arXiv subjects

Reza Shaebani

Publications and source records attributed to Reza Shaebani.

9 recordsLinked to original sources

arXiv2026

Confinement-Induced Optimization of Fluctuation-Induced Forces in Active Fluids

Active matter generates nonequilibrium fluctuations that mediate effective interactions between immersed objects. While fluctuation-induced (FI) forces in active fluids depend on activity, density, and geometry, their dependence on confinement remains poorly understood. We study FI forces between fixed intruders in two-dimensional active fluids composed of self-propelled circular or rodlike particles using Langevin dynamics simulations. We find that the FI force exhibits a pronounced nonmonotonic dependence on intruder separation, reaching a maximum at an optimal gap size well beyond the depletion regime, in contrast to the commonly assumed monotonic decay. This optimal confinement is robust across parameters and is more pronounced for elongated particles. The effect arises from a confinement-controlled balance between particle transport and crowding: narrow gaps hinder exchange between inner and outer regions, whereas large separations effectively decouple the intruders. At intermediate distances, enhanced crowding around the intruders generates maximal collision-rate asymmetries, leading to the strongest effective interactions. These results identify confinement geometry as a key control parameter for FI forces in active matter.

cond-mat.soft↗

arXiv2025

Inferring Tree Structure with Hidden Traps from First Passage Times

Tracking the movement of tracer particles has long been a strategy for uncovering complex structures. Here, we study discrete-time random walks on finite Cayley trees to infer key parameters such as tree depth and geometric bias toward the root or leaves. By analyzing first passage properties, we show that the first two first-passage-time factorial moments (FPTFMs) uniquely determine the tree structure. However, if the random walker experiences waiting phases -- due to sticky branch walls or presence of traps -- this identification becomes nontrivial. We demonstrate that the generating function of the first passage time (FPT) distribution decomposes into contributions from the waiting time distribution and the random walk without waiting, leading to a nonlinear system of equations relating the factorial moments of the waiting time distribution and the FPTFMs of random walks with and without waiting. For geometrically distributed waiting times, additional moment measurements do not suffice, but unique determination of the structure is achieved by varying initial conditions or fitting the Fourier transform of the FPT distribution to measured data. The latter method remains effective also for power-law waiting time distributions, where higher-order FPTFMs are undefined. These results provide a framework for reconstructing tree-like networks from FPT data, with applications in biological transport and spatial networks.

cond-mat.stat-mech↗

arXiv2025

Convective Flows in Sheared Packings of Spherical Particles

Understanding how granular materials respond to shear stress remains a central challenge in soft matter physics. We report direct observations of persistent granular convection in the bulk shear zones of spherical particle packings -- a phenomenon previously associated primarily with particle shape anisotropy or boundary effects. By employing various bead-coloring techniques in a split-bottom geometry, we reveal internal flow fields within sheared granular packings. We find robust convection rolls, strikingly governed by system geometry: at low filling heights, two counter-rotating convection rolls emerge, while at higher filling heights, a single dominant convection roll forms, featuring radially outward flow at the surface. This transition is driven by the height-dependent broadening of the shear zone, which introduces shear rate asymmetry across its flanks. Notably, the transition occurs entirely within the open shear band regime. These findings underscore the pivotal role of system geometry in shaping secondary flow formation in dense packings of frictional particles, suggesting possible broader relevance to geophysical flow dynamics and industrial applications.

cond-mat.soft↗

arXiv2025

Transport-Generated Signals Uncover Geometric Features of Evolving Branched Structures

Branched structures that evolve over time critically determine the function of various natural and engineered systems, including growing vasculature, neural arborization, pulmonary networks such as lungs, river basins, power distribution networks, and synthetic flow media. Inferring the underlying geometric properties of such systems and monitoring their structural and morphological evolution is therefore essential. However, this remains a major challenge due to limited access and the transient nature of the internal states. Here, we present a general framework for recovering the geometric features of evolving branched structures by analyzing the signals generated by tracer particles during transport. As tracers traverse the structure, they emit detectable pulses upon reaching a fixed observation point. We show that the statistical properties of this signal intensity -- which reflect underlying first-passage dynamics -- encode key structural features such as network extent, localized trapping frequency, and bias of motion (e.g., due to branch tapering). Crucially, this method enables inference from externally observable quantities, requiring no knowledge of individual particle trajectories or internal measurements. Our approach provides a scalable, non-invasive strategy for probing dynamic complex geometries across a wide range of systems.

cond-mat.stat-mech↗

arXiv2025

Microtubule polymerization generates microtentacles important in circulating tumor cell invasion

Circulating tumor cells (CTCs) have crucial roles in the spread of tumors during metastasis. A decisive step is the extravasation of CTCs from the blood stream or lymph system, which depends on the ability of cells to attach to vessel walls. Recent work suggests that such adhesion is facilitated by microtubule (MT)-based membrane protrusions called microtentacles (McTNs). However, how McTNs facilitate such adhesion and how MTs can generate protrusions in CTCs remain unclear. By combining fluorescence recovery after photobleaching (FRAP) experiments and simulations we show that polymerization of MTs provides the main driving force for McTN formation, whereas the contribution of MTs sliding with respect to each other is minimal. Further, the forces exerted on the McTN tip result in curvature, as the MTs are anchored at the other end in the MT organizing center. When approaching vessel walls, McTN curvature is additionally influenced by the adhesion strength between the McTN and wall. Moreover, increasing McTN length, reducing its bending rigidity, or strengthening adhesion enhances the cell-wall contact area and, thus, promotes cell attachment to vessel walls. Our results demonstrate a link between the formation and function of McTNs, which may provide new insight into metastatic cancer diagnosis and therapy.

physics.bio-ph↗

arXiv2025

Geometry-Driven Segregation in Periodically Textured Microfluidic Channels

We investigate the transport dynamics of elongated microparticles in microchannel flows. While smooth-walled channels preserve the dependence of particle trajectories on initial orientation and lateral position, we show that introducing periodically textured walls can trigger robust alignment of the particle along the channel centerline. This geometry-driven alignment arises from repeated reorientations generated by spatially modulated shear gradients near the textured walls. The alignment efficiency depends on particle elongation and the relative texture wavelength, with an optimal range for maximal effect. While the observed alignment behavior is not limited to low Reynolds numbers, the characteristic alignment length scale diverges as the Reynolds number increases toward the turbulent flow regime. These findings offer a predictive framework for designing microfluidic devices that passively sort or focus anisotropic particles, with implications for soft matter transport, biophysical flows, and microfluidic engineering.

physics.flu-dyn↗

arXiv2024

Tracking the Morphological Evolution of Neuronal Dendrites by First-Passage Analysis

A high degree of structural complexity arises in dynamic neuronal dendrites due to extensive branching patterns and diverse spine morphologies, which enable the nervous system to adjust function, construct complex input pathways and thereby enhance the computational power of the system. Owing to the determinant role of dendrite morphology in the functionality of the nervous system, recognition of pathological changes due to neurodegenerative disorders is of crucial importance. We show that the statistical analysis of a temporary signal generated by cargos that have diffusively passed through the complex dendritic structure yields vital information about dendrite morphology. As a feasible scenario, we propose engineering mRNA-carrying multilamellar liposomes to diffusively reach the soma and release mRNAs, which are translated into a specific protein upon encountering ribosomes. The concentration of this protein over a large population of neurons can be externally measured, as a detectable temporary signal. Using a stochastic coarse-grained approach for first-passage through dendrites, we connect the key morphological properties affected by neurodegenerative diseases -- including the density and size of spines, the extent of the tree, and the segmental increase of dendrite diameter towards soma -- to the characteristics of the evolving signal. Thus, we establish a direct link between the dendrite morphology and the statistical characteristics of the detectable signal. Our approach provides a fast noninvasive measurement technique to indirectly extract vital information about the morphological evolution of dendrites in the course of neurodegenerative disease progression.

cond-mat.soft↗

arXiv2024

Mechanical Interactions Govern Self-Organized Ordering in Bacterial Colonies on Surfaces

Bacterial colonies growing on surfaces are shaped by mechanical stresses transmitted through the community, governed by the balance between cell growth and steric and cell-substrate interactions. Using overdamped dynamics simulations of nonmotile, stress-responsive bacteria, we examine how purely mechanical interactions determine colony morphology and internal organization. Growth-induced extensile stresses compete with steric constraints, giving rise to the spontaneous formation of microdomains composed of highly aligned cells. We characterize this self-organization through the distribution of microdomain areas and a nematic order parameter that quantifies colony-wide alignment. Mechanosensitivity does not systematically alter domain structure, but increasing substrate friction reduces the mean domain size and broadens the diversity of orientations. Shifting the balance toward steric interactions, by lengthening the cell division size, slows the relaxation of colony shape toward isotropy and broadens the distribution of contact forces, producing a slower exponential decay. In dense colonies, strong forces are transmitted anisotropically through chains of aligned neighbors within microdomains. These findings demonstrate that colony-level morphology and stress organization can emerge from local mechanical interactions alone, even without requiring biochemical signaling. By linking microscopic force transmission to macroscopic growth dynamics, our study provides a physical framework for understanding how mechanical interactions shape the self-organization of bacterial communities under surface confinement.

cond-mat.soft↗

arXiv2024

Vulnerability of Transport through Evolving Spatial Networks

Insight into the blockage vulnerability of evolving spatial networks is important for understanding transport resilience, robustness, and failure of a broad class of real-world structures such as porous media and utility, urban traffic, and infrastructure networks. By exhaustive search for central transport hubs on porous lattice structures, we recursively determine and block the emerging main hub until the evolving network reaches the impenetrability limit. We find that the blockage backbone is a self-similar path with a fractal dimension which is distinctly smaller than that of the universality class of optimal path crack models. The number of blocking steps versus the rescaled initial occupation fraction collapses onto a master curve for different network sizes, allowing for the prediction of the onset of impenetrability. The shortest-path length distribution broadens during the blocking process reflecting an increase of spatial correlations. We address the reliability of our predictions upon increasing the disorder or decreasing the fraction of processed structural information.

cond-mat.soft↗