Our Milky Way’s neighbor, the Large Magellanic Cloud (LMC), is a galaxy significantly shaped by powerful explosions from massive, dying stars that drive gas outflows. These explosions release gas and heavy elements, enriching the galaxy's outskirts and contributing to the formation of stars and planets. Understanding these processes is crucial for studying galactic evolution and the mechanisms that drive it. Our research uses observations from the Hubble Space Telescope to characterize the properties of the outflows from the LMC. Our observations are of light from background stars that pass through the LMC’s gas clouds. These clouds block some of the incoming light, and we analyze the missing features to study the physical properties of the outflows. To compare complex stellar spectra on a similar scale, we fit regions of the light that are free from major features blocking it with a best-fit polynomial. This process helps us differentiate components that either belong to the background star or the LMC’s outflowing gas. By examining the missing light, we gain a deeper understanding of how bursts of star formation impact the galactic environment and ultimately connect our existence to the explosive deaths of distant stars.

As stars begin to die, their surface chemistry changes over time. This is due to the combined effect of two competing processes: (1) gravitational settling that causes heavier elements to sink below the stellar surface and (2) radiative acceleration from photons that push gas upward. Although diffusion is a primary physical process in stellar interiors, its impact on surface chemical abundances is often overlooked in large-scale spectroscopic surveys, leading to systematic biases in stellar age estimates. This project investigates the onset (`turn-on') and suppression (`turn-off') signatures of atomic diffusion as dying stars transition into giants. Using high-resolution optical spectra, we will analyse open-cluster stars across various evolutionary stages to identify the age (or mass) threshold at which diffusion becomes detectable and shuts off. The resulting measurements will constrain the magnitude of diffusion-driven abundance changes, the stellar age (or mass) at which diffusion becomes observable, and the efficiency of abundance restoration during the first dredge-up. It will improve stellar age determinations and enhance the precision of Galactic archaeology and chemical-tagging studies.

ZnO is a wide-bandgap semiconductor with applications spanning optoelectronics, photovoltaics, pharmaceuticals, and related technologies. At the micro- and nanoscale, its functional properties are strongly governed by by surface structure, defect chemistry, and electronic states associated with the crystalline free surface. Targeted lattice doping therefore represents an effective strategy for tailoring surface energetics and enabling new functionalities. Fe incorporation has been proposed to stabilize ZnO nano- and microparticle surfaces by mitigating the internal surface dipoles and passivating dangling bonds. Such provides a controlled materials platform for probing the fundamental bactericidal mechanisms of ZnO. Although the origin of ZnO-induced cytotoxicity remains under debate, our recent findings indicate that surface-mediated interactions with bacteria and/or growth media components facilitate Zn²⁺ ion release from reactive surface defect sites. Surface stabilization through Fe doping is expected to reduce the density of these active sites, thereby limiting Zn²⁺ ion release. In this study, we systematically investigate the bulk and surface characteristics of hydrothermally synthesized Fe-doped ZnO across varying doping dopant concentrations. The antibacterial activity of both pure and Fe-doped ZnO is evaluated against Escherichia coli and Staphylococcus aureus assays. Structural and chemical analyses are performed using X-ray diffraction and X-ray photoelectron spectroscopy, whereas Raman spectroscopy is employed to probe dopant-induced modifications in lattice dynamics and bonding, providing further insight into the relationship between surface states and antibacterial performance.

Zinc oxide (ZnO) is a versatile, inexpensive semiconductor material with unique characteristics. ZnO is particularly known for its inhibitory effects on bacterial growth. ZnO can reduce bacterial growth through mechanisms such as oxidative stress, the deterioration of crucial proteins in the bacterial cell, and the release of Zn²⁺ ions that affect bacterial cell function. The exact mechanism behind ZnO’s antibacterial properties remains unclear. It has been seen that changing the surface and morphology of the particles changes their effectiveness for bacterial inhibition. An additional lesser explored branch of ethanol-based synthesis is solution pH pertaining to ZnO morphology. Our research aims to explore this by doing a wholistic investigation of an ethanol-based synthesis, especially pertaining to how pH affects particle morphology. To produce these materials, we used ethanol-based solvothermal synthesis to create ZnO micro- and nanocrystals. We performed a thorough characterization of these materials to observe changes to the ZnO lattice. This was done by employing scanning electron microscopy (SEM), energy-dispersive X-ray (EDX) spectroscopy, and X-ray diffraction (XRD) spectroscopy.

Graphene quantum dots GQDs possess broad potential in bioimaging and optoelectronics due to their unique optical properties, tunable structure, aqueous solubility, and minimal in vivo and in vitro toxicity. However, despite their solubility, GQD fluorescence may be quenched through interactions with water molecules and aggregation via non radiative decay pathways that reduce emission efficiency. Inspired by the ability of surfactants to prevent quenching interactions for single walled carbon nanotubes, we investigate their utility in preserving GQD fluorescence. Five structurally distinct surfactants, sodium dodecyl sulfate SDS, sodium dodecylbenzene sulfonate SDBS, sodium deoxycholate SDC, sodium cholate SC, and Pluronic F127, are tested across a range of concentrations for preserving fluorescence of top down and bottom up synthesized GQDs to determine optimal conditions. This work reveals that surfactant structure and concentration can non-linearly affect GQD emission in the visible and near-infrared, with SC and SDC providing maximum concentration dependent fluorescence increase. Zeta potential and dynamic light scattering measurements are conducted for each surfactant and GQD system to quantify interfacial charge, colloidal stability, and aggregate size distributions. The present study provides mechanistic understanding of how surfactants influence GQD photophysics, offering strategies to optimize GQD based probes for biomedical imaging and photonic applications establishing a structure-to-function framework that links solution phase organization to fluorescence emission.

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.