Published papers

This study employed dissipative particle dynamics (DPD) simulations to investigate the self-propelled motion of oil droplets in water–oil–surfactant systems. It is the first attempt to replicate self-propulsion models of oil droplets at the molecular level, contrasting previous simulations focused on Brownian motion and hydrodynamic behaviour of colloidal particles. The DPD model reproduced droplet propulsion and visualised internal Marangoni flow, showing that larger droplet radii and greater interfacial tension differences increase propulsion speeds. Additionally, surfactants with stronger oil–oil repulsion enhanced propulsion speed, suggesting that surfactant-induced local structures are crucial for the self-propulsion mechanism.

This study explores the stabilization mechanisms of concentrated emulsions with tunable morphology using amphiphilic polymer-grafted nanoparticles (PGNPs). We employ coarse-grained molecular simulations to investigate concentrated oil-in-water emulsions stabilized by partially hydrolyzed poly(vinyl alcohol)-grafted poly(methyl methacrylate) (PMMA) particles. Two grafting architectures were examined: hydrophilic-hydrophobic (AB-type) diblock PGNPs and reverse BA-type diblock PGNPs. Our findings reveal that AB-type diblock PGNPs tend to aggregate, leading to droplet-droplet coalescence. In contrast, BA-type diblock PGNPs disperse effectively in the water phase, stabilizing robust emulsion through a space-filling mechanism. The study further demonstrates that the stability and morphology of the emulsions can be tuned by varying the number of PGNPs. Our results suggest that BA-type diblock PGNPs are more effective in stabilizing concentrated emulsions, offering insights for the design of novel emulsifiers in industrial applications.

This study analyzed the fracture behavior of poly(lactic acid) (PLA)/polybutylene succinate (PBS)/thermoplastic starch (TPS) blends featuring nanoscale-dispersed core-shell structures using molecular dynamics (MD) simulations, elucidating the toughening mechanisms at the molecular-level. Four models (PLA/TPS25, PLA/PBS/TPS5, PLA/PBS/TPS25, and PLA/PBS/TPS50) with varying core-shell structure parameters were utilized in the MD simulations. The fracture energy relationship obtained from the deformation simulation was PLA/PBS/TPS25 > PLA/PBS/TPS5 > PLA/PBS/TPS50 ≈ PLA/TPS25, which generally reproduced the experimental results of the impact test of the blends. The entire material was fractured by delamination at the TPS/PLA interface in the PLA/TPS25, and at the TPS/PBS interface in the PLA/PBS/TPS50 with the thinnest PBS shell. The PLA/PBS/TPS5 with the thickest PBS shell exhibited a reduced toughening effect due to the formation of numerous voids in the PLA matrix phase during deformation. In PLA/PBS/TPS25 with an intermediate thickness PBS shell, moderate-density voids formed during deformation, releasing strain constraints and effectively strengthening the material. The orientation hardening of PLA and PBS that occurs during deformation also contributes to the deformation energy absorption of PLA/PBS/TPS25. The molecular-level toughening mechanism elucidated in this study is anticipated to contribute to the enhancement of the properties of a wide range of bioplastics containing polysaccharides.

Wettability, a fundamental physicochemical property of functional materials that depends on their surface structure, has seen recent advances in understanding that have led to the development of superhydrophobic surfaces with applications in self-cleaning materials and antifogging coatings. Given the presence of a free energy barrier between the Cassie and Wenzel states, calculating this barrier provides a valuable means to assess surface hydrophobicity. We hypothesize that surface characteristics affect the free energy barrier between wetting states and that the determination of this barrier can be used to predict the hydrophobicity. Focused on models of a butterfly wing (striped surface) and a mosquito eye (pillared surface), this study employed many-body dissipative particle dynamics (mDPD) simulations to explore the relationship between the free energy barrier and surface roughness (height and spacing). For small-radius droplets, the height of the surface pattern predominantly influences the hydrophobicity. Conversely, for large-radius droplets, the spacing of the surface pattern is the dominant factor. Considering that the contact angle indicates static hydrophobicity, the free energy barrier is the indicator of dynamic hydrophobicity, which shows how well the surface can maintain its hydrophobicity under the impact of a droplet. This study underscores the advantages of the free energy barrier method in understanding dynamic hydrophobicity, which can be applied in evaluating high kinetic energy droplets onto surfaces, for example, automobiles, trains, and aircraft.

The study of self-propelled motion in soft matter systems has garnered significant interest owing to its potential applications in microfluidics, soft robotics, and autonomous system design. Understanding the molecular mechanisms underlying motility is crucial for advancing these applications. This study investigates the self-propelled motion of lauronitrile oil droplets in aqueous surfactant solutions, focusing on the impact of different surfactant molecular structures on droplet dynamics. This study compares surfactants with ester and amide linkages, highlighting their critical role in modulating interfacial tension and driving Marangoni convection, a key factor behind droplet movement. Surfactants with ester linkages exhibit a high affinity for lauronitrile and rapidly adsorb at the oil–water interface, generating strong Marangoni flows and driving fast droplet motion. In contrast, amide-containing surfactants exhibit slower adsorption and weaker interactions with lauronitrile, leading to reduced or absent motion. These findings provide new insights into the molecular mechanisms underlying the self-propelled droplet behavior in non-equilibrium systems and contribute to a deeper understanding of self-organizing phenomena.

The ice–rubber interface is critical in applications such as tires and shoe outsoles, yet its molecular tribology remains unclear. Using all-atom molecular dynamics simulations, we studied premelting layers at the basal face of ice in contact with styrene–butadiene rubber from 254 to 269 K. Despite its hydrophobicity, rubber enhanced the structural disorder of interfacial water, promoting premelting. In contrast, water mobility was suppressed by confinement from polymer chains, leading to glassy dynamics distinct from that of the ice–vapor interface. Near the melting point, rubber chains became more flexible and penetrated the premelting layer, forming a mixed rubber–water region that couples the dynamics of both components. These results suggest that nanoscale roughness and morphology of hydrophobic polymers disrupt ice hydrogen-bond networks, thereby enhancing premelting. Our findings provide molecular-level insight into ice slipperiness and inform the design of polymer materials with controlled ice adhesion and friction.

Polymer self-assembly is inherently multiscale: coarse-grained (CG) simulations capture mesoscale organization, whereas all-atom (AA) molecular dynamics resolves local interactions. We present ANY-RM, a fast and straightforward reverse-mapping method that reconstructs AA star-polymer structures from a dissipative particle dynamics configuration by converting bead coordinates into a concentration-distribution field and placing atoms to satisfy local composition constraints. The AA models retain CG phase separation while restoring atomistic detail. Static structure agrees across scales. Our method enables the investigation of both large-scale organization and detailed molecular interactions, offering a comprehensive understanding of polymer self-assembly across multiple scales.

Multi-ingredient cleansing foams pose a combinatorial design challenge because many component ratios must be experimentally screened. We integrate DPD-derived descriptors with machine learning to enable prescreening and prioritization of formulations, thereby reducing exploratory batches and accelerating design cycles while maintaining a traceable physical rationale. The modeling descriptors─the hydrophilic fraction (fHI) and the solubility parameter contrast (Δδ) defined relative to the polyethylene glycol thresholds─probe amphiphilicity, while DPD-derived potential energy and pressure summarize mesoscale self-assembly. Using 430 historical recipes, we benchmark nested cross-validation under three generalization regimes: Points Out (random formulations), Mixtures Out (novel combinations of known ingredients), and Compounds Out (novel raw ingredients; polyols, including humectants and amphiphilic derivatives, in this data set). To prevent leakage, all preprocessing (imputation and scaling) is fit strictly on training folds only, and identical outer-CV partitions are held across feature conditions to enable paired comparisons. Incorporating modeling and simulation descriptors improves mean R2 from 0.665 to 0.716 (Points Out) and from 0.420 to 0.573 (Mixtures Out), and raises Compounds Out R2 from 0.023 to 0.341. Paired difference tests with HC3-robust OLS and Holm correction confirm statistically significant gains─small to moderate for Points Out and moderate to large for Mixtures and Compounds Out. Among algorithms, tree-based ensembles outperform linear, kernel, and neural baselines, reflecting nonlinear composition–property relations. This workflow operationalizes AI-assisted formulation design by triaging candidate recipes prior to wet-lab screening, enabling faster decision-making and tangible experimental savings while retaining physical interpretability via DPD-derived descriptors. Compounds out results apply only to polyols in the present data set; generalization beyond polyols is out-of-scope and will require larger, more diverse data sets and transfer learning.

© 2017 Informa UK Limited, trading as Taylor & Francis Group. Since block copolymers self-assemble into various nanostructures and these are widely used in soft materials such as cosmetics and paints, these continue to be the subjects of fundamental studies that progress new technologies. ABA triblock copolymers self-organise into supramolecular networks in which crosslinked structures change upon heating and other external stimulation. Supramolecular networks that consist of ABA triblock copolymers have three components: bridges, loops and dangles. These supramolecular networks have structural regions (clusters) in which end blocks of polymer chains are aggregated and connected by the internal blocks of the polymer chains. Despite of the importance of relaxation behaviour during the measurement and control of polymer materials, the molecular-level behaviour of these systems has not been addressed. We observed pull-out phenomenon that the ends of these polymers detach from clusters, and estimated characteristic detachment times by counting the number of detachments and compared it with the time of longest relaxation in that system.

We describe herein that amphiphilic semiconductor nanocrystals (NCs) self-assembled into network structures with heterogeneous compositions. The semiconductor nanorods and tetrapods were subjected to ligand exchange with short-chained water-soluble thiolates, giving an amphiphilic surface pattern with a hydrophilic wall and hydrophobic tips. The amphiphilic NCs self-assembled through hydrophobic effects between tip-surfaces in water. The hydrophobic effect-facilitated self-assembly of NCs was well reproduced by a dissipative particle dynamics simulation. The amphiphilic self-assembly of NCs was demonstrated regardless of NC-shapes and compositions to give semiconductor NC-network structures with heterogeneous compositions. The tandem connection of luminescent core/shell nanorods demonstrated energy transfer between the nanorods in the self-assembly. Copyright (C) EPLA, 2017

The "asymmetric Brownian ratchet model" is a variation of Feynman's ratchet and pawl system proposed. In this model, a system consisting of a motor and a rail has two binding states. One is the random Brownian state, and the other is the asymmetric potential state. When the system is alternatively switched between these states, the motor can be driven in one direction. This model is believed to explain nanomotor behavior in biological systems. The feasibility of the model has been demonstrated using electrical and magnetic forces; however, switching of these forces is unlikely to be found in biological systems. In this paper, we propose an original mechanism of transition between states by bubble formation in a nanosized channel surrounded by hydrophobic atoms. This amounts to a nanoscale motor system using bubble propulsion. The motor system consists of a hydrophobic motor and a rail on which hydrophobic patterns are printed. Potential asymmetry can be produced by using a left-right asymmetric pattern shape. Hydrophobic interactions are believed to play an important role in the binding of biomolecules and molecular recognition. The bubble formation is controlled by changing the width of the channel by an atomic distance (similar to 0.1 nm). Therefore, the motor is potentially more efficient than systems controlled by other forces, in which a much larger change in the motor position is necessary. We have simulated the bubble-powered motor using dissipative particle dynamics and found behavior in good agreement with that of motor proteins. Energy efficiency is as high as 60%.

© 2019, © 2019 Taylor & Francis Group, LLC. The nanophase separation structure produced by block polymers is used for highly functional materials and has been studied for 50 years. In recent years, researchers have shown interest in introducing non-covalent bonds, such as hydrogen bonds and metal–ligand bonds, to polymers. The non-covalent bond dissociates or recombines in response to physical conditions, including temperature and concentration. The non-covalent bond freely controls the phase separation structure, namely, the physical properties of the polymer material. However, various interactions are involved in the formation process, for example, polymer–polymer, polymer–solvent, bonding and bending in the polymer. To control the self-assembled structure, it is necessary to understand the influence of the non-covalent bond at the molecular level. In this study, we investigated the effect of the non-covalent bond on a stage of phase separation immediately after dissociation (early-stage) of self-assembled morphologies by introducing the binding site to the triblock polymers. Various stable morphologies, including lamellar, lamellar with a grain boundary, and pre-lamellar, were observed by changing the positions of the non-covalent bond, their dissociation ratio, and the initial configurations. Moreover, we calculated the radial distribution function and static structure factor to identify the structural features. The results suggested that the early-stage of self-assembled morphology in polymer melt could be controlled by the position of the non-covalent bond and its dissociation ratio.

© the Owner Societies. Blue phases are three-dimensional self-assembly structures of liquid crystals with a lattice of line defects. They have attracted considerable interest as photonic materials. It is well known that blue phases occur in cholesteric liquid crystals (CLCs) under certain thermodynamic conditions; however, recent studies have indicated that confining surfaces may induce distinctive structural changes. For example, in a previous study, a quasi-two-dimensional (Q2D) confinement system was investigated with the aid of numerical calculations, and a stable Q2D Skyrmion structure was attained. Here, we performed molecular simulations to investigate the CLC phase behavior at the molecular scale for a quasi-one-dimensional (Q1D) nanotube system. Various morphological behaviors of CLCs were observed by changing the temperature and the radius of the nanotubes. In particular, we discovered a self-assembled structure with cylindrical (or ring-like) defects rather than lines by introducing a novel local orientation analysis. Our simulation results show that the self-assembly of CLCs offers a guide to control the intensity in Q1D systems and fundamental knowledge for their application to optical devices. This journal is

There is a clear relationship between the self-assembling architecture of nanoparticles (NPs) and their physical properties, and they are currently used in a variety of applications, including optical sensors. Polymer-tethered NPs, which are created by grafting polymers onto NPs to control the self-assembly of NPs, have attracted considerable attention. Recent synthetic techniques have made it possible to synthesize a wide variety of polymers and thereby create NPs with many types of surfaces. However, self-assembled structures have not been systematically classified because of the large number of tuning parameters such as the polymer length and graft density. In this study, by using coarse-grained molecular simulation, we investigated the changes in the self-assembled structure of polymer-tethered NP solutions confined in nanotubes due to the chemical properties of polymers. Three types of tethered polymer NP models were examined: homo hydrophilic, diblock hydrophilic-hydrophobic (HI-HO), and diblock hydrophobic-hydrophilic. Under strong confinement, the NPs were dispersed in single file at low axial pressure. As the pressure increased, multilayered lamellar was observed in the HI-HO model. In contrast, under weak confinement, the difference in the pressure at which the phases emerge, depending on the model, was significant. By changing the chemical properties of the grafted polymer, the thermodynamic conditions (the axial pressure in this study) under which the phases appear is altered, although the coordination of NPs remains almost unchanged. Our simulation offers a theoretical guide for controlling the morphologies of self-assembled polymer-tethered NPs, a novel system that may find applications in nanooptical devices or for nanopatterning.

Despite decades of extensive research, the behavior of confined liquids, particularly in the mixed/boundary lubrication regime, remains unelucidated. This can be attributed to several factors, including the difficulty to make direct experimental observations of the behavior of lubricant molecules under nonequilibrium conditions, the high computational cost of molecular simulations to reach steady state, and the low signal-to-noise ratio at extremely low shear rates corresponding to actual operating conditions. In this regard, we studied the correlation between the structure formation and shear viscosity of octamethylcyclotetrasiloxane confined between two mica surfaces in a mixed/boundary lubrication regime. Three different surface separations & mdash;corresponding to two-, three-, and five-layered structures & mdash;were considered to analyze the effect of confinement. The orientational distributions with one specific peak for n = 2 and two distributions, including a parallel orientation with the surface normal for n > 2, were observed at rest. The confined liquids exhibited a distinct shear-thinning behavior independent of surface separations for a relatively low shear rate, (gamma)over dot ? 10(8 )s(-1). However, the shear viscosities at (gamma)over dot ? 10(8) s(-1) depended on the number of layered structures. Newtonian behavior was observed with further increase in the shear rate. Furthermore, we found a strong correlation between the degree of molecular orientation and the shear viscosity of the confined liquids. The magnitude of the shear viscosity of the confined liquids can primarily be determined by the degree of molecular orientation, and shear thinning originates from the vanishing of specific orientational distributions with increasing shear rate. Published under a nonexclusive license by AIP Publishing.