Anxiety related to academics, especially with regards to testing, is a rapidly growing mental health issue impacting all undergraduates at TCU. High levels of test anxiety have been shown to inhibit memory recall, reduce exam scores, and promote poor study habits. Expressive writing is a form of test anxiety intervention consisting of a type of free response developed to allow students to release their minds of anxious thoughts and emotions related to exams. Expressive writing has been shown to reduce test anxiety and improve exam performance. However, the effectiveness of expressive writing may be mitigated by a students’ level of emotional intelligence. Emotional intelligence is defined as the capacity to be aware of, control, and express one’s emotions. In order to better understand the connection between emotional intelligence and expressive writing, I conducted an exploratory mixed-methods study using quantitative survey data to inform our selection of interview participants and the initial development of my interview protocol. Initially, to gain insight into students’ responses to the expressive writing prompt, I collected and coded expressive writing samples from GEOL 10113 students during the Fall of 2020. Prior to the beginning of the semester, I asked the students to complete a Trait Emotional Intelligence Questionnaire-Short Form (TEIQue-SF) to assign a numerical value to their approximate emotional intelligence levels. Next, I stratified students by quartiles into high (Q3), medium (Q2), and low (Q1) emotionally intelligent groups. Then, GEOL 10113 students were asked to participate in focus group interviews, and volunteers were subsequently grouped by their TEIQue-SF score into three focus groups relating to the high, medium, and low emotional intelligence bins. Finally, using the focus group interview data and the selected student’s responses from the initial expressive writing exercise, I found that all of the student responses showed consistent differences between the three focus groups. Highly emotionally intelligent students had more positive experiences with the expressive writing exercise while lower emotionally intelligent students tended to have neutral or negative experiences with the activity. Overall, these data suggest that the level of emotional intelligence relates to how willing students were to convey their thoughts and emotions during the activity, which helped the higher emotionally intelligent students to have positive experiences with the writing exercise. The implications of my study are that while the efficacy of the expressive writing exercise is assumed, emotional intelligence is a confounding variable. Students must engage with it in some sort of authentic manner if they are to benefit from the exercise.

Solar energy is a significant contributor to the renewable energy mix. Many urban developments are making investments to install solar systems across feasible areas. The allocation of solar systems relies on the land’s geography and the amount of solar radiation received. The purpose of this study is to apply to determine the best sites for solar installations in urban areas. Using the TCU area of Fort Worth, Texas as a case study, this study will use ESRI’s ArcMap and ArcGIS Pro to estimate the solar power potential of different residential rooftops. The results will be useful in showing what households are most suitable for solar installation based on their expected energy yield.

Rapid industrialization and global population growth have increased the number of people living in urban areas worldwide. Developing countries, have seen tremendous increases in their industries over the past decades, which generated both positives and negative effects on their people, environment, and economy. One of the negative impacts of industrialization is industrial pollution and the increase in the number of pollutants released into the environment_ in this case, heavy metals. Heavy metal contamination is an alarming problem that many Developing countries are becoming aware of and trying to address. Heavy metal direct or indirect consumption may result in several health effects in the body, including damage and alteration of normal functioning of organs such as the brain, kidney, lungs, liver, and blood, which later result into acute or chronic diseases. This case study will look at heavy metal contamination cases in Rwanda in different drinking water sources. The focus of this case study will be on some common heavy metals released from industrial waste: Lead, Manganese, Iron, Cadmium, Zinc, and Chromium.

Teachers have experimented with the idea of virtual learning and its’ effects on student achievement. Due to the coronavirus pandemic, many schools and universities transitioned from traditional classroom-focused learning to asynchronous online learning. Asynchronous online learning is a type of instruction where online learning is not happening at the same time or place. Consequently, TCU made the abrupt transition in the Spring of 2021 to fully online asynchronous courses. To understand the magnitude of how remote learning can effect students’ academic success, my research project looks at what factors, including remote learning, can predict final grade utilizing GEOL 10113 student performance data and survey data from the spring semester of 2020 surveys. To investigate the impact of online learning, I tested several linear models to determine what confounders have a significant role in predicting students’ success in online and in remote learning. These models investigated which factors, ranging from demographic information to GPA, are significant predictors of both final grade and remote grade. I started the linear model selection process by testing a complex linear model, which had all the possible factors including interactions that can impact final grade or remote grade from the surveys. Once I knew which factors were significant from the complex model, I eliminated non-significant variables and created new models, comparing each model by their AIC values until I found the best-fit linear model for final grade and remote grade. AIC is a measurement of how well a linear model fits and the lower the AIC value the better fit the linear model has. After testing each linear model: GPA, students’ lecture section, remote grade, and exam average were significant to final grade. These models suggest that while remote grade is a significant predictor of final grade, no variable measured in this study is significant enough to impact remote grade. Differing from previous research, my results showed that there were no gaps in achievement amongst gender and underrepresented minority students. Although statistically no variable significantly impacted remote grade, there are trends amongst demographic variables and remote grade, suggesting some potential relationships that could be explored in future studies.

The reusing, recycling, and reduction of waste streams is seen as a viable sustainability strategy. One major waste stream is coffee grounds with about 11.5 million kilograms being generated per day in America of which 90% is landfilled. This waste stream can be repurposed into usable carbon-based materials to address issues of climate, pollution, or engineering applications. For my research, I am converting spent (used) coffee grounds into biochars, a type of carbon-based material, with different charring (burning) temperature to measure the removal of lead (Pb2+) from contaminated water. The charring temperature was changed in order to determine the optimal charring temperature for water treatment. This presentation will go into the maximum amount of lead the biochars can remove, how fast the biochars can remove the lead and the properties of biochars that allow for such removal. Further results, methodology, and modeling applications will be discussed in the presentation.

Anti-inflammatory drugs such as ibuprofen and triclosan are widely used and available in many pharmaceutical and personal care products (PPCP’s). The concentrations of these drugs are increasing in public surface and groundwaters and is often linked to negative impacts on aquatic life. These impacts are due to the fact that PPCP’s bypass water treatment facilities since they are not typically regulated and water treatment methods at the facilities are not designed to remove them. My research focuses on removing PPCP’s using reactive environmental sorbents like nanocrystalline ferrihydrite. Specifically, I examined the interaction of two widely used PPCP’s (Ibuprofen and Triclosan) with nanocrystalline ferrihydrite of varying particle size (<125, 125-250, >250). Results thus far show that when Ibuprofen interacts with nanocrystalline ferrihydrite at pH 4.3-4.8; 28.29% was removed when the particle size was less than 125 microns; 45.89% was removed when the particle size was 125-250 microns; and 49.92% was removed when the particle size was greater than 250. While for Triclosan 40.55%, 54.7%, 23.80% was removed by nanocrystalline ferrihydrite with size <125, 125-250, >250 respectively. My presentation will further cover surface properties of nanocrystalline ferrihydrite controlling the sorption of ibuprofen and triclosan.

The changing climate as well as the cycling of nutrients and contaminants throughout our planet is heavily influenced by interactions involving plant biomass. For example, interactions of plant biomass with soil biota (specifically fungi)regulates climate and pollution by controlling 1) the quantity of CO2 released from the respiration of organic matter and 2) the movement of pollutants on land and in water. This study focused on 1) investigating fungal colonization of coffee grounds, as a model for understanding the fungi-plant biomass interactions in soils, and 2) studying how fungal colonization changes in the physical and chemical properties of coffee grounds after molding them for 0,3,4,5 and 7 months. The objectives of the next phase of this research will be to examine how the fungi-induced changes in physical and chemical properties of coffee grounds impact 1)carbon sequestering potential (i.e. ease of respiration to CO2) of the coffee grounds and 2) the capacity of the coffee grounds to bind Gentian violet dye (as a model for organic/cationic pollutant).

Synthetic nanomaterials continue to revolutionize how we do things industrially, medically and domestically. As we continue to utilize these materials, the inevitability of them entering the environment and the need to understand the associated consequences rises to the forefront. My research focuses on understanding the chemo-dynamics of interactions between polyamidoamine (PAMAM)-based nanomaterials (most commonly in the biomedical field through drug and gene delivery) and reactive minerals in the environment. Specifically, this presentation will cover the size-dependent binding (and debinding) dynamics of carboxyl-terminated PAMAMs (G-COOH) onto (and from) ferrihydrite (FFH), a form of naturally-occurring iron oxide mineral. Early results suggest that at pH 5, the smaller G1.5-COOH PAMAM binds to (and debinds from) FFH in higher quantities but at much slower rates that the larger G3.5-COOH PAMAM. The higher quantities of G1.5-COOH PAMAM being bound to (or debound) from FFH is attributable to its smaller size - facilitating access to internal micropore space in FFH that are inaccessible by the larger G3.5-COOH PAMAM. Difference in the accessibility of internal FFH micropore space by the different sized PAMAMs would also explain observed trends in their rates of binding and debinding. In future research, I will be targeting the confirmation of early results and the expansion of my study to include G-COOH PAMAMs larger than G3.5-COOH.

Nitrate contamination of groundwater has been a growing problem in Texas and California from increased food demands, requiring growing agricultural inputs of synthetic fertilizer and manure. Pyrolysis of pistachio agro-waste is a promising method for reducing waste products and engineering biochar with the capacity to support zerovalent iron impregnation (ZVI). This study examined the efficiency of pistachio biochar for nitrate (NO₃-N) removal in water with and without ZVI. Pistachio biochar was functionalized through varied temperature pyrolysis (400-600℃) over three heating durations (0 min, 5 min, 10 min). Biochar samples from both 400°C and 600℃ pyrolysis were tested with and without ZVI impregnation over a 5 day period in a 20 ppm solution of NO₃-N. The biochar-nitrate solutions were recorded in intervals (1 hr, 3 hr, 7 hr, 24 hr, 68 hr, 96 hr, 120 hr) and Ultraviolet-Visible Spectroscopy was utilized to measure NO₃-N absorbance of samples at 400nm. The experimental data show that pistachio biochar with and without ZVI decreased nitrate levels from water; presenting a potential low-cost and sustainable option for repurposing agro-waste for water remediation.

Rivers are an essential part of any urban or rural landscape, providing drinking water, transportation, and recreational opportunities for local residents. However, with the continuous growth and development of urban areas like Fort Worth and Dallas, flooding poses a significant risk to human life and property. This increased development creates a need for careful monitoring and forecasting of river conditions and flood probabilities. This study explores the associated historical river data for USGS Gauges on the Trinity River in Tarrant and Dallas Counties. This data, along with topographic information and land use surveys, are used to project the possible impacts of flooding scenarios. These possible impacts include damage to property, critical infrastructure, and threats to human life. This data can then be interpreted spatially to effectively inform the public and public officials of risks and monetary costs associated with future flooding events.

This project will consist of designing an AI application. The application will use a deep learning algorithm able to take attendance of the class as students are joining the classroom. I will further expand the patent to recognize the individual students and measure their temperature. Furthermore, the system will classify different emotions during the lecture and give helpful feedback to the professors. This tool will assist with time management, as professors spend several minutes to take attendance, and it will act as an extra tool for the prevention of spreading COVID-19 and any new virus. The patent will further provide useful feedback for the improvement of lectures through emotion detection. An external camera will be used hand in hand with the Open-CV package in python that will allow us to detect the students and identify them. The students' temperature will be measured by an infrared forehead thermometer and welcome them in the class. The algorithm will be using cascade classifiers, and transfer learning. Data for the training process of the algorithm will be collected from volunteering TCU student subjects.

The process of successful skin healing from a wound involves different combinations of interactions. Moreover, by clearly understanding this process, we can provide and determine the appropriate amount of medicine to give to patients with varying types of wounds. Thus, this can improve the healing process of patients. In this research, we use the ADI method to solve a partial differential equation that models the wound healing process. Moreover, we try to explore the relationship between parameters in the model for different patients. Wound images are used as our dataset experiment. To segment the image's wound, we implement U-Net, a deep learning-based model, as our model for this segmentation problem. We believe the combination of ADI and Deep Learning helps us understand the process of wound healing.

Markov chains are stochastic models characterized by the probability of future states depending solely on one's current state. Google's page ranking system, financial phenomena such as stock market crashes, and algorithms to predict a company's projected sales are a glimpse into the array of applications for Markov models. In this research, we analyzed the board game "The Settlers of Catan" using transition matrices. Transition matrices are composed of the current states which represent each row i and the proceeding states across the columns j with the entry (i,j) containing the probability the current state i will transition to the state j. Using these transition matrices, we delved into addressing the question of which starting positions are optimal. Furthermore, we worked on determining optimality in conjunction with a player's gameplay strategy. After building a simulation of the game in python, we tested the results of our theoretical research against the mock run throughs to observe how well our model prevailed under the limitations of time (number of turns before winner is reached).

In the field of Riemannian geometry, the condition on the Riemannian metric so that a manifold has positive scalar curvature (PSC) is important for a number of reasons. Many famous researchers have contributed gradually to this area of geometry, and in this project, we study more about PSC metrics on such manifolds. Specifically, we refine and provide some details to the proof of Gromov and Lawson that the connected sum of 2 n-dimensional manifolds will admit a PSC metric, provided each of the manifolds has a metric with the same condition. We then derive some useful formulas related to the Riemann curvature tensor, the Ricci tensor, and the scalar curvature in many different scenarios. We compute the quantities for a manifold equipped with an orthonormal frame and its dual coframe, namely the connection one-form and the curvature two-form. Then, we observe the change in the structure functions, defined as a function that determines the Lie derivative of the orthonormal frame, under a nearly conformal change of the said frame. The aim of these calculations is that, by expressing the scalar curvature of a manifold M entirely in terms of the structure functions, we can determine a condition on the conformal factor so that when dividing the tangent bundle of M into two sub-bundles, then the scalar curvature restricted to one sub-bundle will “dominate” that of the other one, so that if we know the scalar curvature of the former sub-bundle is positive, we c

Impact of the COVID-19 pandemic on diet and health-related behaviors of adults

Background:
Study objectives were to describe how diet and health habits changed and identify factors impacting diet and health behaviors during the pandemic.

Methods:
An electronic, anonymous survey was developed and distributed via local social media and through a community food-bank following IRB approval. Data were coded into and analyzed for frequencies and correlations using SPSS.

Results:
Participants (n=80) were 97% (n=77) female and 41.37+/-11.7 years. Participants receiving food assistance primarily accessed community food/mobile pantries (22%, n=17). Participants (54%, n=43) agreed that, “I was healthier before the pandemic”, while 15% (n=12) disagreed. Participants (52%, n=42) reported 13.2+/-6.8 pounds unwanted, pandemic weight gain, while 22.5% (n=18) reported 14.1+/-13.9 pounds desired, weight loss. Among participants earning <$50,000/year, 89.5% (n=17) reported inability to afford healthy food, while 2.6% (n=1) earning >$150,000/year reported inability to afford healthy food. Inability to afford healthy food correlated with BMI (ρ=.40, p<.01). Income negatively correlated with pandemic weight gain (ρ=-0.31, p<0.05) and ability to afford healthy foods (ρ=-.73, p<0.01). Participants reported increased pandemic snacking (61.25%, n=49) and alcohol consumption (37.5%, n=30). Higher pandemic stress levels correlated with increased pandemic alcohol and snack consumption, (ρ=.30, p<.01) and (ρ=.44, p<.01), respectively.

Conclusion:
Changes in health perceptions and weight were common. BMI and income impacted ability to afford healthy foods. Increased stress levels were significantly associated with increased alcohol intake and snacking, although weight changes were not associated with alcohol or snacking. This research provides information about pandemic dietary and health behavior changes and how impacts differ based upon income level.

Determination of Caffeine Use and its Effects on University Students
Jason Balters, Senior-Coordinated Program in Dietetics; Jordan Pitts, Senior-Coordinated Program in Dietetics; Anne VanBeber, PhD, RD, LD, FAND, CCMS; Kelly Fisher, DCN, RD, LD, CSP; Lyn Dart, RD, LD, PhD - Department of Nutritional Sciences
Background
Many studies have observed caffeine intake of university students. Investigators have found that the majority of consumers who over consume caffeine were not aware of the FDA recommended limit of 400 mg caffeine/day. Purposes of this research were to determine the level of caffeine consumption among students attending Texas Christian University (TCU) and the perceived effects that users experienced related to sleep, stress, and withdrawal symptoms. A desired outcome of this research was to clarify confounding values of average caffeine consumption in the university population that varied from 124 mg/day to 228 mg/day in the existing literature.

Methods
A survey questionnaire was created to assess the quantity, modality and situational scenarios of caffeine consumption, as well as caffeine’s perceived effect on sleep, energy and stress. Caffeine withdrawal effects felt by students were also ascertained in the survey. The survey was administered via Survey Monkey® and distributed via the TCU email system to approximately 1000 students. Basic descriptive and frequency statistics were calculated using the Survey Monkey® software.

Results
The 217 respondents were 76.5% female with 94% between the ages of 18-25 years old and 94.0% current undergraduate students. Of the total respondents, 70.1% consumed 1-2 caffeinated beverages per day, 66.5% used caffeine to help study, and 67.5% used caffeine to increase energy. Negative perceptions of caffeine’s impact on sleep were reported by 30%; while 18.5% reported a negative impact on stress, and 73.7% reported withdrawal symptoms.

Conclusions
The consumption of 95-190 mg caffeine/day by the majority (54.9%) of study participants was consistent with reported scientific literature. While only 1.8% of those surveyed consumed more than the FDA recommendation of 400 mg caffeine/day, reasons for this are unknown. Additionally, of the 70% who stated they experienced withdrawal symptoms, it is uncertain if these symptoms were indeed caused from the caffeine withdrawal or from another unrelated cause. It is advised that the research survey be revised to include questions that more acutely seek information sought in the stated purpose of the study. Further research regarding caffeine consumption by consumers is also warranted to better assist registered dietitian nutritionists and other health professionals when assessing diet and lifestyle habits of patients.

Background:
Studies in the general population assessing knowledge/attitudes regarding a plant-based diet found that the majority of participants were reluctant to follow the diet. Specific concerns included perceived lack of satiety, low protein content, and undesirable taste. However, there is lack of research in the college-age population. The purpose of this study was to determine university students' general knowledge/attitudes of a whole-foods, plant-based diet.

Study Design:
After providing informed consent, participants (N=209) completed a 28-question online research survey via Survey Monkey®. Participant demographics, health status and eating habits were gathered at the beginning of the survey. The remaining questions assessed participant knowledge and attitudes pertaining to a whole-foods, plant-based diet. Analysis using Pearson correlation coefficients was performed to assess associations between knowledge and attitude towards a whole-foods, plant-based diet in concordance with participants’ health status and eating habits (SPSS, p ≤ 0.05).

Results:
Males were more likely to disagree that following a plant-based diet is beneficial to their health (p ≤ 0.01). Male students were also more likely to believe that a whole-foods, plant-based diet would never satisfy their hunger (p ≤ 0.01), decrease their energy levels (p ≤ 0.05); males were also less likely to understand the meaning of a whole-foods, plant-based diet (p ≤ 0.01). Similarly, students who had never taken a nutrition course were more likely to disagree that a whole-foods, plant-based diet is beneficial to their health (p ≤ 0.01).

Conclusions:
Respondents who had negative attitudes toward a whole-foods, plant-based diet tended to lack knowledge/understanding of the diet. Providing nutrition education about the whole-foods, plant-based diet by registered dietitian nutritionists could result in improved health outcomes among this population.

Background
According to the Environmental Protection Agency (EPA), 2.6 million tons of food waste was composted in 2017, while 30.6 million tons of food waste went to landfills. EPA’s Food Recovery Hierarchy prioritizes actions to prevent and divert wasted food including source reduction and feeding hungry people and animals. The objectives of the study were to 1) describe factors influencing participation in a composting pilot and 2) determine the impact on food and overall waste.

Methods
A 36-question electronic anonymous survey was developed and distributed to 1,054 participants in the composting pilot program by the City of Fort Worth via email following IRB approval. Data were coded into and analyzed using IBM SPSS version 25. Statistical significance was p<0.05.

Results
Approximately 40% of pilot program participants completed the survey (n=418/1054). Participants’ mean age was 45.5+/-14.9 years. Almost 90% (n=375) of participants cited wanting to save the Earth as a reason for program participation. Over 54% (n=223) reported completely filling the five-gallon compost bucket with food waste within 1-2 weeks, on average. Participants (45.7%, n=191) reported frequently throwing away food based upon expiration, best buy, or sell-by dates. Participants reported other waste-reducing behaviors including use of reusable glass and plastic containers and water bottles; 91.2% (n=381), 89% (n=372), 86.1% (n=360), respectively.

Discussion
The majority of participants wanted to save the Earth and were partaking in other waste reduction behaviors. However, food waste remained high among households as evidenced by frequent filling of five-gallon compost buckets and reports of throwing away food based upon dating systems. While it is nearly impossible to eliminate all food waste, landfills need a relief system, and city composting programs or at-home composting could be solutions for many households. Citizens would benefit from additional education regarding food dating systems and food waste reduction methods.

Background: Low-carbohydrate diets are increasingly popular in the US. Despite having vital roles in the body, negative associations are established toward carbohydrates in the media, indicating that reduced consumption leads to weight loss, decreased gastrointestinal conditions and improved brain function and energy status.
Objective: The objective of this study is to determine college students’ perceptions of carbohydrates on health and contributing factors to their perceptions. It is hypothesized that due to recent media coverage and health trends, carbohydrates have gained a negative connotation in regards to health, despite the lack of research supporting these ideas.
Methods: An online survey was developed to investigate participants’ perceptions and knowledge of carbohydrates. Researchers recruited Texas Christian University students through social media and email. Data was analyzed using Excel.
Results: Among study participants (N=127), only 3% (n=4) were currently following a low-carbohydrate diet and 45% percent (n=54) had previously followed one. Of the participants who limited their carbohydrate intake, the main motivators were weight loss (53%, n=51) and health (19%, n=18); they reported low energy levels (51%, n=47), mood swings (20%, n=18), and trouble concentrating (18%, n=17). The most commonly reported sources of nutrition education were internet searches (74%, n=90), social media/blogs (63%, n=77), and friends/family (63%, n=77). Approximately 45% (n=55) were in a health-related major and 47% (n=57) have taken a college nutrition course. The majority were able to correctly identify the roles that carbohydrates play in the body, including energy source (98%, n=117) and brain function (69%, n=82).
Conclusions: A small percentage of participants are following a low-carbohydrate diet, likely due to their awareness of the roles that carbohydrates play in the body, as well as negative symptoms reported by those who previously followed the diet. Many had taken a nutrition class, which indicates that nutrition education influences dietary decisions.

Background. The prevalence of nutrition-related diseases has created a need for increased nutrition education in medical school curricula. Due to the lack of nutrition education for physicians, RD/RDNs bring value to interprofessional teams. Incorporating nutrition education into medical school programs is likely to increase physicians’ ability to provide nutrition advice and promote healthy lifestyles.

Objective. The purpose of this study was two-fold: 1) to determine Registered Dietitians’ (RD/RDNs) perceptions of physicians’ nutrition knowledge; and 2) to determine the interprofessional practice of physicians.

Methods. A 27-question electronic survey was developed and distributed to a Survey Monkey link through email, social media and word-of-mouth communication following IRB approval. Inclusion criteria includes RD/RDNs credentialed with the Commission of Dietetic Registration over the age of 18 years. The investigators used Excel for Mac, Version 16.42 for data analysis. Statistical significance was set at p<0.05.

Results. The study surveys RD/RDNs in Texas. Participants (n=64) were 38.3+/-11.0 years of age and 100% (n=64) female. Over 95% (n=61) of participants reported feeling comfortable interacting with or providing nutrition information to physicians in a healthcare team setting. Approximately 30% (n=19) of participants rated physicians at expected to well above expected on establishing collaborative relationships with RDs/RDNs. Approximately 78% (n=50) of participants reported that they disagreed/strongly disagreed that physicians are well-prepared to educate their patients in nutrition. Over 43% (n=28) agreed/strongly agreed that physicians discuss nutrition with patients/clients when appropriate.

Conclusions. RD/RDNs reported being comfortable interacting with physicians. RD/RDNs did not report that physicians were well-prepared to educate patients about nutrition or discuss nutrition with patients when appropriate. The majority of participants reported that physicians meet or exceed expectations for establishing collaborative relationships. Thus, interprofessional teams are valuable in achieving positive patient outcomes.

Keywords: Registered Dietitian, RD, RDN, Doctor, MD, DO, Medical School, Nutrition Education, Interprofessional Education

During the first 100 million years after the Big Bang the universe was dark, and possibly full of terrors. Sometime during the first 500 million years, these cosmic dark ages ended with the ignition of the first stars. The first stars, which we call these Population III, contained only the hydrogen and helium formed in the Big Bang. These stars are interesting because they are thought to have started a domino effect of enrichment of elements heavier than helium through the cosmos. These ”heavier” elements formed in the core of stars are referred to in astronomy as ”metals”. Without ”metals” the gas out of which Population III stars form cools inefficiently, producing stars with masses as high as a few 1000 times the mass of our Sun. However, while we know that Population III stars are massive, we do not know exactly how massive as they are too faint for detection by all current and upcoming astronomy observatories. The work presented on this poster will explore one possible alternate avenue to answer the question: how massive were the first stars? As a result of their extreme masses, the most massive Population III stars will collapse directly into black holes with masses of a few hundred to a few thousand times the mass of our sun. A billion years after the Big Bang, as the era of the Population III stars ends, astronomers have observed the distribution of supermassive black holes (a million to a billion times the mass of the sun) in galaxies. Our work traces the evolution of the direct collapse black holes, formed from Population III stars, to determine whether the distribution of the masses of Population III stars left an imprint on the distribution of supermassive black holes, 500 million years later. The goal of this work is to randomly populate different potential distributions of Population III masses to compare the varying distributions of direct collapse black holes at different times. The result of this will eventually provide predictions for the dependence of the distribution of supermassive black holes, a billion years after the Big Bang, on the distribution of the masses of Population III stars.

Dark Matter (DM) is hypothesized to be an exotic particle that is invisible to human observation. But thankfully, its existence is proven through its gravitational interaction with luminous matter (such as stars and galaxies), and it is responsible for the formation of the humongous structures across our universe. The leading interpretation of DM is what we call Cold Dark Matter (CDM), where the DM particles have relatively low velocities and low energies. This causes structures to form quite quickly and easily in the early universe. While CDM can explain many observed properties of the universe, it is not without its flaws (specifically on the scale of low-mass dwarf galaxies). The hypothesis of Warm Dark Matter (WDM) poses a viable solution to the shortcomings of CDM. In WDM, the DM particles are of higher energy and have higher velocities. This would cause the formation of the first gravitationally bound structures in the Universe to be delayed when compared to CDM. Using a model to approximate varying temperatures of DM, we compare the rates and characteristics of early structure formation for the current CDM hypothesis, and that of many other types/temperatures of WDM. We expect that the differences between CDM and WDM will be most apparent during the first billion years after the Big Bang, just as the first stars in the Universe ignite. These results may be indicative of the true nature of dark matter, and finally bring our understanding into the light.

In this research we developed biocompatible Graphene Quantum Dots (GQDs) capable of emitting light in the infrared part of the light spectrum. Using the bottom up and top down approaches, we synthesized near-infrared light-emitting GQDs to be used for further cell studies as imaging and drug delivery agents for cancer detection and treatment.
From our bottom up approach, using a one-step hydrothermal reaction using a microwave and oven, the GQDs derived from the Glucose and Liquid ammonia mixture and those from from the L-glutamic acid showed near-infrared emission. And from our top down approach, using a UV based photolytic reaction, the GQDs derived from the mixture of urea, citric acid and hydrogen peroxide also showed near-infrared emission.

Type Ia Supernovae (SNe Ia) are used as measuring sticks in the structure of the Universe. These catastrophic explosions occur when two stars collide, but it’s unknown what kind of stars are combined to produce a SN Ia. Target 1 in our study is an unusual SN Ia; while a standard SN Ia would grow much dimmer after 300 days (late-time), this one remains bright. This is due to delayed interaction between the material ejected from the SN explosion colliding with the material in the surrounding region, causing light-curves to stagnate in late-time and be brighter than standard SNe Ia. It’s unknown if SNe like Target 1 are rare, but their properties would greatly aid in mapping the Universe. Therefore, we searched the public data from the Zwicky Transient Facility for more these types of SNe. We obtained 40 light-curves that are representative of the intrinsic SN Ia distribution in the nearby universe and found two instances of Target 1-like SNe.

Photothermal Therapy (PTT) provides a promising new method of therapy for various medical conditions, including cancer, using infrared wavelengths. In my project, the photothermal effect of various nanomaterials—including Reduced Graphene Oxide, gold nanospheres and nanorods, and Copper Sulfide (CuS) nanoparticles—is characterized by irradiation of the aqueous materials with near-infrared radiation. These materials were then irradiated in live cell cultures to characterize their potential use as a treatment candidate.