This list includes a brief about my academic and co-curricular projects:

Aqueous Dispersions of Lipid Nanoparticles wet hydrophobic surfaces (Substantiation of empirical adsorption time scales using simulation)

    Efficient delivery of aqueous sprays to hydrophobic surfaces is the key technological challenge in a wide variety of applications, including pesticide delivery to plants. To account for losses due to bouncing of pesticide sprays off hydrophobic leaf surfaces, large excess of pesticide is typically employed, resulting in environmentally hazardous run-offs that contaminate soil and ground water.

    We demonstrate that aqueous dispersions of glycerol monooleate nanoparticles, wet hydrophobic and superhydrophobic surfaces and adhere to them. These nano particles comprise glycerol monooleate lipid molecules self-assembled into a double diamond cubic phase, sterically stabilized using amphiphilic block copolymer. We use high speed imaging to monitor the spreading and retraction of aqueous drops impinged on model hydrophobic substrates and on superhydrophobic lotus leaves.

    We show that they diffuse to hydrophobic substrates and reorganize to form a thin, ≈ 2 nm adsorbed lipid layer during the millisecond time scales that characterize drop impact. This adsorbed film drastically reduces the water contact angle, transforming the hydrophobic surface to hydrophilic, thus facilitating retention of the aqueous drop on the surface. Our results have important implications for efficient, environment friendly delivery of pesticide sprays.

Simulation: Explains the particle diffusion and adsorption on the surface. But due to its simplifying assumptions (hydrodynamic effects are neglected accounting to low concentrations; particles are modelled as spheres and hard sphere potential is used) and shortcomings, further effects (effects of charge) have to be included into the code to build a robust model. The time scale for film formation scales as phi^(-0.65) whereas the time scale from simulation scales as phi^(-1) as expected. This suggests that surface modification is governed by phenomena other than diffusion. 

Water droplet retracting and bouncing on a hydrphobic surface after impact

Lipid nanoparticle solution droplet sticking and spreading on a hydrphobic surface after impact

Image on the right:

Simulation box with adsorption wall at the bottom (z=0). The top (z= lz) is open to the bulk of the solution. Periodic boundary conditions are assumed on walls along x and y.  

Tracking a single particle: Brownian Dynamics motion of a cubosome 

Simulation of meso-particles in microchannels: Dissipative Particle Dynamics

    Human Blood is composed of blood cells (also called corpuscles) suspended in blood plasma. Study of human blood and its flow in blood vessels is crucial for diagnosis, pathology and design of biomedical devices. Study of deformative properties and flow of RBCs and cancer cells in minute vessels have peaked the interest of biologists and pathologists, since affected cells differ in their behavior compared to the healthy cells. In this thesis, we use FORTRAN codes to model the RBCs in micro flow domain. We use Velocity Verlet algorithm for time integration.

Boundary Conditions: Generally multiple layers of frozen particles at same particle density as that of the fluid are used to simulate the stationary wall. But since this is computationally expensive, we use modified Instantaneous Frozen Particle (IFP) model as the boundary condition to model the walls. Drawback: It sets of pressure waves which cause density fluctuations in the domain of a larger magnitude than the other methods.

    To circumvent this problem, we use a modified version of IFP. We call this Random Position Instantaneous Frozen Particle (RPIFP) Method. In IFP, as the force between each particle and the boundary is directed towards the center, the density fluctuations appear much more significant. RFIFP attempts to avoid this by introducing a small random component to the position of the boundary particle. In addition to this, adaptive boundary conditions control density fluctuation near the boundary.

Low Torus Ring Structure : RBC in this simulation is modelled as a ring of 10 colloidal particles connected by worm-like chains. This model is adopted since simulating the original spectrin network (cytoskeleton) of an RBC is computationally quite expensive. This model is also useful for simulating a large number of RBCs in the domain.
Healthy RBCs squeezing through the microcapillaries

Mesoparticle squeezing through the contricted neck

500 equidistant points are chosen inside a sphere to model a cancer cell

Low torus ring structure of RBCs travelling through a cylindrical constriction toppling and turning as they travel in the domain