arXane › Physics › Condensed Matter
Wednesday, 4 November 2026: 12 new papers, 3 cross-lists and 6 replacements in Soft Condensed Matter (cond-mat.soft), in arXiv's order, with full abstracts. The same papers as arXiv's own listing, set as a newspaper; every title links to arXiv.
Self-propelled particles such as swimming micro-organisms often accumulate at boundaries. Active Brownian particles (ABPs), that is, particles moving at constant speed with an orientation undergoing rotational diffusion, provide a simple model. ABPs have a finite probability to be on the boundary. Their probability density therefore includes both regular bulk and singular boundary components. Exchanges between these represent capture at the boundary and re-entry into the bulk. We examine the distribution of ABPs in the stationary regime. We first obtain a similarity solution that describes the probability density near the turning points, where orientation is tangential to the boundary and particles re-enter the bulk. This shows that the bulk density is singular and the boundary density is non-differentiable. We then add spatial diffusion to the ABP model and investigate the limit $\eps \to 0$ of small spatial diffusivity. We use matched asymptotics to describe the regularising effect of a finite $\eps$, focusing on the vicinity of the turning points. We assess our predictions against numerical simulations using both lattice Markov chain and finite-element discretisations.
A central challenge in colloidal self-assembly is understanding how interaction strength influences the competition between structural optimization and kinetic arrest during the formation of hierarchical structures. Here, we investigate the assembly process of a binary mixture of differently sized silica colloids dispersed in a near-critical water/2,6-lutidine solvent, where critical Casimir forces provide reversible and temperature tunable attractions. By systematically tuning the effective interactions between large-large (L-L), large-small (L-S), and small-small (S-S) particle pairs, we directly observe how the nucleation and growth of the binary colloid assemblies evolve in a wall-confined quasi-two-dimensional geometry when multiple pair-interactions are present. Confocal laser scanning microscopy imaging shows that combined L-L interactions and L-S interactions lead to compact L-S core-shell clusters with high shell occupancy, while the additional presence of S-S interactions suppress the shell formation and lead to extended, branched binary networks. Particle-resolved analyses of cluster growth and bond dynamics reveal that these structural transitions are governed by the motion of the small particles in the clusters changing from highly dynamic leading to structural reorganization to frozen dynamics resulting kinetic trapping. Our findings demonstrate how the interaction-governed mobility and kinetic arrest of small particles influence the growth of finite binary clusters and offers microscopic insight into the formation of binary crystals and kinetically trapped space-spanning binary colloidal networks and gels.
Structural thixotropic viscoelastic models describe the rheology of human blood accurately, but their structural kinetics are postulated, so their compatibility with the second law of thermodynamics is not guaranteed. We present ROUGE (ROUleaux model Guided by free Energy), a tensorial model of blood that describes the deformation of individual red blood cells and of rouleaux with two conformation tensors, and the degree of aggregation with a scalar variable. A single postulated Helmholtz free energy determines the stress, the relaxation of all three variables, and the entropy generation, so the model satisfies the second law in any flow. To build it, we extend a simplified formulation of nonequilibrium thermodynamics, which accounts for conformational energy and entropy, to microstructures described by several structural variables of different kinds. The coupling between aggregation and rouleaux deformation is motivated by the Langmuir adsorption of fibrinogen and is restricted so that the quiescent, fully aggregated state is the stable minimum of the free energy. Fits to published data for step-ups and step-downs in shear rate show that the fibrinogen binding sites remain far from saturation, so that the free energy has no adjustable parameters. They also show that rouleaux breakdown requires no empirical shear-activation term: the structure is destroyed in proportion to the work done by the flow on the aggregates, and a single structural time produces both breakdown within seconds and an algebraic recovery over tens of seconds. The resulting model has five well-determined parameters, and its fitted solvent viscosity coincides with that of plasma.
The long-wavelength dynamics of two-dimensional (2D) nematic liquid crystals (LCs) is dominated by the motion of topological defects, which behave like interacting particles endowed with both topological charge and orientation. In experimentally relevant regimes, the translational dynamics of the defect positions can be accurately described by effective nematoelastic particle-particle interaction forces. By contrast, analogous quasistatic torque descriptions for the orientational dynamics typically fail due to a breakdown of time-scale separation, severely hindering the inverse design of texture response dynamics in nematic LCs. To overcome this obstruction, we introduce a linear phase-diffusion formulation that accurately predicts the orientational relaxation dynamics of pinned topological defects in the nonlinear 2D Landau-de Gennes theory. The broad practical potential of this approach is demonstrated by showing how it enables the inverse design of transient nematic patterns as well as the inference of hidden defect configurations from sparse measurements.
Wormlike micellar solutions exhibit formulation-dependent nonlinear startup responses, including stress overshoot, undershoot, and strain hardening, making prediction across compositions and shear rates challenging. We developed a multi-fidelity machine-learning framework to predict stress-growth curves in cetylpyridinium chloride (CPyCl)-based wormlike micelles from sparse experimental measurements. The framework combines a low-fidelity analytical model describing the overall startup response with high-fidelity experimental data capturing formulation-specific nonlinear features. This approach addresses the limited and uneven coverage of salt chemistry, concentration, and shear rate without relying solely on data-intensive neural-network training. The trained model predicts complete stress-growth responses at untested shear rates and enables extraction of strain-hardening characteristics from interpolated curves. By augmenting sparse rheological measurements, the framework provides a basis for exploring relationships among formulation variables, linear viscoelastic properties, and nonlinear startup behavior, while supporting more efficient characterization of wormlike micellar solutions.
Topological defects are commonly characterized by their charge, although defects with the same charge can have different internal morphologies. We study the annihilation of isolated $\pm1$ defect pairs in a two-dimensional compressible Toner--Tu model, varying the morphology of the $+1$ defect while keeping the initial charges and separation fixed. Activity produces a morphology-dependent mobility asymmetry: reversing the radial orientation of an aster reverses which defect travels farther, and asters show the strongest response. An early-time analysis reveals that the initial morphology selects distinct density modes whose gradients subsequently feed back on the polarization. Consistently, removing the density-gradient coupling strongly suppresses the asymmetry. These results establish defect morphology, in addition to topological charge, as a dynamical variable governing defect-pair motion in active polar matter.
Mechanical disturbances such as seismic shaking can set a granular slope in motion. Understanding the resulting transport before arrest is important for predicting topographic change, assessing landslide hazards and controlling powder flows in industrial processes. Experiments show that smaller disturbances can trigger avalanches and larger areas are mobilized as slopes approach their stability limit. Yet how much transport varies between events under the same macroscopic conditions and forcing remains unclear. Here we use numerical simulations to show that transport variability is amplified near the critical angle. We apply identical basal pulses to static granular layers with the same macroscopic conditions but different initial particle configurations. Although the mean transport increases approximately in inverse proportion to the angular distance from the critical angle, the standard deviation increases even faster and can exceed the mean. Thus, identical forcing can produce widely different transport amounts. Deterministic constitutive laws that assign a unique response to given macroscopic conditions cannot capture this variability. We introduce fluctuations into the energy balance to develop a stochastic model that captures the main features of the broadening transport distribution. These results show that assessing transport near the critical angle requires considering its distribution, not just its mean, and motivate constitutive descriptions that incorporate fluctuations.
Boundary conditions strongly influence bacterial turbulence, motivating the question of how hydrated porous substrates affect near-interface collective motion. We characterize the spatiotemporal dynamics of dilute and dense Escherichia coli suspensions above agar substrates of varying concentrations, with glass serving as a nonporous reference. In dense suspensions shortly after loading, collective in-plane kinetic energy varies nonmonotonically with agar concentration, while individual swimming energy and characteristic spatial scales show comparatively little variation. Over five minutes above agar, the collective flow attenuates strongly, accompanied by shorter spatial correlations, longer temporal correlations, and a greater relative spectral weight at higher wavenumbers. To explore how the porous boundary may contribute to these changes, we extend mean-field kinetic theory (MFKT) to incorporate normal seepage and tangential slip at the interface. Linear stability analysis distinguishes near-interface collective modes from those in the bulk and identifies how fluid-layer thickness and permeability-dependent slip length modify the instability cutoff. The predicted reduction in the associated length scale provides a qualitative interpretation of the smaller flow structures observed over time and at higher agar concentrations. These findings reveal distinct substrate-dependent changes in the intensity, persistence, and spatial organization of near-interface bacterial turbulence.
This paper addresses the packing fraction of polydisperse similar hyperspheres with a discrete binomial and continuous lognormal particle size distribution. First, a closed form expression is derived of a mix consisting of multiple discrete particles in D-dimensional Euclidean space R^D with small size differences. A recently developed analytical model for binary packings with small size ratio u is used, here applying it to the statistically probable particle pairs in the multi-sized mix. The new model is subsequently applied to discretely sized particles with a binomial number distribution and a constant size ratio u between each subsequent particle size. By taking the limit of u -> 1 and an infinite number of particles sizes, the binomial particle distribution is transformed into a continuous lognormal distribution. The resulting expression for the packing fraction can be solved in closed form, resulting in an original expression for the packing fraction of mixtures of hyperparticles with a lognormal distribution. This explicit expression is governed by the standard deviation, the Euclidean dimension D, and the monosized packing fraction of the considered particle shape. For a number of particle shapes and their packing modes (from looser to denser) the scaled expression is thoroughly compared with data reported in the literature. They concern packed particles with small standard deviations and in Euclidean dimensions D = 2 and 3, and good agreement is found. For large standard deviations the scaled packing fraction approaches a limiting value, which appears not to depend on the Euclidean dimension. Based on the insight, a closed form expression is put forward for the packing fraction that matches the small standard deviation solution, and the large standard deviation asymptote, so covering arbitrary standard deviations, Euclidean dimensions, particle types, and their mode of packing.
Particle-resolved colloid simulations are often limited by the fastest microscopic time scale retained in the model, which can be many orders of magnitude shorter than the collective process of interest. This is especially restrictive for aggregating colloids, where explicit near-contact potentials force the time integration to resolve the rapid positional relaxation of bonded pairs within narrow attractive wells. Here we introduce an overdamped constraint-based Brownian dynamics method that replaces the time-resolved integration of stiff near-contact forces by a geometric projection step. After each free Brownian update, hard-sphere exclusion, sticky bonds, and hard-wall interactions are enforced by projecting the particle positions onto the constrained configuration set. The method preserves hard-sphere structure and diffusion, recovers the aggregation kinetics of sticky hard spheres, and extends the physical time reached at fixed computational cost by one to two orders of magnitude relative to the explicit-potential simulations tested here. This acceleration makes Brownian-dominated, low-P\'eclet electrophoretic deposition accessible to particle-scale simulations, revealing a pathway in which the suspension first densifies and only subsequently develops a bonded network. Implemented as a LAMMPS fix, the framework provides a modular route to long-time Brownian simulations of dense constrained colloidal systems.
Hyperuniform fluids suppress density fluctuations at large length scales, but microscopic protocols for designing their asymptotic scaling remain limited. Here we show how effective driving noise generated by particle dynamics can select the hyperuniform exponent while preserving ordinary diffusion and pairwise center-of-mass conservation of the organized particles. We establish this connection through two protocols based on biased random organization (BRO). First, a two-dimensional BRO layer is nonreciprocally coupled to a three-dimensional Brownian gas through density-dependent kick amplitudes. Bulk density fluctuations generate spatially nonlocal noise with a $q^3$ power spectrum in the adiabatic limit, producing $S(q\to0)\sim q^1$. Second, we introduce reciprocal kicks whose lengths follow a Pareto-like distribution, $P(\ell)\sim\ell^{-1-b}$. For $2<b<4$, the finite second moment supports ordinary diffusion, while the divergent fourth moment changes the long-wavelength noise spectrum to $q^b$. The corresponding fluctuating density field theory predicts continuously tunable hyperuniform scaling, $S(q\to0)\sim q^{b-2}$, supported by particle simulations. Together, these results connect targeted hyperuniform scaling to explicit microscopic dynamics and provide a physical basis for designing collective density correlations in nonequilibrium particle systems.
Self-assembly enables complex structures to emerge from simple building blocks through local interaction rules. Are some structures intrinsically easier to design, that is, do they admit more sets of assembly rules than others? And, can this be predicted from their geometry? We address these questions using a three-dimensional polycube model with programmable face-to-face binding, defining a structure's assembly rule probability as the chance that a randomly chosen rule set produces it. For polycubes, this probability decreases exponentially with assembly complexity, the minimum information required to specify a structure's assembly rules. Intuitively, this follows because structures with low assembly complexity are generated not only by minimal rule sets but also by numerous more elaborate ones, and so arise disproportionately often. Symmetry can reduce assembly complexity by allowing the same building blocks and local interactions to be reused across different parts of a structure. Indeed, we find that the highest probability structures come from a tiny subset of highly symmetric structures. However, symmetry alone does not determine assembly complexity. Structures with the same symmetry can differ substantially in the amount of assembly information they require, depending on how effectively their global symmetry can be exploited by local growth rules. Overall, our results link information, symmetry, and assembly rule probability and suggest that identifying which geometric features permit rule reuse is central to designing minimal programmable self-assembling systems.
Accurate prediction of phase equilibria is essential for chemical process design, particularly in systems characterized by complex intermolecular interactions. This work investigates the influence of conformer selection and sigma-profile characteristics on COSMO-RS predictions of liquid-liquid equilibria (LLE), using the tetrahydrofurfuryl alcohol (THFA)-methyl isobutyl ketone (MIBK)-water system as a case study. Calculations were performed at the TZVP and TZVPD-FINE levels of theory. While TZVP failed to capture the experimentally observed phase behavior, TZVPD-FINE provided substantially improved predictions. The differences are attributed to the relative importance assigned to less-polar conformers and more-polar conformers capable of hydrogen bonding. Although the TZVPD-FINE calculations reproduced the main experimental trends, the predicted organic-phase composition showed a mean percentage deviation of 5.27%, slightly exceeding the adopted 5% agreement threshold. Investigations using individual conformers demonstrated that predictions can be strongly influenced by conformer selection and that representative conformer ensembles are necessary for reliable results. Further analysis showed that diversity in sigma profiles is more important for predictive accuracy than the total number of conformers. These findings emphasize the importance of carefully selecting conformers when applying COSMO-RS to liquid-liquid equilibria involving phases of contrasting polarity.
The ubiquitous power-law relaxation emerges from complex mechanical systems with a vast number of interacting exponential responses. Here we demonstrate that power-law responses satisfy a maximum entropy criterion, regulating the individual contributions under partial knowledge. This leads to a prediction that frequency range of validity decreases with power-law exponent, a property which is confirmed for some biological materials. The close relation with the fractional standard linear solid also leads to an incorporation of the equally ubiquitous stretched exponential behavior.
Understanding how nonequilibrium systems evolve between distinct statistical regimes remains a fundamental challenge in statistical physics. Here we show that particle deposition at an evaporating contact line provides an experimental realization of cooperative one-dimensional deposition, in which post-deposition capillary attraction drives particles to migrate, cluster, and coarsen. As deposition proceeds, the cluster-width distribution undergoes a striking re-entrant transition, evolving from an initial exponential form to a peaked Gamma distribution and subsequently returning to an exponential form. This dynamic transition is accompanied by a non-monotonic crossover in fluctuation scaling, $\sigma_w\sim\langle w\rangle^\kappa$, with $\kappa$ changing successively from $1$ to $1/2$ and back to $1$. Monte Carlo simulations reproduce these statistical transitions. Our results establish evaporative contact-line deposition as a simple model of correlated one-dimensional assembly and reveal how local interactions can produce re-entrant statistical behaviour in a nonequilibrium system.