Published papers

© 2019 The Royal Society of Chemistry. We studied the self-assembly processes and morphologies of tetrapod nanoparticle solutions using dissipative particle dynamics (DPD) simulations. Composite-shaped nanoparticles, such as a tetrapod model with an amphiphilic surface pattern, were also synthesised experimentally. We programmed nanoparticle self-assembly using the DPD results at various values of the hydrophobic ratio (HR) and arm length (AL). Considering these two parameters, we observed the self-assembly processes and morphologies in a tetrapod nanoparticle solution. As a result, it was found that the HR and AL of tetrapod nanoparticles were effective parameters for controlling their self-assembly processes or structures under equilibrium. In this study, we programmed the AL or HR parameters, and we report their influences on self-assembly. Our simulations offer a guide to controlling the morphologies of self-assembled tetrapod nanoparticles, which constitute novel systems that may find applications in nanofluidic devices.

Water transport must be efficiency controlled for the future sustainability of life. Various water transport systems using carbon nanotubes have been proposed in recent years. Although these systems are more permeable than aquaporins, their water transport is passive. In this study, we successfully demonstrate an active water pump driven by simple hydrophobic interaction through computer simulation. Even in the absence of a pressure- or density-gradient, the proposed pump can actively transport water molecules by alternately switching the hydrophobicity of the pump surface. The water transport rate can be easily controlled by varying the time interval of switching. The pump with optimized switching time exhibits prominent water permeance. The results obtained herein can be applied in various water transport technologies because of the simple mechanics. The proposed water pump has the potential to realize an effective device such as a low-energy artificial purification system.

In this study, submicron poly(methyl methacrylate) (PMMA) particles with surfaces modified using poly(vinyl alcohol) (PVA) (PMMA-PVA) were produced to formulate solid-stabilized oil-in-water (O/W) emulsions including high internal phase emulsions with a dispersed phase of over 74 %. The high-internal phase emulsions stabilized by PMMA–PVA exhibited gel-like behavior, and the oil droplets exhibited faceted shapes. PVA with a degree of saponification of 80 and 88 mol% was used as the surface modifier rather than PVA with > 96 mol%, assisting in the formation of a robust adsorbed layer on the PMMA particles. Coarse-grained molecular simulations revealed that the partially-hydrolyzed PVA exhibited a better adsorption behavior compared to the fully-hydrolyzed PVA, being thermodynamically adsorbed onto the PMMA surface in water owing to stable adsorption via its hydrophobic acetyl groups. Furthermore, PMMA–PVA reduced the interfacial tension as the degree of saponification of PVA decreased. Simulation and experimental results suggested that the acetyl groups of PVA not only adsorbed onto the PMMA surface but also promoted adsorption between PMMA and the oil–water interface. Overall, the findings of this study underscore the potency of PMMA–PVA as a particulate stabilizer for formulating O/W high internal phase emulsions.

The authors regret to inform you that we have discovered a mistake in our original modeling of poly(methyl methacrylate) (PMMA) shown in Fig. 1(b) in Ref. [1]. We reran all-atomistic molecular dynamics (MD) simulations with the corrected molecular structure of PMMA (Fig. 1(b)) to obtain interaction parameters, aij, and have revised aij as shown in Table 1. In addition, the dissipative particle dynamics (DPD) simulations of the biphasic system with this corrected aij were performed to see the behavior of poly(vinyl alcohol) (PVA) adsorbed onto the PMMA surface. We found no deviations from Fig. 10 and Fig. 11 in Ref. [1]. Therefore, the conclusion of the article [1] is unaffected. The authors would like to apologize for any inconvenience caused.

Although Pt–M (M = transition metal) catalysts are indispensable for the hydrogen evolution reaction (HER), the key function and operative mechanism of M that extend beyond the electronic modulation of Pt remain poorly defined. To address this gap, we synthesized three-dimensional PtNi multibranched nanostructures (PtNi MBNs) incorporating dual active sites. In situ spectroscopic analyses reveal that Ni sites directly facilitate water dissociation through the generation of Ni–OH species, thereby producing ample H* intermediates that mitigate the hydrogen scarcity in alkaline electrolytes and subsequently enhance the formation of Pt–H bonds. Molecular dynamics simulations further confirm strong spontaneous bonding between Ni and O at the atomic scale, underscoring the critical role of Ni in driving water dissociation and stabilizing Ni–OH bonds. Such Pt/Ni dual-active-site mechanism endows the PtNi MBNs with exceptional performance in 1 M KOH, delivering an ultralow overpotential of 9.29 mV at 10 mA cm−2 (35.2 mV lower than Pt/C) and a high mass activity of 9.30 A mgPt−1 at −70 mV (3.63 times that of Pt/C). Characterization after cycling confirmed that PtNi MBNs exhibit excellent structural stability and electrochemical stability. This study advances the fundamental understanding beyond single site catalytic mechanisms and offers new insights for accelerating HER kinetics in alkaline electrolytes.

Designing magnetic materials with a strong response to external magnetic field is pivotal for enhancing electrocatalytic reactions in energy conversion and storage systems. Although significant progress has been made in spin catalysts for hydrogen and oxygen electrocatalytic reactions, the development of spin catalysts for small organic molecule reactions is still insufficient, which greatly hinders the exploration of the effect of magnetic fields on the electrochemical reaction. Herein, we propose a synergistic strategy combining ternary alloying and phase transition to construct L10-FeCrPt intermetallic nanochains, which exhibit exceptional structural stability and a significantly boosted magnetic field response. After alloying Cr into FePt and transforming from the face-centered cubic (FCC) phase to the L10 phase through coating-assisted annealing, the activity of L10-FeCrPt for the methanol oxidation reaction was enhanced by up to 41 % under a magnetic field of 8000 Oe, surpassing the enhancements of only 13 % and 30 % achieved by FCC FeCrPt and L10-FePt, respectively. Based on the linear relation between the activation energy and the magnetic field, the magnetic moment parameter (M) is proposed to evaluate the magnetic response. The M value of L10-FeCrPt reaches 46839 emu/g, which is 2.7 times that of L10-FePt. DFT calculations indicate that the reaction barrier of L10-FeCrPt is significantly reduced because the external magnetic field can facilitate the adsorption of CO, the breaking of O-H bonds, and the addition of OH* species. This study provides an efficient method for designing spin catalysts with high magnetic field response through ternary alloying and phase engineering.

Molecular dynamics (MD) simulations are increasingly being combined with machine learning (ML) to predict material properties. The molecular configurations obtained from MD are represented by multiple features, such as thermodynamic properties, and are used as the ML input. However, to accurately find the input--output patterns, ML requires a sufficiently sized dataset that depends on the complexity of the ML model. Generating such a large dataset from MD simulations is not ideal because of their high computation cost. In this study, we present a simple supervised ML method to predict the transport properties of materials. To simplify the model, an unsupervised ML method obtains an efficient representation of molecular movements. This method was applied to predict the viscosity of lubricant molecules in confinement with shear flow. Furthermore, simplicity facilitates the interpretation of the model to understand the molecular mechanics of viscosity. We revealed two types of molecular mechanisms that contribute to low viscosity.

© 2018 The Royal Society of Chemistry. The functionality of polymer electrolyte fuel cell membranes depends on the self-assembled structure of the graft polymer. To control self-assembly, nanoparticles (NPs) are often used as catalysts. Hence, we investigate the effect of hydrophilic (HI), hydrophobic (HO), and Janus nanoparticles (JNPs) for the self-assembly of graft polymers using dissipative particle dynamics (DPD) simulations. We found that the differences that appeared among the self-assembled structures of water depended on the concentration of PEFC. We also calculated the diffusion constant of water (D(H2O)) from the slopes of the time-averaged mean square displacement (MSD) curves. HI NPs had the largest effect in suppressing the diffusion of water because the HI NPs incorporated into the water particles. It was also seen that D(H2O) with various NPs gradually decreased as the number of NPs increased for three PEFC concentrations (70%, 80%, and 90%). Thus, a close correlation between the position and chemical composition of NPs in polymer electrolyte fuel cell (PEFC) membrane systems has been found. Moreover, the mean square radius of gyration 〈Rg〉 and the mean square end-to-end distance 〈R〉 was calculated to analyse the self-assembled structures of PEFC. The 〈Rg〉 and 〈R〉 increased as the concentration of PEFC was increased, with and without various NPs.

© 2018 The Royal Society of Chemistry. 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.

In recent years, photonic crystal technology has received much attention. In this study, we focus on liquid crystal droplets considered as one of photonic crystals. It was reported that the orientation of liquid crystal molecules can be controlled by covering the droplet surface with surfactant solutions (Moreno-Razo et al. Nature. 2012;485:86). We investigated a system in which surfactant solutions are added to liquid crystal droplets with based on their study. The effect of surfactant concentration on the orientation behaviour of the liquid crystal molecules was observed. Phase transition of liquid crystal was also observed with changes to the temperature of the liquid crystal droplets. Various self-assembled structures and changes in liquid crystal droplet properties were discussed. We believe that the results of our work will help in further pursuing the research on photonic crystal devices.

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.

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.

Binary and ternary blends with poly(lactic acid) (PLA), poly(butylene succinate) (PBS), and thermoplastic starch (TPS) were prepared by a melt process to produce biodegradable biomass plastics with both economical and good mechanical properties. The mechanical and structural properties of each blend were evaluated. Molecular dynamics (MD) simulations were also conducted to examine the mechanisms underlying the mechanical and structural properties. PLA/PBS/TPS blends showed improved mechanical properties compared with PLA/TPS blends. The PLA/PBS/TPS blends with a TPS ratio of 25–40 wt% showed higher impact strength than PLA/PBS blends. Morphology observations showed that in the PLA/PBS/TPS blends, a structure similar to that of core-shell particles with TPS as the embedding phase and PBS as the coating phase was formed, and that the trends in morphology and impact strength changes were consistent. The MD simulations suggested that PBS and TPS tightly adhered to each other in a stable structure at a specific intermolecular distance. From these results, it is clear that PLA/PBS/TPS blends are toughened by the formation of a core-shell structure in which the TPS core and the PBS shell adhered well together and stress concentration and energy absorption occurred in the vicinity of the core-shell structure.

Quasi-liquid layers (QLLs) are present on the surface of ice and play a significant role in its distinctive chemical and physical properties. These layers exhibit considerable heterogeneity across different scales ranging from nanometers to millimeters. Although the formation of partially ice-like structures has been proposed, the molecular-level understanding of this heterogeneity remains unclear. Here, we examined the heterogeneity of molecular dynamics on QLLs based on molecular dynamics simulations and machine learning analysis of the simulation data. We demonstrated that the molecular dynamics of QLLs do not comprise a mixture of solid- and liquid water molecules. Rather, molecules having similar behaviors form dynamical domains that are associated with the dynamical heterogeneity of supercooled water. Nonetheless, molecules in the domains frequently switch their dynamical state. Furthermore, while there is no observable characteristic domain size, the long-range ordering strongly depends on the temperature and crystal face. Instead of a mixture of static solid- and liquid-like regions, our results indicate the presence of heterogeneous molecular dynamics in QLLs, which offers molecular-level insights into the surface properties of ice.

Amphiphilic molecules spontaneously form self-assembly structures depending on physical conditions such as the molecular structure, concentration, and temperature. These structures exhibit various functionalities according to their morphology. The critical packing parameter (CPP) is used to correlate self-organized structures with the chemical composition. However, accurately calculating it requires information about both the molecular shape and molecular aggregates, making it challenging to apply directly in molecular design. We aimed to predict the self-assembled structure of a molecule directly from its chemical structure and to analyze the factors influencing it using machine learning. Dissipative particle dynamics simulations were used to reproduce many self-assembly structures comprising various chemical structures, and their CPPs were calculated. Machine learning models were built using the chemical structures as input data and the CPPs as output data. As a result, both random forest and the gated recurrent unit showed high prediction accuracy. Feature importance analysis and sample size dependence revealed that the amphiphilic nature of molecules significantly influences the self-assembly structures. Additionally, selecting an appropriate molecular structure representation for each algorithm is crucial. The study results should contribute to product development in the fields of materials science, materials chemistry, and medical materials.

Despite extensive experimentation to efficiently prepare concentrated oil-in-water (O/W) emulsions with sufficient stability, the effect of the chemical structure of the polymeric surfactants on the emulsion stability is not yet understood in detail at the molecular level. We investigated the effect of the degree of saponification and arrangement of the hydrophobic (acetyl) groups of poly(vinyl alcohol) (PVA) on the stability of the concentrated emulsion via a combination of experiments and coarse-grained molecular simulations. This was accomplished by varying the degree of saponification and the arrangement of the hydrophobic groups on PVA, and by systematically changing the volume fraction of PVA molecules. The stability of the emulsion was adversely affected by the presence of an excess or the complete absence of hydrophobic groups on PVA. The former situation promotes the aggregation of PVA molecules with each other; conversely, fully saponified PVA molecules penetrate the water phase, thereby decreasing the number of PVA molecules at the O/W interface. Thus, the depletion of the interface of PVA molecules increases the interfacial tension, and, consequently, destabilizes the interface. Adjustment of the degree of saponification to a suitable value (80%) led to the formation of stable, concentrated emulsions with a smaller amount of PVA. For PVA with an appropriate degree of saponification, the interfacial behavior of concentrated O/W emulsions was to a lesser extent affected by the arrangement of the hydrophobic groups of PVA compared to the degree of saponification. Furthermore, the individual PVA chains at the interface had planar conformations, regardless of the arrangement of the hydrophobic groups. Our results indicate that careful tuning of the degree of saponification, rather than the arrangement of the hydrophobic groups of PVA, is necessary to control the interfacial stability of concentrated O/W emulsions using PVA. The findings of this study provide support for departing from empirical trial-and-error approaches and may facilitate the development of emulsifiers with enhanced performance.