My research interest is aimed towards the understanding of the 'dynamics' of soft matter systems, within the "classical statistical mechanics" framework. Particular focus is on the non-equilibrium dynamics of fluids, close to a second-order phase transition. Few topics that I work on: (a) optically heated active colloids, (b) confined near-critical fluids and surface effects, (c) Casimir forces, (d) collective dynamics in fluids, (e) droplet coalescence, (f) phase separation, and aging, etc. For this purpose, I exploit a variety of computational techniques such as GPU-based molecular dynamics simulations, Monte-Carlo, numerical solution of Ginzburg-Landau like continuum dynamical models and Stokes equations, etc. Simulation results are analyzed by using finite-size scaling concepts and simple theoretical arguments.
A brief description is presented below.
1. Optically heated active colloids:
In the last decade, inspired by biological molecular motors, scientists tried to construct artificial devices that can deliver mechanical work or propel themselves in a liquid environment. One approach is to use phoretic transport mechanisms. An interesting candidate for self-propellers is
light-activated colloidal particles that are being used extensively of late. Micron-sized Janus particles, chemically functionalized appropriately in order to give rise to different surface adsorption properties and suspended in a near-critical solvent, undergo active motion when illuminated with a laser of sufficient intensity. Motility of these active particles depends on the non-equilibrium dynamics surrounding the particle which leads to coupled inhomogeneous temperature and concentration fields. This rich mechanism is sensitive to a variety of system parameters, viz., wetting properties of the colloids, illumination intensity, the concentration of the background solvent, hydrodynamic effects, confinement, etc.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems