Soft Matter最新文献

Soft materials such as colloids, pastes, and polymer liquids are defined rheologically by how they build and relax stress during flow and deformation. Their internal connectivity manifests in a broad spectrum of viscoelastic eigenmodes, with relaxation ranging from fast to slow and contributions that vary from weak to strong. The interplay of these modes determines how the material deforms under shear, compression, or stretching across different processing timescales. Traditional measures of viscoelasticity, such as the Deborah number (De) and the Weissenberg number (Wi), condense this complexity into single scalar values. While useful for certain purposes, these scalar measures mask the fast/slow interplay of relaxation processes that shape the distinct responses of soft matter. To overcome this limitation, we introduce the “spectral classification of processes and eigenmodes” (SCOPE) framework. SCOPE explicitly accounts for the distributed nature of both process times and material relaxation times. It generalizes the classical De and Wi into their functional counterparts—the Deborah function and the Weissenberg function—which connect applied stress and strain to the full spectrum of relaxation times (0 < τ < τ_max), thereby covering the entire range of process timescales and types of deformation. By doing so, SCOPE provides a spectral perspective on viscoelasticity that integrates fast and slow dynamics within a single, unified rheological framework. SCOPE provides criteria that separate viscous from elastic eigenmodes, and modes below or above the onset of nonlinearity. In what follows, we introduce the SCOPE framework in detail and demonstrate its functions for viscoelastic liquids.

The precise arrangement of different chemical moieties in a polymer determines its thermophysical properties. How the sequence of moieties impacts the properties of a polymer is an outstanding problem in polymer science. Herein, we address this problem for the thermoresponsive property of poly(N-isopropylacrylamide-co-acrylamide) in water using all-atom molecular dynamics (MD) simulations for temperatures ranging from 260 K to 360 K. Our simulations classify four distinct classes of thermoresponsive behavior in PNIPAM-co-PAM: (i) sequence exhibiting lower critical solution temperature (LCST) behavior, (ii) sequence exhibiting upper critical solution temperature (UCST) behavior, (iii) sequence displaying both LCST and UCST transitions, and (iv) sequence showing no discernible phase transition within the investigated temperature range. The critical temperature exhibits a strong correlation with the mean block length in periodic sequences displaying LCST-type behavior. This variability in thermoresponsive property is found to be closely linked to the extent of hydrogen bond formation in the system. These findings offer new directions in the design of structurally diverse thermoresponsive copolymers.

Adhesion-independent migration is a prominent mode of cell motility in confined environments, yet the physical principles that guide such movement remain incompletely understood. We present a phase-field model for simulating the motility of deformable, non-adherent cells driven by contractile surface instabilities of the cell cortex. This model couples surface and bulk hydrodynamics, accommodates large shape deformations and incorporates a diffusible contraction-generating molecule (myosin) that drives cortical flows. These capabilities enable a systematic exploration of how mechanical cues direct cell polarization and migration. We first demonstrate that spontaneous symmetry breaking of cortical activity can lead to persistent and directed movement in channels. We then investigate how various physical cues – including gradients in friction, viscosity, and channel width as well as external flows and hydrodynamic interactions between cells – steer migration. Our results show that active surface dynamics can generate stimulus-specific cell behaviors, such as migration up friction gradients or escape from narrow regions. Beyond cell migration, the model offers a versatile platform for exploring the mechanics of active surfaces in biological systems.

Despite its many applications, three-dimensional radical polymerization remains poorly understood. A major challenge is the considerable kinetic slowdown caused by gelation-a liquid-to-solid phase transition that produces a network permeating the entire volume. This rapidly developing structure greatly obscures direct experimental observations of kinetic mechanisms during network formation. Although molecular dynamics (MD) simulations can qualitatively reproduce the gelation process, they are restricted to unrealistically short time scales. To address this limitation, particularly with respect to cycle formation, we propose coarse-grained modeling techniques based on random graphs (RG) and Monte Carlo (MC) simulations, and apply them to the polymerization of (multi)functional acrylates: N-butyl acrylate (NBA), 1,6-hexanediol diacrylate (HDDA), and trimethylolpropane triacrylate (TMPTA). This approach emphasizes the network of monomer units in the polymer rather than representing individual molecules in atomistic detail. In our models, cycles are represented as special types of vertices, depending on their size. The model demonstrates the impact of cycles, such as a delay in the gel point, which varies with cycle size. The number and size of cycles predicted by the coarse-grained models agree well with MD simulations, but they still fail to capture certain structural features, such as overlapping cycles. Typically, in the gel regime, RG and MC models predict structures with many connected cycles essentially in a tree-like pattern.

Understanding and controlling the motion of self-propelled particles in complex fluids is crucial for applications in targeted drug delivery, microfluidic transport, and the broader field of active matter. Here, we investigate the thermophoretic self-propulsion of partially gold-coated polystyrene Janus particles (Au–PS) in temperature-responsive linear poly(N-isopropyl acrylamide) (PNIPAM) solutions across various concentrations and temperatures. Particle velocities are examined at three representative temperatures: room temperature ((21 ± 0.2) °C), (28 ± 1) °C (just below the LCST), and (34 ± 1) °C (above the LCST). Viscosity values of the PNIPAM solutions were found to be close to those of pure water, with no significant shear thinning or other viscoelastic effects observed under relevant experimental conditions. In pure water, Au–PS Janus particles propel with the PS hemisphere leading, driven by their intrinsic thermophoretic response. At low polymer concentrations (0.05 wt%), experiments and theoretical calculations reveal a non-monotonic dependence of particle velocity on temperature, with a maximum near the LCST. In this regime, the positive Soret coefficient of PNIPAM causes the polymer to accumulate near the cooler PS hemisphere, generating a diffusiophoretic drift that can dominate the intrinsic thermophoretic motion and reverse the propulsion direction. Experimentally, the propulsion direction switches from PS-forward to Au-forward between 0.04 wt% and 0.05 wt%, and within the 0.05 wt% solution, a secondary reversal back to PS-forward is observed at higher temperatures, consistent with the weakening of the depletion-induced drift above the LCST. At higher concentrations (0.5 wt% and 1 wt%), the increased polymer content leads to stronger adsorption onto the entire particle surface, which suppresses propulsion by reducing local asymmetry. At 34 °C, thermophoretic propulsion stops, leaving only Brownian motion. Additionally, the diffusion coefficient increases due to temperature raise. These results highlight the potential of thermo-responsive polymers to control microswimmer dynamics, offering tunable transport properties for applications in active matter and targeted delivery systems.

In this work, we explore the simulated transport dynamics of small particles through a polymer melt at temperatures spanning from liquid-like behavior down to near and below the simulated glass transition temperature. Using softness, a machine-learned scalar quantity, we connect the structural neighborhood surrounding a particle with its dynamic behavior to relate the probability of a glassy rearrangement to its local environment. An energetic and entropic scale for the rearrangement process of these small particles emerges and is compared across systems of different particle sizes and interaction strengths. Diffusion coefficients and relaxation times both show strong dependence on our tuning parameters, and the barriers to rearrangement show increasing nonlinearity as the particles become smaller. The trends we observe provide some insight into how local structure plays a role in small molecule transport when the surrounding medium is undergoing a glass transition, leading to large changes in system mobility.

Water-based inkjet inks typically contain non-volatile, polar compounds – referred to as co-solvents – such as glycerol and ethylene glycol oligomers, which constitute approximately 5–50 wt% of the total ink. The hygroscopic nature of both paper and co-solvents makes their interplay with atmospheric moisture a critical factor in controlling the penetration and drying dynamics of ink, as well as the long-term mechanical and morphological stability of the printed paper. In this study, we systematically investigate how co-solvent deposition influences equilibrium moisture uptake and how the ambient humidity influences the ink absorption dynamics into cellulose fibers. We find that co-solvent addition substantially increases moisture uptake and eliminates the sorption hysteresis present in paper. The moisture sorption of co-solvent-infused paper is well-predicted by the mass-weighted average of the individual, single-material sorption isotherms of paper and co-solvent. The rate of pore-fiber transport of co-solvents was observed to depend sensitively on ambient humidity, the presence of predeposited liquids as well as the addition of surfactants and divalent salts.

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Hydrogel coatings are increasingly applied to seeds to enhance hydration and support germination; however, their outcomes remain inconsistent, and the underlying physical mechanisms remain unclear. Here, we dissect the hydrogel–seed interface as a soft material system, isolating how water imbibition, mechanical confinement, and oxygen permeability govern germination dynamics. Using artificial and natural seeds, we show that water uptake follows classical Lucas–Washburn dynamics and is not impeded by the hydrogel coating. Instead, germination delays arise from two key physical effects: the mechanical stiffness of the coating and its restriction of gas exchange. In Petri dishes, soft coatings accelerate germination, suggesting minimal resistance to radicle emergence; however, this advantage disappears in soil, where all coatings delay germination regardless of stiffness. Controlled-pressure experiments in transparent soil rule out mechanical load as the dominant factor. Instead, selectively exposing the hilum and micropyle—critical sites for gas exchange—restores germination timing. These findings demonstrate that hydrogel-coated seeds are primarily limited by oxygen diffusion, not water transport, revealing how soft material interfaces modulate biological function. This work provides design principles for soft coatings that balance hydration, mechanical compliance, and gas permeability in bio-integrated systems.

Shape memory polymers (SMPs) are a class of stimuli-responsive materials with significant potential across diverse fields including soft robotics, biomedical devices, and mechanical engineering. To realize a scale transition from small molecules to a mechanical structure with excellent SMP behaviour, investigators typically need both material design and constitutive model establishment. Traditionally, the design of SMPs relies on empirical methods, limiting the speed of discovery and property tuning. Moreover, the prediction of their behaviour generally depends on theoretical and numerical tools that, however, require an in-depth understanding of theoretical mechanics. In contrast, machine learning (ML) offers a powerful tool to possibly overcome these limitations and has increasingly drawn attention from investigators in multiple fields. In this perspective, we critically review recent advances in the application of ML techniques to SMPs. We discuss major conventional concepts in the field of SMPs, basic procedures and important approaches to ensure ML-assisted SMP design, how different ML tools have been employed to identify new SMP chemistries and to predict their thermo-mechanical and shape memory properties. Despite these successful advancements, ML-assisted SMP discovery and thermo-mechanical modelling remain at an early stage. We discuss how they are limited, e.g., by incomplete structural representations and challenges in integrating thermal and temporal effects into the neural network. Finally, we outline future directions to explore and implement, including developing tools to capture complex SMP topologies and creating polymer-specific neural networks. The discussion allows us to provide new insights into the use of ML tools for the world of SMPs.