Papers

Connecting the autonomous kinematics of active matter to its emergent macroscopic transport is constrained by the requirement for high-resolution Eulerian velocity fields. Here, we use confined bacterial suspensions as a model active fluid, mapping the sparse Lagrangian trajectories of fluorescent spy cells into (2+1)-dimensional geometric braids that directly encode the spatiotemporal entanglement of the flow. We use the finite-time braiding exponent (FTBE) as a proxy to quantify the topological entropy and chaotic mixing of confined bacterial suspensions. In moderately confined wet systems, we find that hydrodynamic coupling drives a structural transition from a dilute active gas to coherent vortices, and ultimately to active turbulence, revealing three distinct density-dependent regimes of topological mixing. Conversely, truncating the hydrodynamic screening length via extreme confinement drives the system toward a dry active matter limit. In this limit, dense in-plane steric collisions suppress irreducible entanglement and substantially reduce the FTBE at high particle densities. By evaluating the topological complexity generated per encounter, we reveal a transition from a discrete geometric encounter regime to an areal escape mechanism. Finally, we establish a square-root scaling between the FTBE and the effective diffusivity, placing the self-sustained mixing of active fluids into the pathline braiding universality class.

The assembly of multicomponent nanoparticles is often kinetically controlled and exhibits strong pathway dependence. Transport, solvent exchange, nucleation/growth, and collision-driven coalescence together determine not only ensemble-averaged properties but also particle-to-particle compositional heterogeneity. Here, we present a computational modeling framework for predicting nanoparticle property distributions by coupling processing conditions, early-stage self-assembly physics, and molecular chemical details with kinetic Monte Carlo (kMC) simulations. The framework combines (i) mixing conditions with solvent-exchange-mediated particle initialization and growth, and (ii) kMC simulations that resolve stochastic collision histories, electrostatics-controlled coalescence, and composition at the level of individual particles. Applied to mRNA lipid nanoparticles, the model predicts size-loading correlations and provides insight into how processing-dependent assembly pathways lead to heterogeneous payload distributions. The kMC simulations further provide merging lineage histories, which explain the emergence of log-normal volume and payload distributions through multiplicative particle-growth pathways. The same framework is also applied to PEI-DNA polyelectrolytic complexation, yielding single-particle-resolved DNA-PEI stoichiometry distributions. The framework and its open-source implementation, FormLNP, provide a process-aware route to predicting and controlling single-particle property distributions across a broad range of multicomponent nanoparticle systems.

The self-assembly of colloidal particles enables the creation of structured materials with programmable functionalities; however, controlling interaction specificity and aggregate morphology in a reversible and scalable manner remains a major challenge. Here, we investigate the selective depletion-induced self-assembly of 3D-printed flat polygonal colloids, where nanoscale surface topography is engineered through precise modeling in two-photon polymerization. By designing anisotropic lateral surfaces, we direct specific interactions that govern aggregate morphology, yielding dimers, chains, zigzag, and honeycomb structures depending on the surface configuration. The specificity of interaction is tuned by varying the length scale of the topographic surfaces, the depletant concentration and the ionic strength of the solution, revealing a transition from selective to non-selective aggregation regimes. The relative placement of lateral interacting surfaces on the colloids enables assembly into aggregates spanning a broad range of sizes, while tuning the interaction strength selectively stabilizes distinct structural motifs. We demonstrate this interplay between geometric arrangement and interaction energy experimentally and corroborate through both theory and simulations for specifically hexagonal shaped colloids. This study establishes a versatile framework for programming colloidal interactions via micro-architectural design, offering new routes for fabricating reconfigurable and functional soft materials.

Spatial confinement and chemical screening fundamentally reshape the non-equilibrium phase behavior of autophoretic active particles. Here, we present a systematic study mapping the collective dynamics of self-propelled particles governed by chemo-attractive translational forces ($\mu_t < 0$) and chemo-repulsive rotational torques ($\mu_r > 0$) across varying screening parameters $\kappa$, torque magnitudes $\mu_r$, and boundary condition coefficients $\Lambda^c$. Beyond standard chemotactic macro-phase separation and dynamic clustering, we report the emergence of novel boundary- and screening-induced bubbly phases, classified into boiling and bursting bubbles. Using a metric triad of steady-state cluster fraction $\langle S \rangle$, temporal fluctuation magnitude $\sigma_S$, and coordination number $\langle Q \rangle$, we draw phase diagrams to demarcate phases for no-flux boundaries ($\Lambda^c = 1$) and chemically permeable interfaces ($\Lambda^c = 0$) . Increasing chemical screening ($\kappa$) systematically suppresses long-range attraction, driving sequential phase transitions from macro-scale collapse toward bubbly states, dynamic micro-clusters, and homogeneous gas phases, while simultaneously inducing aggregate shape anisotropy. These findings provide predictive design rules for controlling active assembly and transport in microfluidic environments.

The localized electromechanical phase transition in dielectric elastomers involves complex moving boundaries and severe electrostatic fringe fields driven by high-curvature interfaces. Traditional phenomenological models fundamentally underestimate the configurational forces by completely ignoring the in-plane electric field components and geometric singularities. Here, we present a asymptotically exact geometric framework to resolve the domain wall propagation. By mapping the highly deformed current configuration to a regular parametric strip via conformal mapping, the field singularities are algebraically eliminated. An exact integration by parts directly translates the higher-order geometric metric into a rigorous topological mass term, projecting the global conformal electrostatics into a non-linear $\sigma$-model Lagrangian density. Incorporating the Gent strain-stiffening model, the continuous translation symmetry yields a Hamiltonian first integral. Eigenvalue analysis and Bogomol'nyi-Prasad-Sommerfield (BPS) bound calculations prove that the domain wall emerges strictly as an asymmetric heteroclinic orbit connecting two saddle points. This geometric resolution provides exact analytical scalings for the localized interface energy, domain wall thickness, and asymptotic decay lengths, eliminating all phenomenological parameters and offering a deterministic paradigm for geometric instabilities in active soft matter.

Couplants are indispensable for ultrasound-based applications to ensure seamless propagation of the acoustic waves between the transducer and targets. Existing hydro/oil-based gel couplants provide excellent acoustic coupling but are single-use and therefore not suitable for emerging applications such as wearable health monitoring, ultrasound-based biometric devices, and prolonged sonodynamic therapy. Alternatively, polymer-based solid couplants have been explored as reusable solutions but generally fail to simultaneously provide effective mechanical conformability and efficient acoustic transmission. Here, we propose an innovative composite material design based on a silicone elastomer matrix, in which mechanical and acoustic properties are tailored independently through the microencapsulation of diols and triols. An emulsion-based material formulation strategy is employed to produce a family of high-liquid droplet content elastomeric composites containing up to 75 volume fraction (%) of liquid. A robust material formulation strategy is established that enables independent parametric control over the resulting mechanical and acoustic properties. A specific composition containing 70 % glycerol has been identified and explored for biomedical applications, exhibiting mechanical softness comparable to that of skin and acoustic properties matching those of soft tissues. The material has been integrated into a functional ultrasound characterization device, demonstrating structural homogeneity and applicability of the proposed couplant. Furthermore, the composites exhibit mechanical conformability to stiff substrates with surface irregularities, making them also promising candidates for industrial applications.

Fluid shear can induce detachment of a thin elastic sheet adhered to a flat substrate. This peeling process is important in a variety of environmental and technological systems. The condition for peeling depends on: the shear rate $\dot{\gamma}$, the fluid viscosity $\eta$, the length of the detached portion of the sheet $L$, the bending rigidity $B$ and the adhesion energy $\Gamma$. What are the laws governing the detachment? We address this question experimentally in the regime of intermediate adhesion, using macroscopic sheets bonded to a substrate and immersed in a shear cell containing a viscous fluid. The experiments indicate a critical shear rate for peeling of the order of $\dot{\gamma} \sim B/(\eta L^3)$. This threshold is, unexpectedly, independent of adhesion. We rationalise this result by applying Griffith's fracture theory to optical measurement data of the shape of the sheet, under conditions of freely moving peeling front or clamped boundary. The results indicate that the large curvature of the sheet for $\dot{\gamma} \sim B/(\eta L^3)$ yields a nearly diverging strain energy release rate at this threshold. This approximate divergence in turn yields a peeling threshold that depends at most weakly on $\Gamma$, confirming a theory that was proposed recently (Salussolia et al., J. Mech. Phys. Solids, 2020, 134). Among other applications, our work provides a quantitative formula that can aid the production at scale of 2D materials such as graphene.

Enzymatic degradation of biopolymers underpins critical processes in biotechnological applications and natural processes, such as food digestion and cancer development, yet the interplay between enzyme propagation in a dense substrate and substrate degradation remains unresolved. It remains especially unclear whether degradation of a dense matrix facilitates or impedes enzyme propagation. We simultaneously track with spatiotemporal resolution unidirectional enzyme diffusion and biopolymer degradation in a model system. We demonstrate that enzyme diffusion is decoupled from catalytic activity, while the degradation front progression is dictated by enzyme diffusion, reaction kinetics and slow enzyme deactivation. These findings establish a quantitative framework to optimize enzymatic processes for applications in biomedicine, biomass valorization, and nanotechnology.

A copper-based metal-organic framework, Cu(Im)2, was synthesized using a sol-gel process and subsequently melt-quenched into glass upon heating above 240 {\deg}C. In this paper, we present a multimodal, multiscale interrogation of the MOF nanocrystals and the resulting glasses. Structural characterization using X-ray diffraction, atomic force microscopy, and electron microscopy was performed to understand the morphology and size of the synthesized nanocrystals and glasses. Thermogravimetric analysis and differential scanning calorimetry were employed to understand the melting process of the crystals to form the glass. Synchrotron pair distribution function analysis was performed to verify that the framework structure is maintained upon melting, and nearfield infrared nanospectroscopy provided insight into the local chemical structure of the materials. The mechanical characterization via nanoindentation revealed that the resulting glasses exhibit an appreciably high elastic modulus (~10 GPa) and the highest fracture toughness (K_1c ~ 0.5 MPa m^1/2) yet reported for MOF glasses. This development is central to the emerging field of MOF-based materials processing and shaping, while upholding mechanical robustness and resistance to cracking.

The vertex model is widely used to describe the mechanics of epithelial tissues, but its conventional formulation assumes that all cells remain tightly packed and always share edges with their neighbors, making it difficult to represent local detachment or gap formation. Here, we propose a minimal extension of the vertex model that enables cell detachment by introducing a new topological transformation, T2-inverse, which acts as the inverse of the classical T2 transition. When the imbalance of forces acting on a vertex, quantified by a tension metric $T$, exceeds a threshold, the T2-inverse splits the vertex into multiple vertices and creates a closed polygon that is incorporated as a pseudo-cell. This operation allows the model to represent the emergence and propagation of local detachment events. Using this framework, we simulate the stretching of a cell sheet and show that force-induced local detachments can accumulate to produce macroscopic tissue rupture. These results demonstrate that the proposed model extends the capability of the vertex model to describe tissue-level breakdown processes, including detachment and tearing, and provides a foundation for studying a broader class of epithelial mechanical phenomena.

We employ pseudospectral methods to solve the homogeneous Ornstein-Zernike (OZ) equation for a model fluid in the vicinity of the critical point. Focusing on the Mean-Spherical Approximation (MSA) as a closure to the OZ equation, we obtain numerical estimates for the critical exponents $\eta$, $\delta$ and $\gamma$ for a system of hard-core Yukawa particles both in two and three dimensions. The three-dimensional MSA exponents are already well-known from an analytic solution and are recovered by our numerical methods. The two-dimensional exponents are a new output of this work. The pseudospectral method allows for rapid and highly accurate solution of liquid-state integral equation theories, and enables calculations on truely infinite domains, as needed for highly correlated states. In addition, we analyse the standard Picard iteration scheme and propose a variation of it which provides increased stability and speed of convergence.

Inert colloids half-coated with platinum (Pt) are a standard model of chemically powered active particles, yet the microscopic origins of their propulsion in hydrogen peroxide (H2O2) remain difficult to dissect experimentally. Whereas self-diffusiophoresis was the prevailing theory, self-electrophoresis has been more recently suggested as the main mechanism of propulsion. According to the latter mechanism, the pole-to-equator Pt-thickness gradient produced by directional metal deposition is sufficient to create anodic and cathodic regions on the metal cap and thereby generate an electric field sustained by H2O2 decomposition. Enhancing self-propulsion performance of such particles thus requires precise control over the Pt thickness distribution, which is currently not achievable with standard methods (e.g. evaporation or sputtering). Here, we propose a method to fabricate Janus active particles by assembling silica microspheres on patterned substrates containing spherical grooves whose depth and spacing set the degree of particle coating while simultaneously suppressing proximity-led defects (Pt bridges). The resulting particles exhibit a tunable platinum-thickness contrast, as verified by Focused-Ion-Beam cross-sections. In 2.5% H2O2, our results suggest that this control can significantly increase propulsion efficiency, while providing evidence indirectly supporting the hypothesis that self-electrophoresis is the dominant mechanism. These results demonstrate that our patterned-substrate route can enhance control over the catalyst deposition and enable novel Janus morphologies, allowing for more precise engineering of active colloids.

Customizing the surface characteristics and stimuli-responsive behavior of polyelectrolyte (PE) during interaction with nanoparticles (NP) have potential applications ranging from biological interactions to advanced diagnostic technologies. Using numerical simulations, we investigate how charge regulation (CR) shapes the conformation, dynamics and adsorption of PE on a oppositely charged NP and compared the results with constant charge (CC) conditions. At low solvent ion concentrations, CR barely affects the adsorption fraction, while significantly enhancing the dynamical response of the PE. However, at high ion concentrations, while complexation is suppressed in the CC case, the CR environment helps to overcome the energy barriers related to Debye screening and thermal fluctuations, thereby promoting the adsorption. By maintaining the strong PE-NP electrostatic interaction, PE exhibits a more directed ballistic-like dynamics towards NP during adsorption, in comparison to the diffusive and sub-diffusive behavior observed in the CC case. Our results emphasize that in high ion concentrations, with a strong Debye screening effect, a small fraction of charge-fluctuating sites suffices for adsorption to happen. A tunable CR mechanism is thus an efficient and robust strategy to control the biomolecular interaction and stability in complex environments.

Understanding the internal architecture of copolymer microgels is crucial for establishing how nanoscale polymer organization controls their stimuli-responsive behavior. Here we focus on thermoresponsive P(N-isopropylacrylamide-co-N-isopropyl-methacrylamide), P(NIPAM-co-NIPMAM), microgels with varying mole fraction of the components, synthesized via radical precipitation polymerization, and we demonstrate that changes in their volume phase transition and equilibrium swelling are governed by composition-dependent internal heterogeneity. Comparison between small-angle neutron scattering (SANS) with isotopic labeling and monomer-resolved simulations show a block-like monomer distribution of the two components. SANS analysis reveals a universal maximum in the polymer mesh correlation length near the transition, evidencing coexistence of collapsed NIPAM-rich and swollen domains. The correlation length increases with increasing NIPMAM content, with a maximum for 75 mol % NIPMAM, implying sparse collapsed regions within the network and thus a large degree of heterogeneity induced by the presence of an increasingly large fraction of intercalated, non-collapsing PNIPMAM. The maximum heterogeneity correlates with the equilibrium swelling ratio, indicating that collapsed microgels retain a structural memory of the transition and present a less-compliant structure in response to temperature variations. Overall, these insights highlight a complex effect of the block-like monomer distribution on the responsive properties of copolymer microgels with different compositions, thus providing a design rule for tailoring responsive colloids for functional soft materials.

The multi-state Bennett acceptance ratio (MBAR) equations combine the data collected under different thermodynamic conditions in a statistically optimal way. Due to their practical importance, several solution strategies have been devised to optimize the convergence of the resulting set of coupled equations. However, even with graphics processing unit (GPU) acceleration, the convergence of these methods can still be either unstable or slow. We propose a new approach where the equations are interpreted as the limit of infinitesimal bin width of the respective weighted histogram analysis method (WHAM). In the proposed binned-to-unbinned MBAR (BU-MBAR) method, the bin width is adapted dynamically to ensure a stable and efficient convergence to the asymptotic solution.

Jammed suspensions of soft microgel particles may exhibit slippage along smooth boundaries. Owing to their expected sub-micrometric dimensions, direct observations of dynamics within the near-surface layers supposed to be responsible for this slippage have been difficult to achieve. Here, we use total internal reflection fluorescence microscopy (TIRFM) to observe nanoparticle dynamics near glass/microgel-suspension interfaces. Indicating near-wall dynamic heterogeneity, velocity profiles for suspensions are nonlinear. These profiles tend to a constant slippage velocity at submicrometric distances from the wall, consistent with macroscopic wall slip measurements. Furthermore, nanoscale particle altitude distributions are strongly dependent on the slip velocity, revealing a dynamically-mediated and nanoscale particle-organisation effect. The collected observations give support for the existence of near-wall heterogeneity as a dominant mechanism contributing to microgel wall slip. Our work also opens new perspectives for the study of particle dynamics and organisation in complex interfacial environments.

The macroscopic collective motion of active continuum on curved manifolds is conventionally addressed through perturbative dynamic renormalization or finite-element simulations, often obscuring the underlying geometric mechanisms. Here, an exact algebraic framework is established to reformulate the active phase transition on hyperbolic spaces $\mathbb{H}^2$. By rigorously expanding the covariant Navier-Stokes-like equations and applying the Weitzenböck identity, we derive the exact critical threshold $\alpha_c = \frac{5}{4}D\kappa^2$ for macroscopic polarization, which is dictated by the geometric mass gap of the Hodge-de Rham Laplacian. We strictly define the parameter subspace $\alpha = 2D\kappa^2$ where the topological free energy reaches the Bogomolny-Prasad-Sommerfield (BPS) limit. This enables the reduction of the complex velocity field to Blaschke products via Möbius gauge symmetry. The flat-space limit ($\kappa \to 0$) exactly degenerates to the topological phase of the classical O(2) model, demonstrating that the constant negative curvature acts as an un-perturbative infrared regularization for non-linear amplitude saturation. Furthermore, mapping the non-variational active convective modes onto the defect translational zero-modes yields an intrinsically non-reciprocal interaction matrix. By analytically extending the singular integral operator of the dynamically condensed defect ring, we identify a macroscopic second-order exceptional point (EP2) characterized by a strictly algebraic dynamic scaling law $\tau \sim |\Delta \nu|^{-1/2}$. This closed-form theoretical paradigm provides exact solutions for geometric frustration and non-Hermitian topology in soft mechanics.

How does particle shape affect driven transport through obstacle fields? We simulate a dissipative rod falling under gravity through randomly placed fixed obstacles. For sufficiently slender rods, the mean descent speed before trapping decreases, increases, and then decreases again as obstacle density rises, opening a window of densities in which rods fall faster in denser fields. Scaling arguments based on collision rates and rod geometry explain the three regimes and their crossovers. Particle shape can thus reverse the expectation that crowding slows driven transport.

Smectic liquid crystals are layered liquid-crystalline phases characterized by orientational order and periodic density modulation. Although their structures can be modeled using continuum theories, computing stable configurations remains challenging in complex geometries, particularly when the high-frequency density modulations associated with smectic layering should be resolved. We propose a deep variational framework (DVF) for computing these configurations within the modified Landau–de Gennes model, in which the coupled orientational and positional order parameters are represented on a regular reference domain while physical confinement is incorporated through coordinate mappings. A warmup penalty mitigates the spectral bias of neural networks toward smooth, nonlayered fields, enabling robust recovery of oscillatory smectic states. Comparisons with a neural-network baseline and finite-difference relaxation demonstrate the essential role of this penalty and the numerical stability of the resulting layered states. The DVF reproduces experimentally established smectic-A defect structures and layer morphologies across diverse confinement geometries and further predicts a chevron-like smectic-C state in a tangent-anchored sphere. Together, these results demonstrate the applicability of the DVF to computing stable smectic configurations across experimentally relevant confinement geometries and anchoring conditions.

Nematic order of confined cell populations plays an important role in determining cell alignment and stable configurations of topological defects, which are related to various biomechanical phenomena. Topological charges (or winding numbers) of topological defects strictly depend on the Euler characteristic of the confining domain, which has typically been non-negative in studies focused on domains without internal obstacles. However, biological tissues often surround two or more internal obstacles or holes, which inherently generate defects with negative charges. To understand the mechanical interaction between cellular tissue and obstacles, it is necessary to elucidate the geometrical effects of obstacles on cell alignment and defects with negative charges. Here, we investigate how cell populations achieve stable defect configurations of two -1/2 defects in a triply connected domain. First, we present experimental observations of C2C12 myoblasts confined by two circular obstacles of varying diameter, demonstrating that two $-1/2$ defects are the most frequent configuration when the obstacles are sufficiently large. Second, to theoretically validate these experimental observations, we perform systematic stability analyses of defect configurations using an explicit expression of cell alignment and numerical minimization of the Frank elastic energy. Our numerical calculations reveal that the most stable configuration shifts continuously from a horizontal, through off-axis, to a vertical configuration as the obstacle size increases. In addition, the experimentally observed defect positions agreed with these theoretical predictions to within 60 $\mu$m. These findings suggest that obstacle sizes control the symmetry of cell alignment, providing insights into how geometric and topological constraints can generate complex force patterns during morphogenesis or organ movements.

Physics-informed neural networks (PINNs) provide a mesh-free framework for solving governing equations, but their application to granular avalanche dynamics over curved terrain remains largely unexplored. This study extends a depth-averaged PINN formulation based on the Savage-Hutter equations to an exponentially curved chute with spatially varying inclination and a strain-rate-dependent Mohr-Coulomb earth-pressure closure. The model is validated against measured front- and rear-edge trajectories from a laboratory granular-avalanche experiment, with selected observations withheld from training. A staged temporal curriculum proved essential for accurate prediction, reducing the held-out trajectory error by approximately two orders of magnitude compared with training over the full time domain from the outset. Sparse-data experiments further showed that observation placement was more influential than observation number within the configurations tested. Four observations bracketing the transition from acceleration to deceleration achieved nearly the same accuracy as the eight-observation reference configuration, whereas observations clustered at early or late times performed poorly. The results demonstrate the importance of both training strategy and informative data placement when applying PINNs to granular flows over curved topography.

We carry out 3D computer simulations to study suspension rheology across the jamming transition for deformable non-Brownian frictionless particles. The simulations are carried out at constant shear rates and suspension densities ($\dot{\gamma} , \phi$), for polydisperse spherical particles. The suspension is probed at densities in the neighbourhood of the jamming point $\phi_c$ , as well as across much wider density ranges. We carry out critical scaling analysis of our close-to-jamming data and confirm that shear-driven jamming is a second-order critical phenomenon requiring strong corrections on shear. We build maps of the local strain rate exponent to classify flow regimes and examine the effect of the corrections on the suspension rheology. Further, using the critical scaling jamming density, we carry out soft granular rheological scaling over the wider density range, demonstrating an excellent data collapse. The soft granular rheology relations are able to predict our data over almost the entire dynamical and density range.

Liquid interfaces are both ubiquitous and also critically important for many commercial products, modern technologies, biological processes, and environmental phenomena. The properties of these interfaces can depend quite sensitively upon the composition of the bulk liquid phase. In this work, we develop a dilute solution theory (DST) for the influence of dilute cosolutes upon the interface between coexisting liquid and vapor phases. We employ a grand canonical perturbation theory to rigorously relate the interfacial free energy to the concentration of the liquid solution. We leverage a corresponding Gibbs ensemble to treat liquid-vapor coexistence and to eliminate the contribution of the bulk phases from this free energy. We express the coefficients of the resulting expansion in terms of microscopic partition functions. By treating solute-solute interactions to lowest order, we distinguish between the intrinsic and effective interfacial preferences of solutes. While the former reflects only solute-solvent interactions, the latter depends upon the solution composition and reflects the influence of solute-solute interactions. We assess this DST with molecular dynamics simulations of binary and ternary mixtures of Lennard-Jones spheres. These simulations demonstrate that DST accurately models the interfacial properties of these systems up to relatively high concentrations. Moreover, the simulations illustrate the impact of attractive solute-solute interactions in converting weak intrinsic surfactants into weak effective depletants.

Ultrasound is increasingly used to control biological cells, but its multiple physical effects, including radiation force, streaming, heating, and cavitation, require theoretical frameworks that can quantify their relative strengths. Here we develop a semi-analytical model to predict flow patterns and energy distributions of ultrasound-induced intracellular streaming. Cells are modeled as viscoelastic droplets immersed in a fluid with different viscoelastic properties. For this geometry, the momentum equation with an Oldroyd-B constitutive law is solved using a perturbation expansion commonly applied in acoustofluidics. The resulting equations for the different orders of the expansion are solved using partial wave expansion and explicit integration. We investigate our model for a range of parameters which is relevant both for synthetic polymer and for protein solutions. We find a series of flow reversals that we can connect to the distribution of energy into the different modes. We also consider density and compressibility contrasts, which shift these transitions and introduce new ones. Finally, we discuss the potential biological relevance of our mathematical results for cellular sensing and signaling.

We numerically investigate the mechanical response of amorphous solids to localized force dipoles, referred to as pinching, using athermal quasistatic simulations of model glasses with varying degrees of stability. We employ a control parameter, the imposed extension, corresponding to the increase in rest length between the two pinched particles relative to their initial separation. Increasing this extension allows us to continuously tune the system from the elastic to the plastic regime. For small extensions, the response remains elastic. In this regime, the displacement field induced by pinching is well described by linear elasticity and exhibits a long-range power-law decay consistent with dipolar forcing. Averaged responses display anisotropic, quadrupolar-like patterns, with quantitative agreement between simulations and analytical predictions. This continuum description remains valid down to particle-scale distances. As the imposed extension increases, the response becomes plastic. Pinching can then trigger either localized or system-spanning rearrangements, depending on glass stability. Well-annealed glasses exhibit localized plastic events, whereas poorly annealed systems display delocalized cascades. We introduce a method to extract the principal axis of plastic deformation and analyze the associated displacement fields and plastic activity. Overall, our results demonstrate that pinching provides a minimal local probe of amorphous solids. The resulting response, governed by both glass stability and imposed extension, offers insight into the interplay between elasticity, elementary rearrangements, and the emergence of collective plasticity.

Dipolar liquids combine two coupled degrees of freedom, translational and dipolar. Intriguingly, these two sectors can reside in distinct thermodynamic states, with the state of one sector possibly favoring a specific state of the other. The onset of ferroelectric order in dipolar liquids is an example of this mutual stabilization. Since this coupling is often encoded in interaction potentials, projecting out one sector to derive an effective interaction for the other can uncover the microscopic mechanism underlying this phenomenon. A coarse-grained theory in which the distance between nearest-neighbor particles depends on their dipolar interaction through a density-dependent mechanical compliance shows that a ferroelectric transition can trigger a liquid-liquid transition. It also predicts a first-order liquid-liquid transition in the paraelectric phase that can in turn induce ferroelectric ordering. The anisotropic dipolar-dependent soft-core interaction is shown to lead to an effective Jagla-like isotropic two-length-scale interaction, known to support liquid-liquid phase transitions. Here, however, the two-length-scale pattern depends on density and dipolar configuration, becoming sensitive to dipolar order. Analysis of supercooled TIP4P/Ice water numerical simulations shows that first-coordination-shell features of pair correlation functions are sensitive to dipolar interaction, supporting the coarse-grained theory foundations. Comparison of low- and high-density liquid water reveals enhanced spatial anisotropy of the first coordination shell in the low-density liquid, with increased effective excluded volume, showing that the liquid-liquid transition involves structural changes within the first shell, beyond reorganization of the interstitial region and tetrahedral order. These findings point to a role for dipolar interactions in supercooled water liquid-liquid phase transition.

Echoes observed in the intensity-intensity autocorrelation functions [$g_2(q, \tau)$] is a powerful method for probing nonaffine deformations and yielding behavior of soft viscoelastic materials subjected to an oscillatory shear. Multispeckle X-ray photon correlation spectroscopy (XPCS) measurements of large number of echoes with high time resolution impose severe constraints in terms of the computational hardware and potential degradation of the sample. These issues are alleviated by implementing a bunch-mode data acquisition scheme in which the highest resolution frames or bunches centered at the echo peaks. In addition, by placing the bunches in an aperiodic Fibonacci sequence, $g_2(q, \tau)$ over a long time span can be measured with orders of magnitude lesser number of frames. The performance of these XPCS acquisition schemes is demonstrated using slowly relaxing model colloidal suspensions. Furthermore, an analytical expression is provided for the quantitative description of full $g_2(q, \tau)$.

The finitely extensible nonlinear elastic (FENE) potential is widely used as a bonded interaction in coarse-grained simulations of polymers, soft matter, colloids, and biological systems. In the classical Kremer–Grest framework, FENE bonding is combined with a short-range Weeks–Chandler–Andersen (WCA) interaction to provide finite bond extensibility together with excluded-volume repulsion. Although this combination is highly successful, it intrinsically couples bonded elasticity to the nonbonded interaction, limiting the ability to independently control these two contributions. Here, we introduce a standalone FENE bond potential in HOOMD-blue in which finite bond extensibility is implemented independently of the choice of nonbonded interaction. This formulation allows the same FENE bond potential to be combined with WCA, Lennard–Jones, or other pair interactions without modifying the bonded interaction itself. We demonstrate the utility of the standalone formulation in coarse-grained polymer chains, colloidal networks, and mesh-based biological membrane models. Across these systems, Pure FENE bonding without short-range excluded-volume stabilization produces pronounced structural contraction, whereas the addition of WCA repulsion suppresses this collapse and preserves finite, spatially extended structures. These results demonstrate that separating finite bond extensibility from steric interactions provides independent control over local bond mechanics and collective structural organization. The standalone FENE formulation therefore provides a modular framework for coarse-grained simulations in which molecular connectivity and nonbonded interactions represent distinct physical mechanisms.

The yielding transition marks the onset of irreversible plastic deformation in amorphous solids and plays a central role in determining the mechanical stability and failure of metallic glasses, colloidal suspensions, and biological assemblies. Despite extensive research, the microscopic factors governing the nature of yielding, particularly the transition between brittle and ductile mechanical responses, remain poorly understood. Recent studies have identified kinetic fragility as a key parameter governing yielding in passive glasses; whether this connection persists in active glasses remains open. Here, using molecular dynamics simulations of a Kob-Andersen glass former doped with Run-and-Tumble (RTP) active particles under oscillatory shear, we show that activity systematically reduces kinetic fragility and consequently alters the mechanical response. The common yield point $\gamma_c$ decreases monotonically with activity and exhibits a power-law dependence on the Arrhenius activation barrier. Increasing activity suppresses the dependence of the yield strain on thermal history and transforms the response from brittle-like to increasingly ductile, with smoother stress relaxation and reduced stress discontinuities. The timescale to reach steady state near yielding retains a critical power-law divergence, indicating that activity does not alter the underlying critical character of the transition. Active glasses also develop broader, more diffuse shear bands. Our results establish kinetic fragility as a unifying parameter governing yielding in both passive and active glasses and demonstrate that activity offers a powerful route to tune the mechanical response of amorphous materials.

Hyperdensity functional theory facilitates the investigation of the equilibrium behavior of a general order parameter or statistical mechanical observable in spatially inhomogeneous classical many-body systems. The approach is based on applying the exact Mermin-Evans classical density functional mapping to an extended ensemble. Here we present the multivariate generalization for investigating simultaneously the properties and interrelations of several different hyperobservables of choice. The resulting framework gives rise to a systematic characterization and prediction scheme for general many-body phenomena. All pertinent equilibrium averages, variances, and covariances constitute universal density functionals, as we demonstrate explicitly. Associated one-body hyperfluctuation profiles quantify the degree of correlation of the local density with first- and second-order combinations of hyperobservables. These multivariate hyperfluctuation profiles are accessible in many-body simulations and they satisfy exact hyper-Ornstein-Zernike equations, which we derive from the minimization principle in the extended multivariate ensemble. The formal structure of the theory integrates naturally with supervised machine learning, which renders all hyperdensity functionals accessible in practice via training of neural networks on simulation data. We demonstrate all salient techniques using the illustrative case of clustering in confined hard rod fluids, thereby choosing the total number of particles and the largest cluster size as the representative hyperobservables of interest. Our numerical methodology enables the efficient and successful prediction of all statistical quantities induced by the chosen hyperobservables, which we verify via comparison to test data and which we attribute to the tight interplay of first-principles and machine-learning concepts that our general approach combines.