(6cq) Computational Design and Characterization of Nanoscale Materials for Applications in Energy, Separations, and Catalysis

In my research I use molecular modeling and ab initio calculations to predict the structural, electronic, and thermodynamic properties of materials. This allows us to accelerate the design of new materials with broad applications in energy, catalysis, and chemical separations. In order to move our society from our current reliance on fossil fuels to sustainable energy sources, we will need to develop advanced materials for energy production and storage, as well as more energy-efficient chemical processing methods.

My graduate research focused on using density functional theory (DFT) to predict vibrational spectra for Si and Ge nanoparticles with impurities and heterojunctions. By understanding how changes to the geometry and electronic structure affect the Raman response, we can interpret experimental spectroscopic data to infer the size, dopant concentration, and interfacial strain in these nanoparticles. My postdoctoral work centers on the design of nanoporous materials for gas storage, separations, and catalysis, with particular emphasis on hydrogen storage and the capture of toxic gases. As a postdoc, I have worked closely with experimental researchers to use computational modeling to offer additional insights into experimental results.

In the future, I plan to build on my background in molecular modeling, materials science, and high-performance computing to discover new materials with applications related to energy, separations, and catalysis. I have identified three areas of research I would like to focus on in my research group:

1. Adsorption-based separations using MOFs, zeolites, and other nanoporous materials
2. Structure and applications of metal nanoparticles supported within porous materials
3. Electronic structure of organic crystals and their application in electronic devices.

Many chemical separations rely on energy-intensive thermal processes like distillation. However, it is difficult and expensive to separate similar molecules, such as close-boiling isomers, using distillation. For example, the C4 aldehyde isomers n-butyraldehyde and isobutyraldehyde are produced in a mixture of n- and iso- isomers that must be separated before they can be used. This separation is currently done by distillation, but sorbent-based separations using nanoporous materials, such as zeolites or metal-organic frameworks (MOFs) offer a better option that is more energy efficient and highly selective. There are a huge number of possible MOFs with diverse textual properties and chemical functionality. This means we can screen thousands of structures and choose a MOF with high selectivity and capacity for separating the specific molecules we are studying. High-throughput screening generates a large amount of data that must be analyzed, so we will use machine learning and data mining techniques to evaluate this data more efficiently and make meaningful predictions about materials properties.

Metal nanoparticles embedded in nanoporous support can be used for many catalysis applications. In many cases, the choice of support influences the catalytic activity of the nanoparticle. For example, there is experimental evidence that some MOFs interact with metal nanoparticles (e.g. Cu in MOF-5) and affect the catalytic process in a way that does not happen in other supports like silica. I plan to use DFT calculations to study this interface between the MOF support and the metal nanoparticle in detail. I am interested in how the electronic structure of the metal nanoparticle is altered by various supports and how this affects the catalytic activity of the particle. I also will use simulations to study the formation of metal nanoparticles in supports to help give insight into experimental synthesis.

I am interested in the use of MOFs and organic crystals in electronic and photonic devices. Some MOFs are conductors or semiconductors and have potential to be used in light-harvesting, chemical sensing, and energy storage applications. However, relatively little research effort is devoted to the electronic structure of these materials. Most device applications will require the MOF to be grown in a thin film on a metal or metal-oxide support. The nature of the interface between these supports and the MOFs is not well understood. I plan to use DFT to study the interface between MOFs and metal or metal oxide supports and how this influences electronic properties such as band structure and electron mobility.

Teaching Interests:

My goal as an instructor in chemical engineering is to give students necessary foundational knowledge and practical skills, while also imparting a sense of the broader connections between various courses they will take throughout their education. Therefore, I am interested in teaching any of the fundamental courses such as thermodynamics, kinetics, transport, and mass and energy balances. Since my background is primarily in molecular modeling and computation, I would also be interested in teaching courses such as numerical methods or MATLAB programming. I am also intrigued by the opportunity to teach more applied courses such as chemical engineering design, which draw from—and synthesize—all elements of chemical engineering.

In addition to these courses, I would be interested in designing and teaching an elective course for graduate students taken from my own research, which focuses on computational materials science and molecular modeling. The course would offer an introduction to density functional theory, molecular dynamics, Monte Carlo methods, and high performance computing. This would be a valuable primer for graduate students who plan to pursue computational research and also provide an accessible overview for students in experimental research who want to gain a sense of how modeling is done and what advantages and limitations various techniques offer.

My interest in teaching extends beyond lecturing in a classroom. The primary role of a professor is mentoring students either in a lecture hall or in a laboratory. As a postdoc, I have supervised several undergraduate students and one masters student doing research. Several of these students have graduated and gone on to jobs in industry or graduate school. I find mentoring students in research to be a rewarding part of academia, and I enjoy building relationships with the students through working together on a challenging research problem.