Graphene quantum dots (GQDs) have gained significant attention due to their unique optical properties, biocompatibility, and potential applications in bioimaging, biosensing, and optoelectronics. The breakdown of single-walled carbon nanotubes provides an alternative method of producing GQDs that has the potential to be more efficient than current methods. We will investigate the effectiveness of various methods to break down single-walled carbon nanotubes, including through UV-light irradiation. Solutions of carbon nanotubes with sodium hypochlorite are placed under 254nm UV-light for two hours, and fluorescence in the visible spectrum is measured before and after UV-light irradiation to observe the production of GQDs. The use of surfactants in these solutions can affect the resulting fluorescence, so solutions of sodium dodecyl sulfate (SDS) and sodium dodecylbenzene sulfonate (SDBS) are also UV-light irradiated and observed. We will perform transmission electron microscopy (TEM) analysis on the samples to characterize the resulting GQDs and determine their size distribution. The findings from this study will contribute to the broader scientific community by improving an avenue of production for GQDs through conversion of carbon nanotubes into smaller, more functional materials while reducing the toxicity associated with carbon nanotubes.

Viral entry in a host cell is mediated by interacting viral fusion proteins and cell receptors. After entry, newly translated viral fusion proteins can end up on the surface of the infected cell. If the infected cell comes into contact with a cell expressing the associated receptor, the interaction can result in membrane fusion. The result of this fusion is a multi-nucleated cell, called a syncytium. Syncytia can cause an increase in severity and duration of an infection, as well as cause damage to the surrounding tissue. Syncytia formation is heavily dependent on spatial interactions and some models are not able to represent this component whatsoever. Agent-based models (ABMs) can accurately represent the temporal and spatial components of syncytia formation by simulating interactions between individual cells. We developed an ABM that can model syncytia formation for up to one million cells at a time. Implementing this model computationally, we have begun fitting to cell-cell fusion experimental data. This model allows us to get new spatial parameters that have never been looked into before. By investigating the spatial aspects, we will develop a better understanding of the role of syncytia during viral infections.

Defective interfering particles (DIPs) are virions missing the viral genome that allows them to replicate on their own, so they require coinfection with a standard virion to enable replication, interfering with the production of standard virus in the process. DIPs may also stimulate an interferon (IFN) response that further suppresses standard virus replication. Our aim was to evaluate the impact of DIPs and IFN on viral replication. We used Python programming to simulate a mathematical model evaluating the effects of DIPs and IFN on viral replication. Features of the viral titer curve were measured, including peak viral load and area under the viral curve, as functions of IFN parameters and DIP production rates. We examined a range of parameter values for DIP production rate and IFN response strength to assess the effects of DIPs and IFN independently and together. DIP production rate over a range of values resulted in no change in DIP or standard virus population dynamics. However, decreased IFN response resulted in an increase in standard virus and DIP population, while increased IFN response resulted in decreased standard virus and DIP population. DIP production in isolation did not impact viral replication, while IFN demonstrated an inverse relationship to viral replication and DIP production. Increased IFN and DIP production rate led to a reduction in infection intensity. IFN is essential to the antiviral effects of DIPs.

The lining of the human respiratory tract (HRT) has a layer of ciliated cells known as an epithelium. When exposed to virus, these cells actively push virus into mucous layers lining the epithelium and then funnel this mucous up and out of the human respiratory tract. This process is called mucociliary clearance (MCC) and is the first line of defense against a viral infection. We know that MCC plays a role in preventing respiratory infections, but we know little else. We hypothesize that, under the right conditions, MCC prevents infection by limiting the ability for virus to enter the lower respiratory tract. To test this, we constructed a compartmental model that uses a system of diffusion-driven partial differential equations to describe the virus propagation in the HRT as a travelling wave front with an advection term included to approximate MCC.  Our model shows that MCC can change the waveform of the virus propagation, and suggests that there exists a critical advection speed that prevents virus from entering the lower respiratory tract.

Galaxy simulations are an effective way to study the evolution of galaxies across
cosmic time. They have provided insights into the structural and chemical evolution
of galaxies, gas and star formation, and how LCDM models predict the large scale
structure of universe. Nevertheless, two primary issues have persisted using LCDM -
the core-cusp problem and the diversity of rotation curves for dwarf galaxies of similar
masses. To determine the effect of AGN on these issues, we utilize FIRE-2, which only
includes stellar feedback. We chose this particular galaxy at redshift 0 and compared
the curve to 8 previous observations, and we find that the innermost regions of the
curve are better matched to the data, but diversity still remains a problem. Thus, we
conclude that AGN feedback prescriptions may be removing too much mass from the
center of the galaxy, causing this discrepancy. Hence, more work is necessary to identify
the cause of this issue and potentially resolve it.