Bio
I am currently a second year Ph.D. student studying electronic materials using computer modeling. Up through high school, I attended schools in Johnsburg, Illinois, very close to the Wisconsin border. It is a small town with both suburban and rural qualities. Towards the end of my high school education, I was spending a lot of time studying science and mathematics. Despite a serious lack of advanced placement classes available, several helpful teachers and administrators helped me put an independent study program together, and I was even able to place highly in regional and statewide science competitions. To stay balanced, I also played a trombone for our school’s marching band, wind ensemble, and jazz band as well as running for a season on our cross-country team. 
For college, I attended Northwestern University just north of Chicago on Lake Michigan. I had a great time there, participating in the marching band as well as the science and engineering community on campus. I chose to pursue a degree in chemical engineering, because I found that it was the best way to apply the areas of science I enjoy the most to something that would be useful for society. I also had the opportunity to work at a start-up company doing research and development on materials for the aerospace and electronics industries.
The above experiences led me to pursue a Ph.D. so that I can gain a greater depth of understanding in my field. My hope is that doing computational work will give me a better fundamental knowledge of the principles behind electronic material development than if I focused on experiment alone. Outside of my research, I am active as the current student president of our department’s graduate student organization. In the small periods of free time I have, I like to run, read some non-science books, and have an occasional “game-night” with friends.
Research
Sometimes, systems in nature or in potential future technologies are very interesting but difficult to study with typical experimental techniques. There could be a number of reasons for this. For example, the system might be so complex that it is difficult to separate one independent variable from the rest when running an experiment. In another case, the experiment might be very difficult to carry out such that altering any one independent variable will be time intensive and costly. One approach to solving these problems is using computer modeling. Software combined with an understanding of theory can be used to build a model that describes simplified versions of interesting systems. These computer models alleviate some of the “noise” that comes from environmental effects in an experiment. Also, while experimental work might be very costly to carry out, once you have a computer, only time and knowledge is needed to properly model the system.
In the realm of electronic materials, used in applications ranging from computer chips to solar cells, modeling can be used to accomplish multiple goals. For example, software is often used to engineer and predict the performance of microscale and nanoscale electronic devices in academia and industry. This software often treats materials as a continuum, assuming that individual atoms don’t need to be explicitly defined in the simulations. In addition, Ab initio, or “from first principles”, calculations can be done. With these calculations, quantum phenomena are treated by solving Schrödinger’s equation. This is often used to calculate mechanical and electronic properties of single molecules and crystals. Since these calculations are very intensive, some approximations need to be made because of computer memory and time limitations. Often, these approximations make it difficult to accurately capture long range electronic interactions between molecules, and accurate calculations of electronic excited states also prove to be elusive.
My work is focused on modeling chemical processes that occur during the fabrication of organic electronic devices. 
Instead of using classical semiconductors, like silicon, organic electronics use small to large polyaromatic molecules made primarily out of carbon as a semiconducting material (Figure 1). These novel electronics could be used for integrated chips, light emitting diodes, or photovoltaic devices (solar cells). Besides the fact that carbon is used, another major difference between organic and classical semiconductors is the nature of the crystals that form. While silicon atoms typically form strong covalent bonds between each other where they share electrons, organic molecules form crystals with only weak, nonbonding interactions. Unlike silicon, these forces are difficult to take into account in standard ab initio calculations, and an intricate self-assembly process is usually required to make functioning electronic devices (Figure 2). To model these systems, I use an approach called Molecular Dynamics (MD). In MD, atoms are treated as point particles following Newton’s three laws of motion. Using this method, long range interactions can usually be accurately taken into account. This method can also be used to simulate many thousands of atoms over a typical time period of several nanoseconds. This amount of time is usually long enough to collect thermodynamic data like free energies and heat capacities as well as gain information on crystallization, aggregation, and diffusive processes in the materials.
Currently, I am working to model graphene nanoribbons (GNRs). Graphene is made up of a honeycomb lattice of carbon atoms and has a lot of potential to be used for electronic devices (Figure 3). GNRs are particularly interesting because they have similar electronic properties to classical semiconducting materials, like silicon. Experimentally, GNRs are initially dispersed in solution and then assembled onto a substrate to make a carbon-based transistor. However, there are some major difficulties with this. First, graphene is really “sticky”. Strong non-bonding forces between graphene sheets usually cause them to aggregate in solution into useless clumps of carbon. Also, graphene is really flexible; it has a tendency to roll up on itself, which doesn’t help with the aggregation process either. With MD, we found that if long polyethylene glycol chains are attached to the sides of these ribbons, they tend to wrap around the rest of the ribbon, potentially making a type of “shield” around the GNR. In experiment, these side chains prevent the ribbons from aggregating, forming stable suspensions in solvent.
MD can generally be a useful tool to understand the assembly process of organic electronic materials. As these materials begin to get a wider acceptance in the market (several smart phones already have organic LED displays), the opportunities to use this tool to aid in the design and engineering of organic electronics may grow. MD might be especially useful for studying organic materials used for solar cells. There, nanoscale structures involving many different organic molecules need to be assembled, and understanding these structures through experimental work alone might be difficult. It will be interesting to see where these studies lead.
Prof. Clancy’s page (my advisor’s page) can be found at: http://www.cheme.cornell.edu/people/profile.cfm?netid=pqc1