Papers

We observe the emergence of extreme events in an active fluid involving two distinct chemical species that regulate active stress. One species is slow diffusing and the other is fast diffusing, and the growth of the fast-diffusing species is modelled using a nonlinear logistic term. We demonstrate the presence of extreme events in the temporal evolution of the concentrations, as well as in the spatial profile of the system,in regimes of merging-emerging soliton-like dynamics and spatio-temporal chaos, through analysis of the time-series, bifurcation diagrams, probability distribution functions of the concentration of the two species, return maps and distributions of inter-event intervals. Interestingly, we also find evidence of pronounced bunching of extreme events and super-extreme events in the slow chemical species in the soliton-like regime. We go on to systematically explore the dependence of the extreme event occurrences on the Péclet number and the strength of the nonlinear growth term, and find that the probability of extreme events increases after a critical Péclet number, while increasing the nonlinearity suppresses extreme events. Lastly, in order to gain further insight, we investigate a modified mode-truncated reduced order model comprising of coupled differential equations mimicking this active fluid system. We find that this reduced order model also exhibits extreme events whose emergence is correlated with a sudden expansion in attractor size due to a crisis arising from attractor collision.So these results demonstrate the existence of extreme events in an active fluid system, and are of potential relevance to biological phenomena where active transport plays an important role.

As stretchable electronics advance toward higher integration density and finer feature sizes, stretchable conductors, which serve as the architectural backbone of these electronics, must be scaled down accordingly. However, achieving both high stretchability and high electrical conductivity at high resolution remains a challenge using existing conductive materials. Here, through material design and interfacial engineering, we develop a thiol-functionalized conducting polymer/gold hybrid stack that can be patterned down to 4 micrometer linewidths using standard photolithography, while maintaining high stretchability, metal-like conductivity (>40,000 S/cm), and environmental stability. Leveraging this capability, we demonstrate grid-based stretchable transparent electrodes that surpass the figure-of-merit of indium tin oxide, as well as a 1000-pixel-per-inch image interconnected with stretchable lines. This work helps to broaden the scope of next-generation functional soft electronics, including e-skins and bioelectronics.

Buckled colloidal monolayers constitute a versatile soft-matter platform for engineering artificial spin lattices, with each particle serving as a single Ising spin. While the average Ising coupling energy has been approximately derived for perfect particle lattices, extracting the complete set of coupling parameters from real samples remains inaccessible. Here, we apply an inverse method to reconstruct all nearest-neighbor effective Ising coupling energies from measured colloidal spin configurations. We design multiple experimental protocols to control the thermodynamic state of the colloidal system, from isotropic compression and shear deformation to modulation of interparticle attraction, each giving rise to distinct spin configurations. Using spin configuration data, we reconstruct all nearest-neighbor effective Ising coupling energies using maximum likelihood estimation. To assess inference reliability without ground-truth model parameters, we propose to use the convergence of the standard deviation of the estimated couplings as a practical, ground-truth-free criterion, and validate its reliability using simulation data with known parameters. The extracted spatially resolved couplings reveal how each control protocol influences the sign, magnitude, statistical distribution, and spatial arrangement of the microscopic coupling parameters: isotropic compression strengthens antiferromagnetic couplings and enhances quenched disorder; shear deformation generates direction-dependent anisotropic couplings; and increased interparticle attraction drives a crossover from antiferromagnetic to paramagnetic and then to ferromagnetic couplings. This work establishes a practical inference framework for estimating effective model parameters of colloidal artificial spin lattices.

Nonreciprocal many-body dynamics can develop a swap phase characterized by spontaneous global oscillations far from equilibrium. To determine how local nonreciprocal interactions predict global oscillations and how broadly this prediction applies, we compare mean-field theory, independent-node theory, and nearest-neighbor spin-coupling theory for a two-spin nonreciprocal Ising model on networks of fixed coordination number (\(z=4\)). Nearest-neighbor spin-coupling theory retains nine independent joint edge probabilities. Monte Carlo benchmarks show that it predicts the oscillation amplitude, angular momentum, and period on edge-swapped networks with a 2.1% aggregate mean absolute relative error. We then extend the square lattice to degree-preserving edge-swapped networks and find sharply different behavior. With increasing system size, the oscillation amplitude and angular momentum decay strongly on the periodic square lattice but remain nearly size independent on edge-swapped networks. These results show that identical local parameters do not determine the macroscopic phase. The contrasting finite-size trends identify network geometry as a key determinant of collective oscillations at fixed local interaction parameters.

Studying the multistability of origami structures presents challenges due to the nonlinearity of their kinematics and the high-dimensional configuration spaces that are difficult to visualize and explore exhaustively. To address this, we utilize the Lagrangian framework for origami to exploit symmetries and obtain reduced-dimensional slices of the configuration space. Our analysis of degree-6 vertices with reflection symmetry reveals topological transitions in their kinematic space as sector angles are varied, with implications for the number of symmetry-constrained minima and the emergence of metastable regions. These lower-dimensional slices are amenable to exhaustive search and visualization. A subsequent full-space stability analysis shows that 18 of 41 degree-6 and 14 of 45 degree-8 symmetry-constrained minima remain minima when all locally compatible perturbations, including those that break symmetry, are admitted. The metastable regions, which would likely be overlooked by numerical optimization alone, are influenced by the interplay between the boundaries of admissible kinematic space and crease mechanical properties. We extend our analysis to cone-like vertices with higher symmetry and one-degree-of-freedom kinematics, exploring symmetry-breaking phenomena, combinatorial structure, and their consequences for branchwise stability. The stability landscapes uncovered have potential applications in mechanical metamaterials, mechanical computing, origami-based robotics, and structures designed to self-deploy and retain their shape.

Thixotropic and viscoelastic materials both retain their deformation history and are difficult to distinguish. However, a stationary linear response obeys the Kramers-Kronig relations. A structure that evolves during a sweep renders the spectrum inconsistent with them, so the residual from one chirp and no model measures nonstationarity. It survives the small-amplitude limit, returns to its baseline at any stationary state, and, for bounded restructuring, peaks near the structural time. We verify this on eight models and demonstrate it on published spectra of a thixotropic silica suspension.

In situ monitoring of reactions with chemical specificity and nanometer-scale spatial resolution under aqueous conditions remains a long-standing experimental challenge. Here, we use thin SiC membrane-enabled liquid-phase infrared nanoimaging and nanospectroscopy to monitor Ca$^{2+}$/Na$^{+}$ ion exchange in a mixture of disodium terephthalate and CaCl$_2$ solution. Beneath the membrane, we follow the formation of calcium terephthalate (CaTP), a metal-organic framework (MOF)-type coordination phase in which terephthalate ligands link Ca$^{2+}$ centers. The symmetric and asymmetric carboxylate stretching modes serve as chemically specific near-field reporters of Ca$^{2+}$ coordination to the terephthalate linkers. Nano-FTIR spectra reveal pronounced peak shifts and linewidth changes in these vibrational modes, indicating modification of the carboxylate coordination environment during CaTP formation. Complementary near-field nanoimaging resolves the emergence of nanoscale precipitates beneath the SiC membrane following CaCl$_2$ injection, providing spatially resolved evidence of the formation of the CaTP coordination framework. Solid-phase nanoimaging and nanospectroscopy identify well-defined CaTP particles, providing further evidence for crystalline CaTP formation. We demonstrate that the chemistry of metal-ligand interactions can be monitored in situ via liquid-phase near-field nanoscopy. This capability could enable the study of catalytic, electrochemical, and biological processes under native aqueous conditions.

Cells in connective tissues reside within the extracellular matrix (ECM), which consists of a fibrous mesh that exhibits non-linear strain-stiffening behavior, driven by a transition from bending-to-stretching-dominated deformation. While bulk rheology captures macroscopic mechanical properties, cells actively sense and respond to local microscale heterogeneities and stiffness anisotropy in their environment. Characterizing ECM micromechanics is therefore essential for understanding the mechanical cues experienced by cells. This study quantifies local stiffness anisotropy in stretched fibrous gels by combining experimental and numerical approaches. Experimentally, we utilized optical tweezers microrheology to measure local stiffness in fibrin gels subjected to uniaxial stretch. The gels demonstrated gradual local stiffening along both the tensile and perpendicular axes, with a more profound increase along the tensile axis, resulting in local anisotropy. To investigate the physical parameters driving this phenomenon, we developed a 3D finite element model of a discrete random fiber network, successfully replicating the experimental local stiffening and anisotropy. Numerical analysis further revealed that within the sub-isostatic region, both fiber thickness and network connectivity strongly influence local anisotropy: slender fibers and higher connectivity amplify the anisotropy by up to an order of magnitude. This contributes to the formation of a highly anisotropic local environment, thereby playing a significant role in directing mechanically driven biological processes, such as cell migration and durotaxis. Our simulations also indicate that local micromechanical responses may differ from the material's global stiffening behaviors, highlighting the need for characterization at the microscopic scale.

We study the bulk phase behavior of a polydisperse liquid crystal fluid made of biaxial boards with restricted orientations using both fundamental measure theory and Monte Carlo computer simulations. The continuous polydispersity is included in the intermediate particle length of the boards via a truncated Schulz distribution. By calculating several phase diagrams across a range of polydispersity coefficients, we find that polydispersity (i) enhances demixing between two uniaxial nematic phases and (ii) expands the stability region of the biaxial phase. Although metastable with respect to non-uniform phases, we identify certain mixtures exhibiting two-phase coexistence paths involving uniaxial-uniaxial and uniaxial-biaxial phase separations. Whether these paths can be completed depends on the precise shape of the parent distribution function. Monte Carlo simulations performed for a representative case exhibit the same phase diagram topology predicted by theory, thereby validating the theoretical approach. The main difference between both approaches lies in quantitative agreement, with simulated phase transitions systematically occurring at higher packing fractions than those predicted theoretically. Our combined theoretical and simulation results may prove relevant to the design and interpretation of sedimentation experiments on colloidal suspensions of polydisperse anisotropic particles.

Understanding interparticle radiation forces between spherical and nonspherical particles is crucial for the trapping of micro- and nano-objects in acoustophoresis. Most theoretical and numerical models treat spherical particles as identical to estimate interparticle forces, owing to their symmetry. Here, we elucidated interparticle radiation forces between three particles with different shapes and material contrast using experimentally validated numerical models. We performed a frequency sweep of the full acoustic device, accounting for viscous effects in a two-dimensional model, to predict the system's resonance. The pressure field obtained at the resonant frequency was then fitted to a 1D sinusoidal wave and used in a three-dimensional model based on the perturbation technique and tensor integral method to calculate the interparticle forces on the particles due to scattering and re-scattering of the acoustic field. Our results revealed a shape-dependent reversal of interparticle forces, even for positive-contrast materials. We found that for spherical particles, the interparticle forces are independent of material properties when the particles are positioned on the nodal plane. However, material contrast and particle asymmetry significantly affect interparticle forces when particles are placed outside the nodal plane. Finally, we studied the effects of the orientation of the asymmetric particle placed outside the nodal plane on the interparticle radiation forces. Interestingly, for the orientation angle of 0 degrees, the interparticle force is maximum for the asymmetrical particle and then gradually decreases to zero as we increase the orientation angle to 90 degrees. The detailed analysis presented in the paper will facilitate a better understanding of shape-based acoustic manipulation of microparticles.

Active disordered solids including dense human crowds and epithelial cells under confinement exhibit striking system-scale oscillatory spatiotemporal patterns. These are linked to a local feedback mechanism, self-alignment, that aligns the direction of a particle's motility vector to the total force. Simulations and experiments of solids made of such agents show these spontaneous oscillation patterns. Theoretically, they have been linked to both a nonlinear bifurcation and to selection of long-wavelength normal modes. Here we derive a closed nonlinear equation for the displacement field of active self-aligning 2d solids subject to angular noise. Along the normal modes of the solid, the dynamics of the mode amplitudes correspond to nonlinearly damped and stochastically driven harmonic oscillators. To linear order, we show that the system transitions from Active Brownian type correlated motion to oscillatory motion that increasingly condenses onto the lowest modes of the solid. We compare the analytical predictions for the mode spectra with simulations, finding excellent agreement approaching the transition from the disordered side. Strong nonlinearities manifest deep in the oscillating phase at strong alignment and small noise, consistent with the previous observations. At the continuum level, we derive a closed-form nonlinear wave equation for self-aligning solids. The transition to undamped oscillations is a second order dynamical phase transition driven by the competition between noise and alignment. At the linear level, we predict travelling acto-elastic waves, together with the emergence of system-scale oscillations for confined systems, consistent with observations in tissues and crowds. Our framework extends the understanding of self-aligning solids, which are pervasive among artificial and biological systems across multiple scales.

Urban street networks are modeled as spatial graphs whose geometry reflects planning, history, and physical constraints. Global orientation measures describe the overall order of a city but do not reveal how street directions form coherent neighborhoods. Here we introduce bearings communities—spatially connected domains whose streets share a common orientation—and analyze them using concepts from the physics of polycrystalline materials. Across $1025$ large cities worldwide, these domains are almost perfectly ordered: their mean coherence is $0.964$ and varies by only $1.4\%$ from one city to the next, far above the value expected for randomly oriented streets. Every city is therefore a mosaic of well-ordered orientational grains, differing only in their number, size, and mutual alignment. Because the grains are equally regular everywhere, a city's global order is set mainly by how many of them it averages over: a city appears ordered because it is built from a few large grains, not because its fabric is locally more regular. A null model in which grain orientations are randomized confirms this grain-counting mechanism. Finally, the interface fraction $\gamma$ measures how strongly domains interlock, yielding a morphology diagram that organizes cities continuously between grid-like, polycrystalline, and fragmented limits.

The 3D digital reconstruction of multi-scale, heterogeneous porous media is traditionally limited by the resolution boundaries and artifacts of X-ray computed tomography (XCT). This paper introduces a physically consistent 2D to 3D pore space synthesis framework that supersedes conventional geometric shuffling with dynamic Newtonian gravitational deposition using a high-performance rigid-body mechanics engine. To honor strict physical contact mechanics, statistical 2D targets, and morphological constraints, we develop a two-stage dynamic packing pipeline that combines realistic 3D grain shapes with a virtual sub-particle insertion engine. The framework is validated against synthetic and natural core benchmarks. First, we show that our stereological integral equation systematically converts size distributions; bypassing this inversion step induces a systematic 50% deviation in permeability predictions despite identical total porosities, independently altering flow channel topology. Second, temporary virtual spheres serve as architectural placeholders to stabilize large non-equilibrium dissolution voids, ensuring the reconstructed model satisfies target flow metrics within the prescribed tolerance. Finally, when applied to a fine-grained, weakly consolidated sandstone, the workflow exposes and suppresses systematic XCT artifacts, including phantom internal porosity. Ensembles of two-point correlation functions demonstrate that the hydrodynamically and structurally synthesized digital twins quantitatively exhibit closer structural proximity to the uncorrupted rock geometry than the reference XCT dataset itself. This methodology establishes a scalable foundation for high-fidelity multi-phase rock reconstruction, with potential extensions toward complex amorphous cement phases.

Slender elastic objects constrained to curved surfaces are ubiquitous in soft systems, with examples ranging from DNA wrapping around histones to actin filaments forming contractile rings on the cell membrane during cytokinesis. In the continuum limit, the conformation of these effectively one-dimensional (1D) objects is governed by both the geometry and mechanics of the embedding surface, and the elasticity of the object. Existing phenomenological typically describe such behavior through elastic energy minimization that incorporates bending and twisting, but neglect stretching that may be important for filaments with finite cross-section or are highly incompatible with the underlying surface. Here we present a coarse-grained geometric theory of 1D elastic curves constrained on rigid surfaces, derived from the first principles. For an isotropic material, our model emerges naturally in terms of the Young's modulus, Poisson's ratio, and area moments of inertia. The resulting effective energy functional explicitly includes stretching in addition to bending and twisting. We minimize it to determine the equilibrium curve conformations on different zero, positive, or negative Gaussian rigid surfaces. Our theory provides a general framework for analyzing the equilibrium configurations of surface-bound elastic curves in biologically and physically relevant settings.

Across biological scales, from cytoskeletal networks to whole tissues, orientational order and topological defects arise within complex three-dimensional geometries. However, quantifying nematic order orientational order with head-to-tail symmetry remains challenging: 2D projections introduce geometric distortions, while current 3D methods often struggle to resolve distinct nematic fields within curved or multilayer structures. Here, we introduce NEMO, a modular Python framework for the depth-resolved quantification of tangential nematic order and surface morphology. By reconstructing biological surfaces as triangulated meshes, NEMO projects curved intensity layers, extracts local nematic directors, and computes locally averaged nematic tensors within the tangent plane. The pipeline identifies topological defects and computes their topological charge by accounting for the Gaussian curvature of the underlying surface. Furthermore, NEMO quantifies tissue morphology through inter-surface distance and surface-fitted estimates of Gaussian and mean curvatures. We show the capacities of this framework using a synthetic nematic film on a vesicle and experimental actin organisation in Hydra. By combining customisable projections with surface-constrained analysis, NEMO provides a unified framework for quantifying the interplay between orientational order and geometry across scales.

To elucidate the physical mechanism of turbulence attenuation by polymers at each scale, we conduct direct numerical simulations of homogeneous isotropic turbulence in dilute polymer solutions. We model polymers as FENE dumbbells and simulate them using Brownian dynamics. By visualising the hierarchical structures of coherent vortices, we demonstrate that as the Weissenberg number increases, polymers progressively suppress vortices from smaller to larger scales, attenuating their turbulent energy. While inspired by Lumley's theory, we propose a novel timescale-based framework for analysing this attenuation process using the scale decomposition. We define a scale-dependent Weissenberg number, $\mathrm{Wi}_{\mathrm{sd}}(k)$, as the ratio of the polymer relaxation time to the turnover time of multiscale vortices at each wave-number. We reveal that when expressed in terms of $\mathrm{Wi}_{\mathrm{sd}}(k)$, the energy attenuation rate at each scale collapses onto a single curve that rises at $\mathrm{Wi}_{\mathrm{sd}}(k) \gtrsim 1$, proving that $\mathrm{Wi}_{\mathrm{sd}}(k)$ successfully describes both the onset and the degree of turbulence attenuation at any given scale $k^{-1}$. Furthermore, the scale decomposition uncovers that polymers preferentially align with the turbulent stretching direction at the scale satisfying $\mathrm{Wi}_{\mathrm{sd}}(k) \approx 1$. Based on these results, we establish a physical picture of the polymer–turbulence interaction and explicitly link it to the statistics in turbulence attenuated by polymers.

Pressure and concentration profiles from non-equilibrium molecular dynamics (NEMD) simulations are often used to infer water transport mechanisms in dense and swollen polymer membranes. However, the mechanical restraints needed to stabilize simulated membranes can alter these profiles and their interpretation. Here, we test whether restraint-induced differences in pressure and water concentration profiles are accompanied by changes in molecular water dynamics. Crosslinked poly(ethylene glycol) diacrylate (PEGDA) membranes were simulated using three support strategies: a graphene support lattice, frozen membrane atoms near the permeate interface, and homogeneously distributed frozen membrane atoms. The restraints produced markedly different profiles, ranging from solution-diffusion-like to pore-flow-like, while overall water fluxes remained comparable. Molecular water dynamics, however, changed little. Interfacial exchange was strongly bidirectional and far exceeded the net permeation flux, membrane water diffusivities were similar, and complete crossings occurred in both directions. Local water associations were short-lived, and directional velocity correlations decayed rapidly with time and distance. Thus, substantially different pressure and concentration profiles can arise despite similar molecular water dynamics. Such profiles alone are therefore insufficient to identify transport mechanisms in mechanically restrained NEMD simulations. In PEGDA, water motion is dominated by stochastic diffusion with a small net directional bias.

Reciprocal flows repeatedly return fluid elements to their initial positions, producing no net advective transport on time average. Nevertheless, their interplay with diffusion gives rise to nontrivial transport. To describe this phenomenon, we develop a general theory of effective diffusion under two-dimensional linear flows with arbitrary time dependence. By analyzing the advection-diffusion equation, we derive exact expressions for the mean square displacement and the effective diffusion coefficients for extensional, simple shear, and rotational flows within a single framework. We show that the reciprocal flows universally induce diffusion enhancement. The diffusion tensor exhibits pronounced anisotropy, which can result in directional diffusion suppression in spite of the direction-averaged diffusion enhancement. Our results provide a general framework for diffusion control by time-dependent flows and can provide new strategies for transport manipulation in microfluidic and biological systems.

Chirality, a systematic rotational bias in the motion of a particle, arises in a wide range of physical and biological systems, from charged colloids in magnetic fields to swimming bacteria near surfaces. While its effects in homogeneous environments are well understood, the interplay between chirality and structural disorder has remained largely unexplored. Here we investigate the chiral random walk on two-dimensional percolation clusters above the percolation threshold, combining numerical simulations with an analytically tractable annealed-disorder approximation. We find that the long-time diffusion coefficient depends non-monotonically on both the chirality parameter and the obstacle density: for every obstacle density above the percolation threshold, there exists an optimal chirality that enhances diffusion relative to the achiral walk. We show that the optimal chirality is set by an edge-adhering mechanism: maximum diffusion is achieved when the persistence length of the wall-adhering motion matches half the typical obstacle cluster perimeter. This yields a closed-form prediction for the optimal chirality in terms of geometric properties of the medium alone, which we verify across the full range of obstacle densities studied. The enhancement extends to first-passage statistics, where chirality shortens typical search times at strong disorder while lengthening them at weak disorder, with direct implications for biological navigation in disordered environments.

Chiral active fluids, composed of continuously rotating active constituents, exhibit a wealth of nonequilibrium phenomena arising from the interplay of activity, hydrodynamic interactions, and broken mirror symmetry. While these systems naturally generate circulating flows and active turbulence, converting their microscopic rotational motion into directed macroscopic transport remains an open challenge. By means of mesoscale hydrodynamic simulations, here we show that channels with an intrinsic asymmetry result in the spontaneous formation of a persistent net flux in the absence of externally imposed pressure gradients or body forces. Our results can be extrapolated to various geometric confinement strategies providing efficient and versatile strategies for transforming chiral activity into autonomous fluid transport, such that they can be utilized as a promising platform for self-powered microfluidic pumping.

DNA nanostructures are emerging as programmable components for engineering synthetic cell membranes, yet how their collective packing and deformability regulate molecular dynamics at membrane interfaces remains poorly understood. Here, we investigate the packing and mobility of DNA nanostars with tunable stiffness on lipid-coated droplets. The negatively charged nanostars spontaneously adsorb onto cationic membranes, and their interfacial packing is changed by the bulk DNA concentration and droplet size, which together determine the number of encapsulated nanostars. Combining fluorescence recovery after photobleaching experiments with coarse-grained simulations, we reveal distinct packing-dynamics relationships for rigid and soft nanostars. For rigid nanostars, diffusion first decreases gradually and then drops sharply with increasing interfacial packing, approaching a dynamically arrested state consistent with jamming-like behavior. In contrast, at equivalent experimental conditions, soft nanostars systematically reach lower interfacial packing fractions and show a weaker decrease in apparent mobility. This behavior is consistent with their weaker membrane affinity, which facilitates adsorption-desorption with the bulk. When this exchange is suppressed in simulations, crowding reduces the lateral diffusion of both nanostar types, but produces distinct dense configurations: soft nanostars become strongly deformed, whereas rigid nanostars largely retain their shape and form interlocked, gear-like arrangements. Nanostar adsorption also impedes lipid diffusion, while differences in membrane affinity result in distinct lipid mobile fractions. These findings reveal how the interplay between nanostar packing and deformability regulates molecular transport at membrane interfaces, providing a physical design principle for tuning lateral fluidity and crowding in artificial cells.

Self-organisation of granular systems is a key determinant of their macroscopic behaviour and has been studied extensively in assemblies of hard particles. We use numerical simulations to test this understanding in mixtures of hard and soft particles, focusing on cells, the smallest irreducible loops of the contact network, as structural descriptors. We show that, while the cell statistics display robust qualitative features under isotropic compaction, they depend on inter-particle friction, $\mu$, and soft-particle fraction, $\kappa$. Specifically, (i) the quadron area distributions retain a $\Gamma$ form, albeit with parameters that vary systematically with $\mu$ and $\kappa$. (ii) Predictions of the cell order distribution (COD) by maximising the entropy, without taking mechanical stability into consideration, become increasingly inaccurate at large cell orders. (iii) Irrespective of $\mu$, the normalised cell stress distributions collapse onto one master Weibull form, whose only shape parameter depends weakly on $\kappa$. This suggests a quasi-universal form that may deteriorate slightly at very high fractions of soft particles. (iv) Cells align preferentially along the local major principal stress direction, showing the same coordinated stress–structure self-organisation as in hard particles. The relative robustness of cell statistics to the addition of soft particles suggests that hard and hard–soft granular mixtures can be described by one model.

Using computer simulations, we investigate the collective behavior of two-dimensional active Brownian particles (ABPs) with excluded-volume interactions in the bulk and in the presence of a single static circular obstacle. Perception-mediated interactions are introduced through a vision-based steering mechanism that enables each particle to reorient its propulsion direction according to the instantaneous positions of neighboring particles within a prescribed vision cone. In the bulk, these intelligent active Brownian particles (iABPs) exhibit distinct collective states, including aggregated clusters, worm-like chains, worm-aggregate coexistence, and dilute gas phases. We characterize these states using a shape anisotropy parameter and construct the corresponding phase diagrams. The presence of a static obstacle, which interacts with the particles through purely repulsive forces, qualitatively alters their collective behavior. In contrast to conventional ABPs, whose isotropic accumulation around the obstacle increases with activity, iABPs exhibit the opposite trend, with boundary accumulation decreasing as the activity increases. We further identify empirical scaling relations that describe particle accumulation and cluster formation at the obstacle boundary. Consequently, the relative effective diffusion coefficient of iABPs displays a nonmonotonic dependence on self-propulsion speed, whereas that of conventional ABPs decreases monotonically with increasing activity. In addition, we show that the residence times of iABPs are orders of magnitude shorter than those of conventional ABPs, indicating that perception-mediated interactions can be useful for controlling the organization and transport of active particles in complex environments.

An object releasing a chemical into a surrounding flow leaves behind a plume that encodes the rate of release, but for most solutes, that plume is invisible. Classical transport theory describes this configuration through a self-similar concentration boundary layer whose structure has never been resolved experimentally in forced flow at low Reynolds number: a century of measurements at these conditions has returned integrated transfer rates rather than the field itself. Here we show that both the field and the information it carries are experimentally accessible. Around hydrogel posts photopatterned inside a microfluidic channel, a released solute drives suspended colloidal particles away by diffusiophoresis, i.e., their drift along chemical gradients, carving a particle-free “shadow” downstream. Using a fluorescent solute, we resolve the concentration field around the post and recover the classical self-similar structure of convective mass transfer. For an invisible surfactant, the width of the shadow, benchmarked against a directly imaged solute on the same platform, then serves as a proxy for the dimensionless rate of mass transfer from the post. Finally, comparing two orientations of a triangular post tests a classical prediction by Brenner: the local concentration fields rearrange, yet no orientation dependence of the integrated transfer rate is resolved. For solute–tracer pairs whose diffusiophoretic response is known, colloidal shadows thus turn ordinary tracer particles into quantitative reporters of chemical exchange that cannot be observed directly.

We find a striking departure from simple electrostatics in a family of two-dimensional lattice models whose effective description takes the form of an emergent electromagnetism: a high-order multipolar charge configuration can induce an electric field characteristic of a lower-order multipole. Studying the archetypical hardcore dimer and U(1) spin-1/2 link models, we trace this to a breakdown of the superposition principle: for dense charge configurations, there can be a nonlinear suppression, or even enhancement, of the far field compared to the linear expectation. Our work establishes that microscopic lattice-scale constraints in models for emergent electromagnetism can change the far-field behaviour to differ from often utilized long-wavelength descriptions in standard effective continuum models.

In this study, we investigate impact-wave propagation in a pre-stressed compressible hyperelastic bilayer resting on a frictionless rigid substrate within the framework of nonlinear elasticity. Semi-analytical solutions for the transient displacement fields induced by localized surface impulses are derived using the Fourier-Laplace transform, and their long-time asymptotic behavior is obtained through the method of stationary phase. Finite element simulations are further performed to validate the theoretical predictions. The results establish a direct connection between characteristic features of the transient displacement fields and the group velocities of the guided-wave modes, revealing how dispersive propagation and modal contributions shape the observed waveforms. Three representative bilayer models corresponding to human articular cartilage, gingiva, and skin are then considered to examine the effects of variations in layer stiffness and thickness. The results show that structural and material variations can substantially modify the spatial distribution, arrival positions, and characteristic velocities of impact waves. In particular, higher-order modes can make significant contributions to the displacement response in certain parameter regimes, such that the dominant features of the wavefield cannot always be characterized by the fundamental mode alone. These findings demonstrate that transient-wave measurements contain mechanical information beyond that captured by a single characteristic surface-wave velocity and provide a theoretical basis for interpreting transient wavefields in multilayered soft materials and for future non-invasive mechanical characterization.

We study the statistical properties of passive and active many-body systems with orientational degrees of freedom on non-orientable domains. By rephrasing topological constraints as non-local symmetry relations on an orientable double-cover, we show that non-orientability eliminates global rotational soft modes without acting like an external field. In a passive XY model, this results in topological caging, where orientational fluctuations that exhibit conventional diffusive behavior on a torus saturate on a Klein bottle to a finite value that we compute exactly in the thermodynamic limit. In models of active self-propelled particles with orientational degrees of freedom, topological caging persists despite continuously changing interaction neighborhoods. In an active Ising spin model, non-orientability enforces the coexistence of ordered anti-parallel domains with vanishing global polar order, a state that is absent on orientable domains.

Understanding protein crystallization pathways is essential for controlling crystallization in structural biology, materials science, and pharmaceutical applications. Classical nucleation theory does not fully capture crystallization processes for several proteins, including ferritin. Here, we combine acoustic levitation with small- and wide-angle X-ray scattering (SAXS and WAXS) to monitor ferritin crystallization in evaporating aqueous polyethylene glycol (PEG) solutions. Acoustic levitation rapidly drives the droplets through a broad range of protein and polymer concentrations, enabling time-resolved measurements of crystallization during evaporation. The scattering data show that ferritin crystals form during evaporation and subsequently lose their crystalline order upon further dehydration. Varying the PEG molecular weight switches between distinct crystallization pathways: one dominated by attractive protein-protein interactions and another dominated by repulsive interactions and excluded-volume effects. Furthermore, we find that lower molecular weight PEG (1000 g/mol) suppresses the dehydration-induced loss of crystalline order observed for higher molecular weight PEG (6000 g/mol), providing a simple strategy for improving protein crystal stability.

The characteristic intersection angle of the desiccation crack relaxes from near \ang{90} toward \ang{120} under repeated drying–wetting cycles. However, the theoretical understanding of this relaxation is insufficient, especially the modeling of the drying–wetting cycles. Here we introduce a phase-field model of desiccation fracture, extending the model proposed in previous studies by adding crack healing and a scar effect left by past cracks. By repeating drying–wetting cycles in a finite element simulation, we find that the angle distribution develops a growing peak near \ang{120} as the cycle number increases, consistent with experiments. The standard deviation of the intersection angle from \ang{120} relaxes exponentially with a characteristic time of about 2.85 cycles. These results are consistent with experiments, except that the characteristic time is slightly smaller than the experimental value. Crack energy dominates the total energy and also relaxes exponentially with nearly the same characteristic cycle as the angle relaxation. This decay is driven mainly by a shortening of the effective crack length rather than a change in effective fracture toughness.

Saddle points in the energy landscape of granular packings dominate the discrete steepest descent dynamics and ultimately determine the path that an out of mechanical equilibrium packing will follow and the resulting stable minimum that it will find. The saddle points that ultimately determine the resulting minima tend to be low-index saddle points. For models with an analytic energy landscape, such as the $p$-spin model, the steepest descent minimization path is affected by higher-index saddle points, which pull the system towards saddle points of decreasing index before arriving at the minima. Here, we examine the steepest descent minimization path of granular packings and compare them to the $p$-spin model. We show that the granular packing steepest descent minimization paths act like their smooth energy landscape counterparts and get attracted by saddle points. The index versus time curves for all models follow a shifted, stretched exponential. We further show that the shape parameter for the granular packings is unchanged when the energy landscape is modified to become analytic (Gaussian potential in a harmonic well) or non-local (Mari-Krzakala-Kurchan). The $p$-spin, on the other hand, has a significantly larger shape parameter. The reason is not due to the dimensionality, packing fraction, nonanalyticity, or the locality of the Hamiltonian of the models. The exact reason for the discrepancy in the shape parameter is \st{still} an unsolved mystery.