Gallium oxide is a wide-bandgap semiconductor gaining significance for its outstanding optoelectronic and gas-sensing properties. Although gallium oxide is known for its antibacterial efficacy, limited research is available on the antimicrobial properties of gallium oxyhydroxide (GaOOH). This study investigates GaOOH's antibacterial action by examining the effect of the growth solution's pH on its chemical and physical properties and their correlation with bacterial growth inhibition. The hydrothermal method was used to synthesize GaOOH microparticles (MPs). Deionized water, ammonium hydroxide, and gallium nitrate hydrate salt were mixed to create samples with pH levels ranging from 5 to 10 at 60°C. Subsequent analysis, including scanning electron microscopy, Fourier-transform infrared (FTIR) spectroscopy, and photoluminescence spectroscopy, revealed that higher pH levels increased the average GaOOH MPs length and created more crystal lattice defect sites. The correlation between surface chemistry and pH was evident in the position of higher energy FTIR Ga-OH bending bands. Antibacterial studies demonstrated a greater inhibition of Escherichia coli, a Gram-negative bacterium, at higher pHs. This suggests a potential role of defect sites in GaOOH's antimicrobial activity. There was significant inhibition of Staphylococcus aureus growth. However, no conclusive correlation with pH was established, possibly due to the characteristics of the Gram-positive cell wall. Future studies should further explicate the relationship between GaOOH MPs morphologies and growth inhibition of Escherichia coli and Staphylococcus aureus.

Although there is an effective vaccine for SARS-CoV-2, or COVID-19, the virus is still spreading and affecting millions of people worldwide. SARS-CoV-2, along with many other viruses, is able to form large, multi-nucleated cells, known as syncytia. Syncytia formation, along with syncytia death, may affect the SARS-CoV-2 course of infection. We have been able to compute the death rate of syncytia using data from a study by Vanhulle et al. (2023) that used measurements of electrical impedence to study syncytia formation in cell-cell fusion assays. The death rate of syncytia was found using mathematical modeling. This knowledge can help further our understanding of syncytia and viral disease propagation.

Since the ancient times, a common pigment used for expression in clothes and art was egyptian blue (EB). Today, instead of using this cuprous silicate as a way for one’s personal expression, we will provide reasons why this pigment can be used as a novel bioimaging agent for cell work. Finding another bioimaging agent for cell-use is always an advantage because each agent supplies their own advantages when working in cells. So the more agents we have in our possession, the more angles we can take on a problem. To be considered a bioimaging agent, it needs to dissolve in polar solvents (mainly water), be non-toxic, and display fluorescence in the near-infrared range of the optical spectrum. EB has all three of these properties with the right preparation. Sonicating EB reduces their size to become extremely small sheets, which increases interaction with water molecules to ultimately allow the sheets to dissolve within the water solvent. These sheets are on the nanoscale, so they will be referred to as EB nanosheets (EBNS). EBNS fluoresce in the near infrared and have no history of being toxic. EBNS have the capability of emitting more photons per photons absorbed compared to most materials (high quantum number). This novel material also does not quench fluorescently as easily as other agents due to its copper atoms. EBNS have strong Raman vibrational modes that can help image cells too. We want to highlight why EBNS can be an effective platform for future bioimaging applications and ultimately, cancer imaging/treatment applications.

Several viruses have the ability to cause cells to fuse together into large multinucleated cells called syncytia. It is known that syncytia help the virus propagate without leaving the cell, however it is unknown how the formation rate is affected by temperature. This project aims to use mathematical modeling to investigate the rate of syncytia formation in the HIV virus as temperature varies. A cell-cell fusion mathematical model was used to analyze data from cell-cell fusion assays at various temperatures. Parameters were estimated via minimization of squared residuals, with uncertainties assessed through bootstrapping. These findings could help develop strategies for controlling viral spread.

Cell imaging is an important tool in cancer diagnosis and therapy. Folic acid receptors are overexpressed on the surface of various cancer cells, making it an attractive target for cancer imaging. In our research, we aim to exploit this biological phenomenon by creating Folic Acid Graphene Quantum Dots (GQDs) that can help us selectively target and visualize cancerous tissue. GQDs were used as a base due to their easy functionalization abilities, high cellular viability, and fluorescent properties that allow them to be tracked inside the cell. We functionalized GQDs with folic acid and assessed their structure and morphology as well as optical properties using FTIR, TEM, absorption, and fluorescence spectroscopies. The efficacy of the FA-GQDs is evaluated through their internalization study in cancerous (HeLa) cells at hours 1,6,12, 24, and 48 by utilizing the intrinsic fluorescence of FA-GQDs. In vitro toxicity tests have shown low toxicity (80% viability) of the synthesized FA-GQDs. The proposed FA-N-GQDs provide a novel platform for the detection of cancerous tissues and could be used as a cancer diagnosis biodevice.

The Large Magellanic Cloud (LMC), a small neighboring galaxy around one Milky Way diameter away, provides a unique opportunity to study outflowing gas clouds in great detail. Massive stars in the LMC undergo supernova explosions when they die, blasting gas in all directions. If the gas escapes from the galaxy, a galactic wind is formed. Using data from the Hubble Space Telescope, we can try to better understand how this wind moves and its physical properties. Because there can be numerous of these gas clouds in each direction, we often detect complex patterns that we are characterizing with a Gaussian fitting algorithm. Thoroughly studying the resolved galactic wind of the LMC will ultimately contribute to our understanding of the processes that drive galaxy evolution.

Human Immunodeficiency Virus(HIV) Type-1 has been studied heavily for decades, yet one of the main areas that has yet to be thoroughly researched is that of the cell-cell fusion. This cell-cell fusion creates multi-nucleated cells called syncytia. Cell-cell fusion of HIV can be regulated via cytosine arabinoside(AraC), a chemotherapy agent. Previous work has shown that syncytia and their formation can be modeled via ordinary differential equations, with an Erlang time distribution measuring the fusion of the cells, though this has not been applied to studying drug-treated systems. By applying the mathematical model to the spread of syncytia under drug treatment, we can gain novel information about the formation of syncytia and its regulation by AraC. We find that AraC affects the syncytia formation rate and slightly affects fusions fusion rates, and requires inclusion of the density of syncytia in the mathematical model. This information is much needed for explaining the full workings of HIV in vitro, and will further help the push to develop full models in regards to HIV type-1

Inflowing and outflowing gas in a galaxy’s environment provides an avenue for recycling star-forming materials. To probe these galactic environments, we look for edge-on galaxies with background active galactic nuclei (AGN) at a range of projected positions and orientations relative to the host galaxies. The AGN serve as bright background flashlights that shine light through the intervening gas, enabling us to study the composition and physical properties of the CGM. We use archival spectroscopic observations from the Hubble Space Telescope (HST) and emission-line spectra from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) Survey to probe these clouds. We find 9 existing galaxy-AGN pairs in the archive that meet our criteria, with more to come with future observations and an in-progress proposal for the next HST observation cycle. Our data for the galaxies include spatially-resolved maps of gas density, gas & stellar motion, and gas ionization, which allow us to parameterize quantities like star-formation rates. By comparing the data across a large sample, we assess the influence that a galaxy’s environment has on its star formation.

Massive stars die through powerful supernova explosions, which produce clouds of gaseous debris that can be propelled to the outskirts of the galaxy. The material on the outer edge is more vulnerable to processes occurring in the environment. These processes pull and tug the debris and can form a gaseous stream flowing from the galaxy. One prominent example in the night sky is the Magellanic Stream (MS), which flows out of our neighboring galaxy, the Large Magellanic Cloud (LMC). With observations from the Hubble Space Telescope, we are examining the absorption features of light from background stars that pass through the gaseous material of the MS enabling us to measure its physical properties. We traced the small-scale motion of the neutral hydrogen gas using emission-line data from the Galactic All-Sky Survey and the Galactic Australian Square Kilometre Array Pathfinder programs to determine where the MS begins relative to the LMC. Comparing these observations, we find the MS in the absorption spectra on the nearside of the LMC between +235 ≤ vlsr ≤ +350 km/s. By investigating the physical properties of the MS, we can better understand how the environmental processes shaped its formation.

Surface defects in nano- and micro-crystals strongly affect performance of materials in applications, necessitating elucidation and control of those defects. The beta variant of gallium oxide (β-Ga2O3) in nano- and microcrystalline form is attracting a strong interest due to its potential applications in such critical areas as biological therapeutics, optoelectronics, and catalysis. In our studies, β-Ga2O3 crystals are produced through a simple bottom-up hydrothermal method, which yields, as a first step, an α-GaOOH precursor, which then undergoes calcination to bear the final product. Variation of growth parameters allows for a synthesis of particles with tunable morphologies and surface structures. Optoelectronic and physicochemical properties of both α-GaOOH & β-Ga2O samples are studied by a range of experimental techniques. These investigations address, among others, the surface defect properties. We also evaluate the impact of surface defects and particle morphologies on the antibacterial action α-GaOOH.

Currently our lab is designing a system that allows us to leverage cathodoluminescence spectroscopy to study the optoelectronic properties of gallium oxyhydroxide and gallium oxide. This system would allow us to place our samples within a vacuum chamber and irradiate it with a high-energy electron beam, causing light emissions that are then collected by a fiber optic cable. This optical system allows us to capture the emissions and investigate them as its characteristics are dependent on the material properties of the sample. Furthermore, since we are working in ultra-high vacuum conditions, the components of the system have to be designed with careful consideration, in addition to allowing several degrees of freedom in order to precisely position our sample within the vacuum chamber.

The herpes virus, like many other viruses, can be engineered to target and kill cancer cells. The herpes virus, when loaded with immune stimulating factors, like interleukin 12, can be even more effective at killing cancer cells. We use a mathematical model of oncolytic virus infection and apply it to experimental data from Fukuhara et al. (2023) to assess the effectiveness of different herpes virus strains in treating cancer. We are able to estimate virus characteristics such as viral production rate and infectious lifespan of the different strains, allowing for a quantitative comparison. This type of analysis can help identify which strains are most effective at killing tumors.

Researchers use mathematical models of cancer to study the effectiveness of different regimens of chemotherapy when treating tumors. These models help predict how different treatments affect cancer cell growth in hopes of determining which will effectively kill a tumor. Realistic pulsed drug treatments are computationally expensive and difficult to analyze mathematically. We examine when the effect of a pulsed drug treatment can be well-represented by a constant dose model. Our approach studies treatment applied in various cancer growth patterns, such as exponential, linear, logistic, Mendelsohn, surface, Gompertz, and Bertalanffy models. Mathematically modeling and analyzing the comparison between tumor growth under a pulsed drug treatment and under a constant dose helps us understand when the use of the simpler model can make accurate predictions.

Syncytia are the multinucleated cells that can occur due to virus infection of cells. Mathematical models in the form of ordinary differential equations can be used to simulate the growth of these infections. Several ODE models can explain syncytia growth. Before employing these models on actual data, it is essential to analyze their structural and practical identifiability. Structural identifiability is an inherent property of each model and its parameters, referring to our ability to determine parameter values for the model. Practical Identifiability analysis of a model is concerned with accurately determining parameter values given experimental error. Obtaining accurate parameter values allows us to make conclusions about our data within the context of our model that can provide insight into the nature of the spread of syncytia. These two techniques allow us to determine whether or not the parameters of a model are identifiable with the data we plan to collect. Consequentially, we can plan experiments adequately to truly parameterize the data in the contexts of our model and make accurate conclusions.

Currently, research of gallium oxide (GO) nano- and microcrystals is rapidly expanding with the demand for potential uses. GO has been shown to be a promising material for possible applications in many different fields including photocatalysis, biomedicine, and optoelectronic devices. In our lab (led by Dr. Strzhemechny) we examine both the fundamental (nature of crystal defects) and applied (antibacterial action) properties of GO. During the hydrothermal growth process of GO, we are producing different nano and microscopic morphologies of this material by controlling various growth parameters including varied pH and adding surfactants to the material. The synthesis procedure includes using the precursor material, gallium nitrate hydrate, ammonium hydroxide. We use a calcination furnace that can get to temperatures high enough to effectively synthesize GO. Now, with a thermocouple and pyrometer we can predict outcomes during the calcination step with high accuracy and precision.

Open clusters are groups of stars with the same age, chemistry, and velocity. These characteristics make open clusters powerful tools for tracing the dynamic and chemical evolution of our home galaxy, the Milky Way. The goal of the Open Cluster Chemical Abundance and Mapping (OCCAM) survey is to identify and analyze a large sample of open clusters with a wide range of chemical abundances. To do this, it utilizes the infrared spectra provided by the Sloan Digital Sky Survey’s (SDSS) APOGEE spectrograph and the kinematic data from the Gaia Space Telescope to form a large survey of open clusters with uniformly derived chemical abundances (e.g., C, Mg, Si, Al, Fe, Ni). Here, we present the results from the OCCAM analysis of the latest SDSS/APOGEE data release. This dataset of 153 different open clusters, including 2061 individual stars, is used to investigate the variation of the Milky Way’s chemistry for multiple different abundance groups. In addition to this dataset, we also present the current status of new optical observations that will allow us to expand the wavelength coverage for each star and trace more elements. These new observations enable us to accurately decipher the chemical fingerprints from ancient supernovae (e.g., Y, Ba, Ce, Nd, Eu) and expand our analysis.

Characterizing Galactic sub-structures is crucial to understanding the assembly history and evolution of the Milky Way. To accomplish this, we need to identify and analyze the accreted sub-structures. With ESA Gaia and SDSS-IV/APOGEE, studies have been done to analyze the kinematics and chemical abundances, respectively. However, one challenge that still remains is deriving reliable ages for these sub-structures. We utilize the new relationship between the carbon to nitrogen ratio and stellar age derived by the OCCAM team, which has recently been extended to the metal-poor regime, to probe stars within the sub-structures in the metallicity range -1.2 ≤ [Fe/H] ≤ +0.3 dex. This allows us to determine the ages of a greater number of stars within these sub-structures, which paints a more coherent picture of the original galaxies that have been disrupted to form the Milky Way’s halo. Using the sample of halo sub-structures in Horta et al. (2023), we apply the newly extended calibration to determine ages of stars within these sub-structures and compare them to previous age estimates.

Graphene quantum dots (GQDs) is an emerging nanocarbon platform that is now actively utilized for therapeutic applications. Their increasing popularity arises due to relatively high biocompatibility, water solubility, optical properties enabling multi-color fluorescence imaging and the ease of functionalization with a variety of therapeutic agents. Such properties pave the way for a variety of imaging and sensing applications. Herein, we are utilizing rGQDs (reduced graphene quantum dots) synthesized top down from reduced graphene oxide for dopamine sensing. Detecting dopamine can provide insights about the neural health and the activity of neurotransmitters in the brain. However, due to the presence of dopamine receptors throughout our body, this will also help assess other vital functions including secretion of pituitary hormones [1], gut motility [2], immunomodulatory effects in inflammation-related diseases [3][4] and cardiovascular effects (dopamine can act as both autocrine or paracrine compound in the mammalian heart) [5]. In our work rGQD near-infrared (NIR) fluorescence appears to react proportionally to dopamine concentration within the range of 1000ng/ml – 1ng/ml as assessed with NIR fluorescence imaging of dopamine/rGQD interactions on cotton discs and biocompatible gels as well as with NIR fluorescence spectroscopy. This rapid NIR response and the capability of dopamine sensing in gel matrix suggests the potential for detection of blood-relevant dopamine concentrations in vivo, which will be explored with GQD-based implantable sensors. In addition to the development of a novel non-invasive dopamine sensing mechanism, the present study will aid in gaining valuable insight into GQD properties in vivo and their potential for in vivo analyte detection.
References:
1. Nira Ben-Jonathan, Robert Hnasko, Dopamine as a Prolactin (PRL) Inhibitor, Endocrine Reviews, Volume 22, Issue 6, 1 December 2001, Pages 724–763, https://doi.org/10.1210/edrv.22.6.0451
2. Graeme Eisenhofer, Anders Åneman, Peter Friberg, Douglas Hooper, Lars Fåndriks, Hans Lonroth, Béla Hunyady, Eva Mezey, Substantial Production of Dopamine in the Human Gastrointestinal Tract, The Journal of Clinical Endocrinology & Metabolism, Volume 82, Issue 11, 1 November 1997, Pages 3864–3871, https://doi.org/10.1210/jcem.82.11.4339
3. Channer B, Matt SM, Nickoloff-Bybel EA, Pappa V, Agarwal Y, Wickman J, Gaskill PJ. Dopamine, Immunity, and Disease. Pharmacol Rev. 2023 Jan;75(1):62-158. doi: 10.1124/pharmrev.122.000618. Epub 2022 Dec 8. PMID: 36757901; PMCID: PMC9832385.
4. Feng YF and Lu Y (2021) Immunomodulatory Effects of Dopamine in Inflammatory Diseases. Front. Immunol. 12:663102. doi: 10.3389/fimmu.2021.663102
5. Neumann J, Hofmann B, Dhein S, Gergs U. Role of Dopamine in the Heart in Health and Disease. Int J Mol Sci. 2023 Mar 6;24(5):5042. doi: 10.3390/ijms24055042. PMID: 36902474; PMCID: PMC10003060.

This study addresses the escalating concern over the interaction of multiple respiratory viruses by introducing a mathematical model to analyze triple infection dynamics involving influenza (IAV), respiratory syncytial virus (RSV), and SARS-CoV-2. With the ongoing COVID-19 pandemic and the resurgence of RSV, understanding the dynamics of triple infections is critical for public health preparedness. Comprehending the interactions among these viruses is crucial for improving our capacity to forecast and curb disease outbreaks. The central question addressed in this study is how variations in infection rates influence the duration and maximum population size of each virus in a triple infection scenario. Prior research has explored coinfections involving two respiratory viruses, yet triple infections, especially among adults, remain infrequent and poorly elucidated. The urgency to address these questions arises from the potential for overwhelming hospitals and exacerbating disease burden, especially in vulnerable populations. By developing a mathematical model to analyze triple infections, this research aims to provide insights that can inform public health strategies and mitigate the impact of respiratory virus outbreaks. Through extensive simulations, the study evaluates how variations in infection rates influence the duration and maximum population size of each virus. The findings unveil intriguing patterns: while SARS-CoV-2 demonstrates remarkable resilience across various infection rates, influenza and RSV display more nuanced responses, exhibiting sensitivity to changes in transmission rates.

Graphene Quantum Dots (GQDs), with their outstanding optoelectronic, chemical, and bio-compatible properties serve as versatile materials for various imaging applications. Intriguing optical properties at ultralow cryogenic temperatures have been observed in other carbon-based nanomaterials suggesting a potential for similar behavior in GQDs. This study explores GQD fluorescence across the visible and near-infrared spectral regions at temperatures ranging from ambient (300 K) down to cryogenic (76K) via experimental measurements supported by complementary DFT calculations. Our findings demonstrate a decreasing linear relationship between integrated density and temperature making GQDs a viable candidate for applications in low-temperature imaging.

Graphene quantum dots (GQDs) have emerged as a forerunner of carbon nano-biotechnology due to their multifunctional delivery and imaging capabilities as they exhibit fluorescence in the visible and near-infrared, high biocompatibility, and water solubility. These properties put GQDs forward as a compelling drug delivery platform that has already been utilized in a variety of applications including the delivery of chemotherapeutics, antibiotics as well as siRNA and CRISPR-based gene therapy. However, cellular entry pathways of this nanomaterial still remain largely undefined. In a number of studies describing GQD cellular internalization different and, often, conflicting results have been presented due to surveying only few endocytosis inhibitors and disregarding their potential off-target pathways. Understanding the cell internalization routes of GQDs is crucial while delivering drugs in different types of cell lines. Herein, we performed a holistic approach to cell uptake studies on GQDs of different charges by the comparative study of their preferred endocytosis paths in non-cancerous (HEK-293) and cancerous (HeLa) cell lines. The concentration and cell viability of GQDs were determined by MTT assays, while their endocytosis paths were investigated through confocal fluorescence microscopy on cells treated for up to 24 hours. The potential for GQD interactions with the cell membrane was also examined via zeta (ζ) potential measurements. Our findings provide insights into the internalization mechanisms of the GQDs into cell membranes of healthy and cancer cells. The optimization of these mechanisms can serve for the enhancement of a variety of novel GQD applications in biomedicine including therapeutic delivery, disease detection through sensing as well as diagnostic imaging.

Due to high tissue penetration depth and low autofluorescence backgrounds, near-infrared (NIR) fluorescence imaging has recently become an advantageous diagnostic technique used in a variety of fields. However, most of the NIR fluorophores do not have therapeutic delivery capabilities, exhibit low photostabilities, and raise toxicity concerns. To address these issues, we developed and tested five types of biocompatible graphene quantum dots (GQDs) exhibiting spectrally-separated fluorescence in the NIR range of 928–1053 nm with NIR excitation. Their optical properties in the NIR are attributed to either rare-earth metal dopants (Ho-NGQDs, Yb-NGQDs, Nd-NGQDs) or defect-states (nitrogen doped GQDS (NGQDs), reduced graphene oxides) as verified by Hartree-Fock calculations. Moderate up to 1.34% quantum yields of these GQDs are well-compensated by their remarkable >4 h photostability. At the biocompatible concentrations of up to 0.5–2 mg ml−1 GQDs successfully internalize into HEK-293 cells and enable in vitro imaging in the visible and NIR. Tested all together in HEK-293 cells five GQD types enable simultaneous multiplex imaging in the NIR-I and NIR-II shown for the first time in this work for GQD platforms. Substantial photostability, spectrally-separated NIR emission, and high biocompatibility of five GQD types developed here suggest their promising potential in multianalyte testing and multiwavelength bioimaging of combination therapies.

Graphene quantum dots (GQDs) represent the forefront of contemporary research within the domain of biophysics. Known for their innumerable applications, these nanoparticles have remarkable functionalities in cellular imaging and drug delivery applications. In our research, we combine NGQDs (Nitrogen-doped GQDs) with the ligand L2 to create a drug delivery system for L2, an anti-Alzheimer’s drug. L2 faces challenges in traversing the blood-brain barrier (BBB) due to its inherent properties. However, the BBB is permeable to NGQDs due to their small size. Hence, we are using NGQDs as a vehicle to facilitate the transport of L2 across the BBB. Furthermore, the intrinsic fluorescence of NGQDs within the body enables us to safely monitor and track the hybrid system, ensuring its successful delivery to the targeted organ – the brain.

The Smith Cloud is a high-velocity cloud (HVC) on its final approach to the Milky Way galaxy
and shows evidence of interaction with the Milky Way’s disk. We investigate the gas-phase chemical depletion patterns in the Smith Cloud using UV absorption-line observations toward two background QSOs taken with the Hubble Space Telescope (HST)/Cosmic Origin Spectrograph (COS) G130M grating. We also use high signal-to-noise H i 21-cm emission-line spectra that were taken with the Green Bank Telescope (GBT). We find strong evidence of silicon gas-phase depletion, with [Si/S] = −0.74(+0.26)(−0.27) and [Si/O] ≲ −0.30, implying a possible presence of dust containing silicon within the Smith Cloud. We additionally find evidence of near-solar metallicity within the Smith Cloud ([S/H] = +0.08 ± 0.09 ± 0.15) along a sightline near to where we find dust. We present evidence that the Smith Cloud progenitor previously encountered the Milky Way’s galactic plane, polluting its gas with metals and dust.

There is currently a mismatch between the chemical properties of a typical star and those within star clusters across the Milky Way galaxy. Star clusters are groups of stars bound by gravity, many of which are found in the disk of the Milky Way. Studying these star clusters reveals essential information about the rich history of our Galaxy, as we can measure their age and their chemical composition independently. While some clusters interact with their environment, causing them to dissolve, other clusters remain bound for billions of years. In order to investigate these disruption events, we will study the evolution of star clusters throughout cosmic time via simulations. With the use of cosmological simulations, such as the Feedback In Realistic Environment (FIRE) simulation, we are able to learn why clusters move from their original place of formation and how far they go. Additionally, FIRE allows us to trace star clusters through their different stages of their evolution, and study how they survive as they interact with other components of the galaxy. This enables us to investigate where open clusters form, if and why they move from their radius of formation, and how they traverse and interact with the Galaxy over time. In this work, we focus on tracing the unique trajectories of three illustrative open clusters throughout time. In the future, we aim to compare the FIRE-2 simulation results to the observed results from the SDSS-based Open Cluster Chemical Abundance and Mapping (OCCAM) survey.