Nanochannel with uniform and Janus surfaces: shear thinning and thickening in surfactant solution

Structure and dynamics of amphiphilic patchy cubes in a nanoslit under shear

Patchy nanocubes are intriguing materials with simple shapes and space-filling and multidirectional bonding properties. Previous studies have revealed various mesoscopic structures such as colloidal crystals in the solid regime and rod-like or fractal-like aggregates in the liquid regime of the phase diagram. Recent studies have also shown that mesoscopic structural properties, such as an average cluster size M and orientational order, in amphiphilic nanocube suspensions are associated with macroscopic viscosity changes, mainly owing to differences in cluster shape among patch arrangements. Although many studies have been conducted on the self-assembled structures of nanocubes in bulk, little is known about their self-assembly in nanoscale spaces or structural changes under shear. In this study, we investigated mixtures of one- and two-patch amphiphilic nanocubes confined in two flat parallel plates at rest and under shear using molecular dynamics simulations coupled with multiparticle collision dynamics. We considered two different patch arrangements for the two-patch particles and two different slit widths H to determine the degree of confinement in constant volume fractions in the liquid regime of the phase diagram. We revealed two unique cluster morphologies that have not been previously observed under bulk conditions. At rest, the size of the rod-like aggregates increased with decreasing H, whereas that of the fractal-like aggregates remained constant. Under weak shear with strong confinement, the rod-like aggregates maintained a larger M than the fractal-like aggregates, which were more rigid and maintained a larger M than the rod-like aggregates under bulk conditions.

Janus Droplet Formation via Thermally Induced Phase Separation: A Numerical Model with Diffusion and Convection

Microscale Janus particles have versatile potential applications in many physical and biomedical fields, such as microsensor, micromotor, and drug delivery. Here, we present a phase-field approach of multicomponent and multiphase to investigate the Janus droplet formation via thermally induced phase separation. The crucial kinetics for the formation of Janus droplets consisting of two polymer species and a solvent component via an interplay of both diffusion and convection is considered in the Cahn-Hilliard-Navier-Stokes equation. The simulation results of the phase-field model show that unequal interfacial tensions between the two polymer species and the solvent result in asymmetric phase separation in the formation process of Janus droplets. This asymmetric phase separation plays a vital role in the establishment of the so-called core-shell structure that has been observed in previous experiments. By varying the droplet size, the surface tension, and the molecular interaction between the polymer species, several novel droplet morphologies are predicted in the development process of Janus droplets. Moreover, we stress that the hydrodynamics should be reckoned as a non-negligible mechanism that not only accelerates the Janus droplet evolution but also has great impacts on the coarsening and coalescence of the Janus droplets.

An Efficient Random Number Generation Method for Molecular Simulation

We propose a new random number generation method, which is the fastest and the simplest of its kind, for use with molecular simulation. We also discuss the possibility of using this method with various other numerical calculations. To demonstrate the significant increases in calculation speeds that can be gained by using our method, we present a comparison with prior methods for dissipative particle dynamics (DPD) simulations. The DPD method uses random numbers to reproduce thermal fluctuations of molecules. As such, an efficient method to generate random numbers in parallel computing environments has been widely sought after. Several random number generation methods have been developed that use encryption. In this study, we establish for the first time that random numbers with desirable properties exist in the particle coordinates used in DPD calculations. We propose a method for generating random numbers without encryption that utilizes this source of randomness. This is an innovative method with minimal computational cost, since it is not dependent on a complicated random number generation algorithm or an encryption process. Furthermore, our method may lead to faster random number generation for many other physical and chemical simulations.

Molecular Insight into the Possible Mechanism of Drag Reduction of Surfactant Aqueous Solution in Pipe Flow

The phenomenon of drag reduction (known as the “Toms effect”) has many industrial and engineering applications, but a definitive molecular-level theory has not yet been constructed. This is due both to the multiscale nature of complex fluids and to the difficulty of directly observing self-assembled structures in nonequilibrium states. On the basis of a large-scale coarse-grained molecular simulation that we conducted, we propose a possible mechanism of turbulence suppression in surfactant aqueous solution. We demonstrate that maintaining sufficiently large micellar structures and a homogeneous radial distribution of surfactant molecules is necessary to obtain the drag-reduction effect. This is the first molecular-simulation evidence that a micellar structure is responsible for drag reduction in pipe flow, and should help in understanding the mechanisms underlying drag reduction by surfactant molecules under nonequilibrium conditions.

The influence of random number generation in dissipative particle dynamics simulations using a cryptographic hash function

The tiny encryption algorithm (TEA) is widely used when performing dissipative particle dynamics (DPD) calculations in parallel, usually on distributed memory systems. In this research, we reduced the computational cost of the TEA hash function and investigated the influence of the quality of the random numbers generated on the results of DPD calculations. It has already been established that the randomness, or quality, of the random numbers depend on the number of processes from internal functions such as SHIFT, XOR and ADD, which are commonly referred to as “rounds”. Surprisingly, if we choose seed numbers from high entropy sources, with a minimum number of rounds, the quality of the random numbers generated is sufficient to successfully perform accurate DPD simulations. Although it is well known that using a minimal number of rounds is insufficient for generating high-quality random numbers, the combination of selecting good seed numbers and the robustness of DPD simulations means that we can reduce the random number generation cost without reducing the accuracy of the simulation results.

Effect of the Janus Amphiphilic Wall on the Viscosity Behavior of Aqueous Surfactant Solutions

The effects of the chemical nature of an interface are one of the key parameters which can affect self-assembly and rheological behavior. To date, several studies have reported self-assembled structures and rheological behaviors in the development of various functional materials. In this study, we investigated the self-assembly and viscosity behavior of aqueous surfactant solutions confined in three types of Janus amphiphilic nanotubes (JANTs), which have two, four, and eight sequential domains, respectively, using molecular simulation. We found that the viscosity behavior depends on the surfactant concentration and the chemical nature of the wall surface. For instance, although the concentration levels of the surfactants are the same (c = 10%), completely different viscosity behaviors were observed in the two sequential domains (Newtonian-like) and the four and eight sequential domains (strong shear-thinning) of the JANTs. Our simulations demonstrated how the rheological properties of aqueous surfactant solutions, including viscosity and velocity profiles, can be controlled by the chemical nature of the JANT wall surface, effect of confinement, and their self-assembly structures. Considering the foregoing, we hope that our study offers new knowledge on nanofluid systems.

A biointerface effect on the self-assembly of ribonucleic acids: a possible mechanism of RNA polymerisation in the self-replication cycle

Despite decades of intensive research, many questions remain on the formation and growth of the first cells on Earth. Here, we used computer simulation to compare the self-assembly process of ribonucleic acids in two environments: enclosed in a vesicle-cell membrane and in the bulk. The self-assembly was found to be more favoured in the former environment, and the origin of such a biointerface effect was identified. These results will contribute to a better understanding of the origin of life on the primitive Earth.

Multiscale prediction of functional self-assembled materials using machine learning: high-performance surfactant molecules

Various physical properties of functional materials can be induced by controlling their chemical molecular structures. Therefore, molecular design is crucial in the fields of engineering and materials science. With its remarkable development in various fields, machine learning combined with molecular simulation has recently been found to be effective at predicting the electronic structure of materials (Nat. Commun., 2017, 8, 872 and Nat. Commun., 2017, 8, 13890). However, previous studies have used similar microscale information as input and output data for machine learning, i.e., molecular structures and electronic structures. In this study, we determined whether multiscale data can be predicted using machine learning via a self-assembly functional material system. In particular, we investigated whether machine learning can be used to predict dispersion and viscosity, as the representative physical properties of a self-assembled surfactant solution, from the chemical molecular structures of a surfactant. The results showed that relatively accurate information on these physical properties can be predicted from the molecular structure, suggesting that machine learning can be used to predict multiscale systems, such as surfactant molecules, self-assembled micelle structures, and physical properties of solutions. The results of this study will aid in further development of the application of machine learning to materials science and molecular design.

Self-Assembly and Viscosity Behavior of Janus Nanoparticles in Nanotube Flow

Janus nanoparticles (JNPs) have received considerable attention because of their characteristic physical properties that are due to more than two distinct chemical or physical surfaces. We investigated the rheological properties of a JNP solution in the nanotubes using a computer simulation. Prediction and control of the self-assembly of colloidal nanoparticles is of critical importance in materials chemistry and engineering. Herein, we show computer simulation evidence of a new type of velocity profile and a hallmark shear-thinning behavior by confining a JNP solution to a nanotube with hydrophobic and hydrophilic wall surfaces. We derived curves of the shear rate versus the viscosity for two quasi-one-dimensional nanotube systems including diluted and concentrated volume fractions of JNP solutions. For the diluted system, under relatively low shear rates, shear-thinning behavior with a moderate slope or behavior similar to a Newtonian fluid is observed because of the clustering of JNPs. Under relatively high shear rates, the slope of shear thinning changes markedly because the self-assembled structures are rearranged. Moreover, for concentrated systems, when the nanotube wall is hydrophobic, new characteristic velocity profiles that have not been reported before are observed. Our simulation offers a guide to control the rheological properties of JNP solutions by the chemical patterns on the surfaces of nanochannels, the effect of confinement, and the self-assembled structure.

Probing nonlinear rheology layer-by-layer in interfacial hydration water

Viscoelastic fluids exhibit rheological nonlinearity at a high shear rate. Although typical nonlinear effects, shear thinning and shear thickening, have been usually understood by variation of intrinsic quantities such as viscosity, one still requires a better understanding of the microscopic origins, currently under debate, especially on the shear-thickening mechanism. We present accurate measurements of shear stress in the bound hydration water layer using noncontact dynamic force microscopy. We find shear thickening occurs above ∼ 10(6) s(-1) shear rate beyond 0.3-nm layer thickness, which is attributed to the nonviscous, elasticity-associated fluidic instability via fluctuation correlation. Such a nonlinear fluidic transition is observed due to the long relaxation time (∼ 10(-6) s) of water available in the nanoconfined hydration layer, which indicates the onset of elastic turbulence at nanoscale, elucidating the interplay between relaxation and shear motion, which also indicates the onset of elastic turbulence at nanoscale above a universal shear velocity of ∼ 1 mm/s. This extensive layer-by-layer control paves the way for fundamental studies of nonlinear nanorheology and nanoscale hydrodynamics, as well as provides novel insights on viscoelastic dynamics of interfacial water.

Mesoscopic modeling of structural and thermodynamic properties of fluids confined by rough surfaces

Simulations show that the ordering of particles confined by rough surfaces induces a structural phase transition while the interfacial tension is insensitive to it.

Poisson property of the occurrence of flip-flops in a model membrane

How do lipid molecules in membranes perform a flip-flop? The flip-flops of lipid molecules play a crucial role in the formation and flexibility of membranes. However, little has been determined about the behavior of flip-flops, either experimentally, or in molecular dynamics simulations. Here, we provide numerical results of the flip-flops of model lipid molecules in a model membrane and investigate the statistical properties, using millisecond-order coarse-grained molecular simulations (dissipative particle dynamics). We find that there are three different ways of flip-flops, which can be clearly characterized by their paths on the free energy surface. Furthermore, we found that the probability of the number of the flip-flops is well fitted by the Poisson distribution, and the probability density function for the inter-occurrence times of flip-flops coincides with that of the forward recurrence times. These results indicate that the occurrence of flip-flops is a Poisson process, which will play an important role in the flexibilities of membranes.

Understanding molecular motor walking along a microtubule: a themosensitive asymmetric Brownian motor driven by bubble formation

The "asymmetric Brownian ratchet model", a variation of Feynman's ratchet and pawl system, is invoked to understand the kinesin walking behavior along a microtubule. The model system, consisting of a motor and a rail, can exhibit two distinct binding states, namely, the random Brownian state and the asymmetric potential state. When the system is transformed back and forth between the two states, the motor can be driven to "walk" in one direction. Previously, we suggested a fundamental mechanism, that is, bubble formation in a nanosized channel surrounded by hydrophobic atoms, to explain the transition between the two states. In this study, we propose a more realistic and viable switching method in our computer simulation of molecular motor walking. Specifically, we propose a thermosensitive polymer model with which the transition between the two states can be controlled by temperature pulses. Based on this new motor system, the stepping size and stepping time of the motor can be recorded. Remarkably, the "walking" behavior observed in the newly proposed model resembles that of the realistic motor protein. The bubble formation based motor not only can be highly efficient but also offers new insights into the physical mechanism of realistic biomolecule motors.