Previously, I earned my PhD in Mechanical Engineering at the University of Nebraska-Lincoln (UNL) in Prof. Jae Sung Park’s laboratory, where I also worked as a postdoctoral researcher for a year. At UNL, my research centered on large-scale numerical modeling of multiscale fluid dynamics, electrokinetics, and granular/colloidal suspensions by developing in-house multiphysics computational fluid dynamics (CFD) models and particle-level simulations. One of my key contributions was proposing novel approaches to harness nonlinear phenomena specifically emerging under the applied external fields, such as induced-charge electrophoresis and dielectrophoresis, for controlling the microstructures and mechanical properties of particulate suspension systems. This work culminated in the development of a high-fidelity computational solver, built in Fortran, based on Stokesian dynamics. Additionally, I led interdisciplinary projects, including an investigation into atherosclerosis mechanisms using a CFD solver I developed, which integrated Direct Numerical Simulation (DNS) with the Immersed Boundary Method (IBM).
At CU Boulder, my current work includes developing an enhanced framework for Turing theory through an efficient, large-scale, discrete Eulerian-Lagrangian simulation model. This framework computationally recreates imperfect, multiscale structural features by incorporating the diffusiophoretic assembly of finite-size particles with a reaction–diffusion instability that acts as the chemical signal blueprint. Turing patterns are stationary, wave-like structures that emerge from the nonequilibrium interaction between reactive and diffusive components, with key requirements being short-range self-enhancement and long-range inhibition. Although central to biophysics, their traditional formulation relies on a single characteristic length scale balancing reaction and diffusion properties, which makes them too simplistic to capture the multiscale, grain-like, and imperfect nature of many biological patterns. In this work, we couple diffusiophoretically assisted assembly of cells with finite size distributions guided by a background chemical gradient within a Turing framework while accounting for intercellular interactions. This approach introduces critical control parameters such as the Péclet number, cell size distribution, and intercellular forces, which together allow us to recreate the complex structural features observed in natural systems, such as those found on the Ornate Boxfish. Our study highlights imperfections like variable pattern thickness, packing constraints, and inconsistencies in the pattern. With the capability of the current framework to produce complex structures while encapsulating fundamental colloidal physics, our model not only deepens our understanding but also initiates new investigations into imperfect Turing patterns that deviate from the classical formulation in significant ways.
I am also leading a study on the particle-free zone known as the exclusion zone driven by diffusiophoresis near ion-exchange membranes and the associated onset of flow instability. The exclusion zone (EZ) plays a critical role in applications such as water purification, mechanical sorting, and energy storage. In ionic solutions near ion-exchange membranes, diffusiophoresis is known to be the primary mechanism driving the colloidal particles away from the surface, forming an EZ that can extend hundreds of micrometers. However, the development of the EZ is accompanied by the emergence of a particle-rich layer, which can trigger a flow instability reminiscent of the Rayleigh-Taylor instability. Here, we are exploring the dynamics of EZ formation and identifying the mechanisms governing the onset and suppression of this instability by combining the custom-built experiments with in-house numerical modeling. In particular, we assess the impacts of chemo-physical properties of suspension as well as geometrical factors. Our preliminary results indicate that while EZ dynamics remain largely unaffected by colloidal concentration and capillary geometry, these factors significantly influence the onset of instability.
I am also collaborating with Prof. Daniel K. Schwartz’s laboratory in the Department of Chemical and Biological Engineering to investigate the impact of electrokinetics on colloidal transport in interconnected porous media. This work leverages super-resolution 3D single-nanoparticle tracking, combined with an in-house simulation model, to provide mechanistic insights into the system’s dynamic behavior and to bridge microscopic transport phenomena with macroscopic properties.
Overall, my research interests span the fundamentals of colloidal transport and fluid mechanics, complex fluids, rheology, and stimuli-responsive smart fluids, with a focus on integrating advanced simulations and experimental approaches.