Materials Science and Engineering PhD Student Lunch and Seminar


Location: W210 Duncan Student Center

Two students in the Materials Science and Engineering PhD program will provide an update on their graduate research. Students in the program and students interested in learning more are welcome!

To RSVP, contact Heidi Deethardt for the meeting invitation.


Angela Abarca Perez, Civil and Environmental Engineering and Earth Sciences

Title: "Rational design of catalytic polymeric membranes for treating nitrate in drinking water"

Abstract: Catalytic hydrogenation is a promising process to treat recalcitrant pollutants in drinking water (e.g., nitrate, perchlorate, trichloroethylene). Metallic nanoparticles have shown a great potential as water-purification catalysts due to their high surface area and shape-dependent catalytical properties, enhancing the reactivity and degradation of contaminants. Two practical limitations exist that have limited the use of catalysis in drinking water treatment: mass transfer limitations from traditional three-phase packed bed reactors, and high costs associated with noble metals. Herein, these challenges are addressed in the design of a nanostructured triblock polymer membrane based on polysulfone and polystyrene-b-poly (vinyl pyridine) composite membranes that is loaded with near single-atom catalysts. The membrane has short pore diffusion links, which increases the mass transfer rate and thus the overall rate of reaction. Near single atom loading was accomplished through the surface modification of the membrane with functional groups that provide attractive coordination with metals, specifically Pd. This drastically reduces the cost of using noble metals, which are orders of magnitude more reactive than more earth-abundant “catalysts.” We demonstrate the use of the membrane in batch and flow through reactors for the hydrogenation of a model oxidized contaminant, nitrite. Understanding how these novel ligands interact with Pd to remove nitrite can serve as a base for future research to be tailored to remove other type of contaminants like halogenated organic contaminants or Per- and polyfluoroalkyl substances. In addition to contribute to the development of cost-effective sustainable water treatment technologies, by providing a membrane that has higher removal with significant lower quantity of Pd.

Hanfeng Zhang, Aerospace and Mechanical Engineering

Title: "Machine learning-assisted exploration of thermally conductive polymers based on high-throughput molecular dynamics simulations"

Abstract: Finding amorphous polymers with higher thermal conductivity is important, as they are ubiquitous in a wide range of applications where heat transfer is important. With recent progress in material informatics, machine learning approaches have been increasingly adopted for finding or designing materials with desired properties. However, limited effort has been put on finding thermally conductive polymers using machine learning, mainly due to the lack of polymer thermal conductivity databases with reasonable data volume. In this work, we combine high-throughput molecular dynamics (MD) simulations and machine learning to explore polymers with relatively high thermal conductivity (>0.300 W/m-K) – a statistically important threshold as most neat polymers have thermal conductivity lower than this value under normal conditions. We first randomly select 365 polymers from the existing PoLyInfo database and calculate their thermal conductivity using MD simulations. The data are then employed to train a machine learning regression model to quantify the structure-thermal conductivity relation, which is further leveraged to screen polymer candidates in the PoLyInfo database with thermal conductivity >0.300 W/m-K. 121 polymers with MD-calculated thermal conductivity above this threshold are eventually identified. Polymers with a wide range of thermal conductivity values are selected for re-calculation under different simulation conditions, and those polymers found with thermal conductivity above 0.300 W/m-K are mostly calculated to maintain values above this threshold despite fluctuation in the exact values. Given the observed uncertainties in the MD-calculated TC, we have also constructed a Bayesian neural network to evaluate the epistemic and aleatoric prediction uncertainties, where a state-of-the-art approximate Bayesian inference algorithm is used for scalable training. The strategy and results from this work may contribute to automating the design of polymers with high thermal conductivity.

Originally published at