Oxidative stress, an imbalance of reactive oxygen species, has been shown to participate in a multitude of diseases from Alzheimer to cancer. Thus, there is a search for radical scavenging agents capable of circumventing oxidative stress. Due to their remarkable properties, quantum dots are known to be utilized in a variety of applications including binding of reactive oxygen species (ROS). However, the translation of nanomaterials to clinic is often hampered by their off target toxicity. Thus, the aim of our work is to develop and test fully biocompatible graphene quantum dots (GQDs) with a variety of dopants that will the tune radical scavenging activity (RSA) of the GQD. We have synthesized and tested over ten types of doped GQDs and accessed their radical scavenging ability via DPPH, KMnO4, and RHB assays. Among those, thulium and aluminum doped GQDs show superior scavenging.

Respiratory syncytial virus (RSV) can cause a severe respiratory illnesses particularly in young children and the elderly. Defective viral genomes (DVGs) have recently been found during RSV infections and are thought to be linked to the severity of the illness. In this study, we use mathematical models to simulate the spread of RSV using data from environments in which DVGs are detected early and late in order to estimate infection rates and other infection parameters in each setting. We find that the presence of DVGs is reflected in changes in the infection rate and viral clearance rate of infections.

During the first billion years after the Big Bang the first, faint, galaxies formed. With luminosities less than one millionth that of our Milky Way galaxy, they are too faint to be observed by even our most advanced telescopes. A fraction of these first galaxies are preserved as ultra-faint dwarf galaxies in the local universe. These ultra-faint dwarfs are the fossils of the first galaxies. Therefore, we can study the faintest satellites of the Milky Way and learn about the formation and evolution of the first galaxies using galactic paleontology. We know that the stellar properties of the faintest Milky Way satellites match the stellar properties of galaxies formed in high resolution hydrodynamic simulations of the first billion years. We also know that the semi-analytic model Galacticus can reproduce the stellar properties of the faintest Milky Way dwarfs in the modern epoch. In this work, we determine whether Galacticus is also able to match the high resolution simulations of the first billion years.

The creation and evolution of elements throughout time across the Milky Way disk provides a key constraint for galaxy evolution models. To provide these constraints, we are conducting an investigation of the zirconium, neodymium, cerium, and barium abundances created in supernovae explosions, for a large sample of open clusters. The stars in our study were identified as cluster members by the Open Cluster Chemical Abundance & Mapping (OCCAM) survey that culls member candidates by Doppler velocity, metallicity, and proper motion. We have obtained new data for the elemental abundances in these clusters using the Subaru Observatory 8-m telescope in Hawaii with the High Dispersion Spectrograph (HDS). Analyzing these neutron-capture abundances in star clusters will lead us to new insight on star formation processes and the chemical evolution of the Milky Way galaxy.

Cancer is a leading cause of death worldwide with around one in every six caused by cancer, but many cancers can be cured if treated properly. Mathematically programmed cancer cell models can be used by researchers to study the use of oncolytic viruses to treat tumors. With these models, we are able to help predict the viral characteristics needed in order for a virus to effectively kill a tumor. Our approach uses both cancerous and non cancerous cells in relationship to the tumor to determine the speed at which the cells replicate, however there are several models used to describe cancer growth, including the Exponential, Mendelsohn, Logistic, Linear, Surface, Gompertz, and Bertalanffy. We study how the choice of a particular model affects the predicted outcome of treatment.