385611 Predictive Theoretical Modeling of Complex Fluids: From Advanced Materials to Engineering Nanomedicine
Theoretical modeling provides insights into fundamental understanding of many complex systems ranging from organic-inorganic hybrid materials to biological fluids. A suitably chosen coarse-grained theory that captures the essential physics of the system can be predictive and guide the materials design for a variety of applications before conducting exhaustive experimental efforts or computational tests of the full complex problem. Here I demonstrate the application of classical theoretical modeling in two nanoparticle systems followed by my proposed research plans.
Nanoparticle-organic hybrid materials (NOHMs) are a new type of complex fluid consisting of 10 nm diameter spherical inorganic core particles surface-functionalized with oligomeric organic molecules with no other solvent. The absence of a solvent, the small size of the nanocores and oligomers, and the incompressibility of the tethered oligomeric fluid make the oligomer-mediated interactions non-pairwise-additive. In my PhD research, I developed a classical density-functional approach for model hard spheres with tethered bead-spring oligomers that allows a direct description of the system free energy as a functional of the probability densities of cores and oligomers without assuming pairwise additivity. Based on the coarse-grained model and my theories, I predicted a striking result of solvent-free NOHMs that the structure factor must go to zero at zero wave number, which can be used to characterize the polydispersity in experimental systems. I also discovered that the carbon capture capacity, disorder-order phase transition, and transport properties of these materials are governed by the oligomer-configurational entropy as the tethered hairs have to uniformly fill the interstitial space. These predictions have been verified by relevant molecular dynamics simulations and experiments.
The use of ligand-functionalized nanocarriers that bind to specific receptor proteins is gaining translational prominence in targeted vascular drug delivery. Owing to the wide spectrum of time and length scales involved in the hemodynamics and nanocarrier-endothelium binding landscape, a unified theoretical framework bridging the microscale many-body hydrodynamic interactions in the vessel tube and the nanoscale nanocarrier adhesion on the vessel wall would provide useful insight into the tailored design of functionalized nanocarriers for specific targeted biophysical environments. As a postdoctoral researcher, I developed a theoretical formalism for studying nanocarrier-cell adhesive dynamics in the presence of red blood cells (RBCs) using a combined framework of generalized Langevin equations and dynamical density-functional theory. Analyzing the RBC-driven margination and the position/velocity autocorrelation functions of the nanocarriers for different vessel diameters, blood flow rates, and binding free energies would allow the quantification of nanocarrier binding affinity and the prediction of biodistribution. The predicted correlation functions for nanocarrier motion have been validated by finite-element direct numerical simulations of Navier-Stokes fluid around a moving nanoparticle at different separations from the vessel wall.
My proposed research plans are aimed at addressing some of the key challenges in engineering nanomedicine and advanced materials design using nanoparticle-based systems. By combining classical theories and computational techniques, my research group will explore how the active mass transport of the magnetic nanoparticle is affected by physiologically relevant interactions in blood flows, how the drug-loaded nanoparticle may overcome biophysical barriers in the extracellular matrix to reach the target site, and how the functionalization of the particle can be optimally designed to achieve self-assembling bioactive nanostructures. Concurrently, I will develop and apply new theoretical and simulation approaches to advance fundamental studies of systematic materials processing, complex networks characterization, and ordered directed self-assemblies for energy applications.