Aqueous solutions of oppositely charged polymers, under certain conditions, can macrophase separate yielding a polymer rich phase known as a complex coacervate and a polymer poor phase often with vanishing polymer concentrations. We explore this phase separation in systems of block copolymers containing charged blocks and neutral, hydrophilic blocks using a combination of simulation and theory.
In collaboration with Prof. Ian Foster, Prof. Juan de Pablo and colleagues at University of Chicago, we are exploring methods for extracting polymer properties from the literature using machine learning. Resulting polymer properties, as well as associated tools can be found at https://pppdb.uchicago.edu/
We are improving software called ZENO, which is a Monte Carlo based code that computes several quantities including the intrinsic viscosity and hydrodynamic radius. Additionally, the code is used to explore the role of shape on hydrodynamic properties for a class of cube-like particles. Computations are directly compared with experimental results and are found to be accurate within experimental uncertainty. More information including links to the source code and web app version can be found at https://zeno.nist.gov/
Inspired by work using patchy particles to model colloidal systems and protein solutions, we quantify the phase separation and self-assembly of patchy particles using both simulations and theory. In particular, master curves are identified, and a corresponding framework is developed that can be used to parameterize patchy particle models using experimental data.