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.

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.

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.

Influenza virus causes periodic pandemics and thousands of deaths annually, but many of the details of the viral replication cycle are still poorly understood. This study develops a mathematical model of the dynamic transitions of a virus from the extracellular space through the initial intracellular replication processes. These stages include: binding, endocytosis, HA Acidification, Fusion, and Uncoating. Experimental data from the viral entry phases were fit to a system of differential equations, which represent the biological processes. The model parameters were estimated using optimization techniques that minimize the sum of squared residuals, thereby aligning model predictions with observations. An identifiability analysis was performed to see which parameters can be estimated with the given model and available data. We find that the model fits the experimental data well with identifiable parameters, allowing us to characterize the different stages of viral entry. The model can be used to compare different viral strains or treatment options, in addition to helping explain the kinetics of viral entry.