The effect of boundary conditions on the mesoscopic lattice Boltzmann method: Case study of a reaction-diffusion based model for Min-protein oscillation

Min-protein oscillation in Escherichia coli has an essential role in controlling the accurate placement of the cell division septum at the middle-cell zone of the bacteria. This biochemical process has been successfully described by a set of reaction-diffusion equations at the macroscopic level. The lattice Boltzmann method (LBM) has been used to simulate Min-protein oscillation and proved to recover the correct macroscopic equations. In this present work, we studied the effects of LBM boundary conditions (BC) on Min-protein oscillation. The impact of diffusion and reaction dynamics on BCs was also investigated. It was found that the mirror-image BC is a suitable boundary treatment for this Min-protein model. The physical significance of the results is extensively discussed.     

Computational analysis of the roles of biochemical reactions in anomalous diffusion dynamics

Most biochemical processes in cells are usually modeled by reaction–diffusion(RD) equations. In these RD models,the diffusive process is assumed to be Gaussian. However, a growing number of studies have noted that intracellular diffusion is anomalous at some or all times, which may result from a crowded environment and chemical kinetics. This work aims to computationally study the effects of chemical reactions on the diffusive dynamics of RD systems by using both stochastic and deterministic algorithms. Numerical method to estimate the mean-square displacement(MSD) from a deterministic algorithm is also investigated. Our computational results show that anomalous diffusion can be solely due to chemical reactions. The chemical reactions alone can cause anomalous sub-diffusion in the RD system at some or all times.The time-dependent anomalous diffusion exponent is found to depend on many parameters, including chemical reaction rates, reaction orders, and chemical concentrations.     

Stability studies of high-stable water-in-oil model emulsions

Different compositions and emulsification protocols were used to prepare stable water-in-oil (w/o) emulsions. Water, mineral oil, and a mixture of Span 80 and Tween 80 surfactants were combined to form emulsions that can be used as reference for electrolyte-free systems. Here, we have proposed emulsions wherein different properties were evaluated. Electrical conductivity measurements indicated that conductivity increases linearly with increasing surfactant content. The emulsions’ flow curves and viscoelastic behaviors were delineated by rheological measurements. Stability studies by centrifugal testing have shown that smaller the surfactant content, lower the stability, for any used stirring speeds. Furthermore, higher the applied mixing rate to make the emulsion, higher the stability, regardless of the amount of surfactant. Electrical field stability analysis showed, for all systems, that critical electric field (CEF) values were dependent on either surfactant amount and emulsion elastic modulus.     

Phase equilibrium data and thermodynamic modelling of the system (propane + DMF + methanol) at high pressures

Reported in this work are phase equilibrium data at high pressures for the binary and ternary systems formed by {propane + N,N-dimethylformamide (DMF) + methanol}. Phase equilibrium measurements were performed in a high-pressure, variable-volume view cell, following the static synthetic method for obtaining the experimental bubble and dew points transition data over the temperature range of (363 to 393) K, pressures up to 11.5 MPa and overall mole fraction of the lighter component varying from 0.1 to 0.995. For the systems investigated, vapour–liquid (VLE), liquid–liquid (LLE) and vapour–liquid–liquid (VLLE) phase transitions were visually recorded. Results show that the systems investigated present UCST (upper critical solution temperature) phase transition curves with an UCEP (upper critical end point) at a temperature higher than the propane critical temperature. The experimental data were modelled using the Peng–Robinson equation of state with the Wong–Sandler and the classical quadratic mixing rules, affording a satisfactory representation of the experimental data.