arXiv subjects

Mayurakshi Deb

Publications and source records attributed to Mayurakshi Deb.

3 recordsLinked to original sources

arXiv2026

Hydrodynamic Phase Separation and Morphological Evolution in Chiral Active-Passive Mixtures

The collective behavior of passive particles within chiral active matter has emerged as a significant area of soft matter research. However, most existing studies focus on systems where chirality is imposed by external torques rather than intrinsic activity. In this work, we study emergent dynamics in a suspension of active spinners and passive colloids by computing many-body hydrodynamic interactions via Ewald summation. By systematically exploring a broad range of area fractions and rotational velocities, we identify distinct phase-separation regimes sensitive to the system's kinematic parameters. Specifically, we report the emergence of unique structural morphologies, including the formation of passive particle vortices surrounding phase-separated active spinners and the development of large-scale active-passive bands. We characterize the underlying dynamics by analyzing the temporal evolution of characteristic length scales and the non-equilibrium velocity distributions of the passive particles. Our findings provide new insights into the role of long-range hydrodynamic couplings in governing the self-organization of non-equilibrium condensed matter.

cond-mat.soft↗

arXiv2026

Ewald summing irreducible components of flow around active particles

We present a method to compute Ewald summation for the irreducible components of flow around active particles to study hydrodynamic interactions in active colloidal suspensions. An active particle is modeled as a colloidal sphere with a surface slip velocity. Using this model, we obtain an irreducible representation of the fluid flow produced by an active particle in periodic geometry of Stokes flow for an arbitrary surface slip. The solution of the active flow is obtained in terms of lattice sum of the Oseen tensor and their derivatives. The lattice sum is accelerated using the Ewald summation technique. We apply the method to compute explicit expression for rigid body motion of hydrodynamically interacting active particles. Our method presents a way for dynamic simulation of active particles due to arbitrary mode of active slip in periodic geometry of Stokes flow.

cond-mat.soft↗

arXiv2025

Inferring activity from the flow field around active colloidal particles using deep learning

Active colloidal particles create flow around them due to non-equilibrium process on their surfaces. In this paper, we infer the activity of such colloidal particles from the flow field created by them via deep learning. We first explain our method for one active particle, inferring the $2s$ mode (or the stresslet) and the $3t$ mode (or the source dipole) from the flow field data, along with the position and orientation of the particle. We then apply the method to a system of many active particles. We find excellent agreements between the predictions and the true values of activity. Our method presents a principled way to predict arbitrary activity from the flow field created by active particles.

cond-mat.soft↗