CyberSec Research

Motivated by recent experiments of motile bacteria crossing liquid-liquid interfaces of isotropic- nematic coexistence (Cheon et al., Soft Matter 20: 7313-7320, 2024), we study the dynamics of prolate microswimmers traversing clean liquid-liquid interfaces. Using large-scale lattice Boltzmann simulations, we observe that neutrally wetting swimmers can be either trapped or cross the in- terface, depending on their initial angle, swimming speed and the interfacial tension between the two fluids. The simulation results are rationalized by considering a competition between interfacial (thermodynamic) and active (hydrodynamic) forces. The swimmers get trapped at the interface due to a thermodynamic trapping force, akin to Pickering effect, when the forces from interfacial tension dominate over the swimming forces. The trapping behavior can be captured by calculating a critical capillary number by balancing the interfacial and active energies. This prediction agrees remarkably well with the numerical simulations as well as the bacterial experiments of Cheon et al., (Soft Matter 20: 7313-7320, 2024). Finally, our results demonstrate that the torque resulting in a reorientation of the swimmers parallel to the interface have both hydro and thermodynamic components.

Curvature plays a central organizational role in active polymer dynamics. Using large-scale Langevin-dynamics simulations, we study active semiflexible filaments confined to smooth curved surfaces and map how curvature, bending rigidity, and activity interact. We find geodesic alignment, curvature lensing, and curvature-induced trapping. In particular, regions of negative Gaussian curvature localize filaments and hinder global surface exploration. These results show how surface geometry can be used to control the organization and transport of active matter on curved substrates

We consider pattern formation in a sheared dense mixture of cohesive and non-cohesive grains. Our findings show that cohesive grains, which would typically form distributed agglomerates, instead segregate into percolating stripes or layers when the cohesive grain concentration ($c_o$) and cohesion strength ($C$) increase -- in a way that the average agglomerate size and the average normal stress collapse onto a single curve when plotted against $c_oC$. Our central proposal is that the development of interfaces between cohesive and non-cohesive grains is akin to phase separation in binary molecular mixtures driven by an effective free energy, although we are dealing with a non-equilibrium system; we setup the segregation flux such that the effect of this free energy is activated only upon application of the external driving. By constructing the segregation flux proportional to the gradient of the variational derivative of the free energy, we closely reproduce the layering in the steady-state limit. We find a robust correspondence between the parameter $c_o C$ in the discrete simulations and the parameters in the free energy.

A wide range of natural and engineered fluid flows exhibit spatial or temporal viscosity variations, spanning scales from microbial locomotion to planetary mantle convection. These variations introduce qualitatively new physical mechanisms absent in constant-viscosity flows. This review surveys such phenomena across scales. In low Reynolds number (Stokes) flows, viscosity gradients couple translation and rotation, enabling novel particle responses to uniform forcing-- mechanisms that microorganisms may exploit. In shear flows, viscosity variation alters base flow profiles and breaks symmetries, modifying stability and transition dynamics. At high Reynolds numbers, stratification fundamentally changes the singular perturbation structure governing energy production, enhancing or suppressing canonical instabilities and introducing new ones. Viscosity variation also affects nonnormal growth and nonlinear interactions that drive transition to turbulence. While laminar and fully developed turbulence have been extensively studied, transitional processes remain poorly understood in variable-viscosity flows. In turbulent regimes, viscosity variation impacts jets, wall-bounded flows, and mixing layers. At geophysical scales, incorporating eddy viscosity stratification in climate models may improve predictions, while in Earth's mantle, viscosity contrasts drive large-scale convection and geological evolution. Particle-laden flows, common across contexts, can generate effective viscosity stratification through inhomogeneous loading. Throughout, we highlight cases where viscosity variation alters flow behavior qualitatively, and point to open questions. This review aims to guide graduate students and researchers toward tractable, cross-disciplinary problems.

Through evolution, bacteria have developed the ability to perform chemotactic motion in order to find nourishment. By adopting a machine learning approach, we aim to understand how this behavior arises. We consider run-and-tumble agents able to tune the instantaneous probability of switching between the run and the tumble phase. When such agents are navigating in an environment characterized by a concentration field pointing towards a circular target, we investigate how a chemotactic strategy may be learned starting from unbiased run-and-tumble dynamics. We compare the learning performances of agents that sense only the instantaneous concentration with those of agents having a short-term memory that allows them to perform temporal comparisons. While both types of learning agents develop successful target-search policies, we demonstrate that those achieved by agents endowed with temporal comparison abilities are significantly more efficient, particularly when the initial distance from the target is large. Finally, we also show that when an additional length scale is imposed, for example by fixing the initial distance to the target, the learning agents can leverage this information to further improve their efficiency in locating the target.

To maintain homeostasis, living cells process information with networks of interacting molecules. Traditional models for cellular information processing have focused on networks of chemical reactions between molecules. Here, we describe how networks of physical interactions could contribute to the processing of information inside cells. In particular, we focus on the impact of biomolecular condensation, a structural phase transition found in cells. Biomolecular condensation has recently been implicated in diverse cellular processes. Some of these are essentially computational, including classification and control tasks. We place these findings in the broader context of physical computing, an emerging framework for describing how the native dynamics of nonlinear physical systems can be leveraged to perform complex computations. The synthesis of these ideas raises questions about expressivity (the range of problems that cellular phase transitions might be able to solve) and learning (how these systems could adapt and evolve to solve different problems). This emerging area of research presents diverse opportunities across molecular biophysics, soft matter, and physical computing.

The traditional Mpemba effect refers to an anomalous cooling phenomenon when an initial hotter system cools down faster than an initial warm system. Such counterintuitive behavior has been confirmed and explored across phase transitions in condensed matter systems and also for colloidal particles exposed to a double-well potential. Here we predict a frictional Mpemba effect for a macroscopic body moving actively on a surface governed by Coulomb (dry) friction. For an initial high temperature, relaxation towards a cold state occurs much faster than that for an intermediate initial temperature, due to a large temperature overshooting in the latter case. This frictional Mpemba effect can be exploited to steer the motion of robots and granules.

The extensional rheology of dilute suspensions of spheres in viscoelastic or polymeric liquids is studied computationally. At low polymer concentration (c) and Deborah number (De), a wake of highly stretched polymers forms downstream of the particles due to larger local velocity gradients than the imposed flow, indicated by a positive deviation in local De. This increases the suspension's extensional viscosity with time and De for De less than 0.5. When De exceeds 0.5 (the coil-stretch transition), the fully stretched polymers from the far field collapse in regions with lower local velocity gradients around the particle's stagnation points, reducing suspension viscosity relative to the polymer-only liquid. The interaction between local flow and polymers intensifies with increasing c. Highly stretched polymers impede local flow, reducing local De, while it increases in regions with collapsed polymers. Initially, increasing c aligns local De and polymer stretch with far-field values, diminishing particle-polymer interaction effects. However, beyond a certain c, a new mechanism emerges. At low c, fluid three particle radii upstream exhibits increased local De, stretching polymers beyond their undisturbed state. As c increases, this deviation becomes negative, collapsing polymers and resulting in increasingly negative stress from particle-polymer interactions at large De and time. At high c, this negative interaction stress scales as c squared, surpassing the linear increase in polymer stress, making dilute sphere suspensions more effective at reducing the viscosity of viscoelastic liquids at larger De and c.

Phase separation of two phase separating solutes in a common solvent can result in mesoscale (micron-sized) droplets with complex topologies of the domains of each solute within each droplet. Such topologies have been observed in-vitro in systems of chromatin oligomers, biomolecular condensates, and polymeric mixtures. In these systems the solutes phase separate from the solvent into droplets due to the relatively large free energy gain, which includes the energies and entropies of mixing with the solvent. Within each droplet, further phase separation can occur between the two solutes due to an additional free energy difference that promotes their demixing; in some systems, the extent of demixing can be, in some cases, be modulated by an additional component. The minimal free energy topologies are predicted as universal functions of the interfacial tension ratios and fractions of each solute within a droplet. We compare the predictions with several experimental systems to estimate the ranges of interfacial tensions. Experimental aspects that may depend on the kinetics or molecular weight variations in the system are also discussed.

We present a vectorial extension of the Hopfield associative memory model inspired by the theory of amorphous solids, where binary neural states are replaced by unit vectors $\mathbf{s}_i \in \mathbb{R}^3$ on the sphere $S^2$. The generalized Hebbian learning rule creates a block-structured weight matrix through outer products of stored pattern vectors, analogous to the Hessian matrix structure in amorphous solids. We demonstrate that this model exhibits quantifiable structural properties characteristic of disordered materials: energy landscapes with deep minima for stored patterns versus random configurations (energy gaps $\sim 7$ units), strongly anisotropic correlations encoded in the weight matrix (anisotropy ratios $\sim 10^2$), and order-disorder transitions controlled by the pattern density $\gamma = P/(N \cdot d)$. The enhanced memory capacity ($\gamma_c \approx 0.55$ for a fully-connected network) compared to binary networks ($\gamma_c \approx 0.138$) and the emergence of orientational correlations establish connections between associative memory mechanisms and amorphous solid physics, particularly in systems with continuous orientational degrees of freedom. We also unveil the scaling with the coordination number $Z$ of the memory capacity: $\gamma_c \sim (Z-6)$ from the isostatic point $Z_c =6$ of the 3D elastic network, which closely mirrors the scaling of the shear modulus $G \sim (Z-6)$ in 3D central-force spring networks.

We study the emerging self-organization in active ring suspensions, focusing on how the rings' orientational order and geometric entanglement vary with density and spatial confinement. To quantify entanglement, we introduce the wrapping number, a pairwise measure of ring interpenetration, while orientational order is characterized by the alignment of the normal vectors to the rings' osculating planes. Both wrapping number and alignment distinguish active from passive systems, and their combination aptly identifies the self-organized states that emerge with the onset of activity. Mutual-information analysis reveals a significant correlation between alignment and wrapping number across all considered active conditions. However, self-organization displays a non-monotonic dependence on the activity-induced entanglement. Specifically, moderate wrapping stabilizes contacts of neighboring aligned rings, while excessive entanglement disrupts alignment. We show that this competition arises because increasing entanglement interferes with the planar conformations required to form aligned stacks. Given the simplicity of this microscopic mechanism, analogous effects may occur more generally in polymer systems where the degree of entanglement is regulated by out-of-equilibrium effects.

One of the most important properties of soft functionalized magnetic composite materials in view of their technological potential is given by the magnetorheological effect. It describes the change in rheological properties such as the shear modulus by application of external magnetic fields. We demonstrate how computational material design can support in approximately doubling the magnitude of this important phenomenon for magnetic elastomers. Key is to work with two perpendicular magnetic field directions. We expect future practical relevance of our concept.

We investigate a two-dimensional system of active Brownian dumbbells using molecular dynamics simulations. In this model, each dumbbell is driven by an active force oriented perpendicular to the axis connecting its two constituent beads. We characterize the resulting phase behavior and find that, across all values of activity, the system undergoes phase separation between dilute and dense phases. The dense phase exhibits hexatic order, and for large enough activity, we observe a marked increase in local polarization, with dumbbells predominantly oriented towards the interior of the clusters. Compared to the case of axially self-propelled dumbbells, we find that the binodal region is enlarged towards lower densities at all activities. This shift arises because dumbbells with transverse propulsion can more easily form stable cluster cores, serving as nucleation seeds, and show a highly suppressed escaping rate from the cluster boundary. Finally, we observe that clusters exhibit spontaneous rotation, with the modulus of the angular velocity scaling as $\omega\sim r_g^{-2}$, where $r_g$ is the cluster's radius of gyration. This contrasts with axially propelled dumbbells, where the scaling follows $\omega\sim r_g^{-1}$. We develop a simplified analytical model to rationalize this scaling behavior.

The experimental use of micropatterned quasi-1D substrates has emerged as an useful experimental tool to study the nature of cell-cell interactions and gain insight on collective behaviour of cell colonies. Inspired by these experiments, we propose an active spin model to investigate the emergent properties of the cell assemblies. The lattice gas model incorporates the interplay of self-propulsion, polarity directional switching, intra-cellular attraction, and contact Inhibition Locomotion (CIL). In the absence of vacancies, which corresponds to a confluent cell packing on the substrate, the model reduces to an equilibrium spin model which can be solved exactly. In the presence of vacancies, the clustering is controlled by a dimensionless Peclet Number, Q - the ratio of magnitude of self-propulsion rate and directional switching rate of particles. In the absence of CIL interactions, we invoke a mapping to Katz-Lebowitz-Spohn(KLS) model to determine an exact analytical form of the cluster size distribution in the limit Q << 1. In the limit of Q >> 1, the cluster size distribution exhibits an universal scaling behaviour (in an approximate sense), such that the distribution function can be expressed as a scaled function of Q, particle density and CIL interaction strength. We characterize the phase behaviour of the system in terms of contour plots of average cluster size. The average cluster size exhibit a non-monotonic dependence on CIL interaction strength, attractive interaction strength, and self-propulsion.

Cell shape changes, essential for processes such as motility or division, are controlled by the actomyosin cortex that actively remodels biological membranes. Their mechanisms can be deciphered in___vitro using biomimetic reconstituted systems, such as giant unilamellar vesicles (GUVs) with controlled lipid composition coupled to reconstituted actin networks. These assays allow mimicking cell shape changes in controlled biochemical and biophysical environments. However, studying the dynamics of these shape changes on statistically significant populations of GUVs with the possibility to sequentially modify the protein composition of the assay is a major experimental challenge. To address these issues, a microfluidic approach is used to immobilize several dozens of isolated GUVs and monitor membrane and actin network evolution. The loading of the chamber with GUVs and actin is first characterized. Then, the actin-induced remodeling of populations of homogeneous and phase-separated GUVs is monitored and shows that actin networks prevent the coalescence of lipid microdomains and that, in return, the number of domains affects the actin network structure. This microfluidic-based experimental strategy, thus, allows for studying actin-induced membrane deformation in___vitro and can be adapted to other studies on membrane remodeling processes.

Current artificial intelligence systems show near-human-level capabilities when deployed in isolation. Systems of a few collaborating intelligent agents are being engineered to perform tasks collectively. This raises the question of whether robotic matter, where many learning and intelligent agents interact, shows emergence of collective behaviour. And if so, which kind of phenomena would such systems exhibit? Here, we study a paradigmatic model for robotic matter: a stochastic many-particle system in which each particle is endowed with a deep neural network that predicts its transitions based on the particles' environments. For a one-dimensional model, we show that robotic matter exhibits complex emergent phenomena, including transitions between long-lived learning regimes, the emergence of particle species, and frustration. We also find a density-dependent phase transition with signatures of criticality. Using active matter theory, we show that this phase transition is a consequence of self-organisation mediated by emergent inter-particle interactions. Our simple model captures key features of more complex forms of robotic systems.

In this paper we compare three frameworks for modeling flows of complex fluids: (i) local conservations of mass, momentum and energy, (ii) GENERIC, and (iii) Onsager principle. The first is based on the mass, momentum, and energy conservation implied by mechanics, the second on the observed approach of externally unforced fluids to equilibrium states at which their behavior is well described by equilibrium thermodynamics, and the third on the minimal resistance to external influences. The comparison is illustrated on isothermal and incompressible polymeric fluids.

Synthetic sequence-controlled polymers promise to transform polymer science by combining the chemical versatility of synthetic polymers with the precise sequence-mediated functionality of biological proteins. However, design of these materials has proven extraordinarily challenging, because they lack the massive datasets of closely related evolved molecules that accelerate design of proteins. Here we report on a new Artifical Intelligence strategy to dramatically reduce the amount of data necessary to accelerate these materials' design. We focus on data connecting the repeat-unit-sequence of a \emph{compatibilizer} molecule to its ability to reduce the interfacial tension between distinct polymer domains. The optimal sequence of these molecules, which are essential for applications such as mixed-waste polymer recycling, depends strongly on variables such as concentration and chemical details of the polymer. With current methods, this would demand an entirely distinct dataset to enable design at each condition. Here we show that a deep neural network trained on low-fidelity data for sequence/interfacial tension relations at one set of conditions can be rapidly tuned to make higher-fidelity predictions at a distinct set of conditions, requiring far less data that would ordinarily be needed. This priming-and-tuning approach should allow a single low-fidelity parent dataset to dramatically accelerate prediction and design in an entire constellation of related systems. In the long run, it may also provide an approach to bootstrapping quantitative atomistic design with AI insights from fast, coarse simulations.