CMC, with the CMC increasing with s for s < N/2. In comparison, the influence of the asymmetry of the chain architecture on the CMC is much weaker, as is also found experimentally.

Spatial correlation of the dynamic propensity of a glass-forming liquid.

We present computer simulation results on the dynamic propensity (as defined by Widmer-Cooper et al 2004 Phys. Rev. Lett. 93 135701) in a Kob-Andersen binary Lennard-Jones liquid system consisting of 8788 particles. We compute the spatial correlation function for the dynamic propensity as a function of both the reduced temperature T, and the time scale on which the particle displacements are measured. For T ≤ 0.6, we find that non-zero correlations occur at the largest length scale accessible in our system. We also show that a cluster-size analysis of particles with extremal values of the dynamic propensity, as well as 3D visualizations, reveal spatially correlated regions that approach the size of our system as T decreases, consistently with the behavior of the spatial correlation function. Next, we define and examine the 'coordination propensity', the isoconfigurational average of the coordination number of the minority B particles around the majority A particles. We show that a significant correlation exists between the spatial fluctuations of the dynamic and coordination propensities. In addition, we find non-zero correlations of the coordination propensity occurring at the largest length scale accessible in our system for all T in the range 0.466 < T < 1.0. We discuss the implications of these results for understanding the length scales of dynamical heterogeneity in glass-forming liquids.

Spectral statistics of the quenched normal modes of a network-forming molecular liquid.

We evaluate the density of states of the quenched normal modes of ST2 water, and their statistical fluctuations, for a range of densities spanning three regimes of behavior of a hydrogen bonded liquid: a lower-density regime of random tetrahedral network formation, in the vicinity of a liquid-liquid critical point, and in a higher-density regime of fragile glass-forming behavior. For all cases we find that the fluctuations around the mean spectral densities obey the predictions of the Gaussian orthogonal ensemble of random matrix theory. We also measure the participation ratio of the normal modes across the entire frequency range, and find behavior consistent with the majority of modes being of an extended nature, rather than localized.