Nanoscience Research: Mapping PLE Shift of Carbon Nanotubes
The summer before my senior year of high school, I interned for a program called SHARP, which placed me inside the Nanoelectronics and Nanostructures Group at UC Berkeley.
There I met Dave and Pat, two phenomenal grad student mentors whom I assisted with their research. I worked on two projects, one with nanomagnets, and the other with carbon nanotubes.
Carbon nanotubes are rolled tubes of graphene, which are sheets of carbon just one atom thick. Graphene is actually one of the strongest materials in the world (in terms of tensile strength), about 100x stronger than steel. Naturally, carbon nanotubes have a host of remarkable properties. In electronics, their small band gaps and ballistic conductance means that they are lower energy and faster than silicon. Because of this, they make really efficient semiconductors, and could one day replace silicon in computers.
Each carbon nanotube has a specific chirality, which is essentially the angle & radius at which it is rolled. This chirality determines specific properties, such as how conductive the nanotube is.
Pat’s Project
My grad mentor Pat grew carbon nanotubes and used a technique called photoluminescence excitation (PLE) to examine the electronic properties of the different chiralities, such as their respective band gap energies. PLE involves exciting electrons with a certain wavelength (λ) of light and measuring the new wavelength of the light that is emitted.
And it produces images like this:
My goal was to graphically display the relative excitation/emission λ of each chirality we tested. I used MATLAB to find the peak intensities in the raw data and overlay them on the heat map. We saw that the theoretical excitation/emission λ of each chirality were very close, but not exactly consistent with the observed λ.
We wanted to see just how much disparity there was between the observed (actual) and theoretical (predicted) λs, so I mapped the shift:
There didn’t seem to be significant variance in the results, but a more extensive analysis is needed. Ideally, one would take many PLE datasets and look at the average shift for nanotubes of each chirality. For instance, maybe nanotubes with chirality (9,2) are remarkably consistent, whereas (10,5) nanotubes have a wider range of excitation/emission λ.
Some further data visualization we did for fun:
This was a pretty fun project which was more focused on interesting ways to visualize the data. Understanding chirality and band gaps was conceptually challenging, but the visualizations were that much more helpful as a result. I learned data vis tools in MATLAB, got to play around with an Atomic Force Microscope (AFM), and learned an incredible amount about materials research. Overall, a fantastic summer.
