Science Project Summaries

Marine debris is litter, including plastic, metal, rubber, and glass, that has been intentionally or unintentionally abandoned in the environment. My research is focused on studying the source locations of marine debris and the sites where it is deposited to understand how marine debris moves through tropical ecosystems in southwest Puerto Rico. Marine debris often accumulates in large quantities along coastlines, especially on beaches, where it can become buried, fragmented, and/or degraded by exposure to sunlight and seawater. To characterize the types of marine debris in the environment, we use Fourier-transform infrared (FTIR) spectroscopy to identify different polymers in marine debris we have collected. We will then be able to identify commonly discarded polymers from the FTIR spectra. This study will: (1) identify items and types of polymers discarded in the environment; (2) focus on debris collected during the wet season in 2023 to compare to previous dry season collections and previous years; and (3) improve our assessment of spectra from degraded plastics. Together this knowledge can be applied to determine the residence time of different marine debris in the environment and to develop effective management strategies.

My laboratory has an FTIR instrument, with marine debris collected over multiple years and seasons, including debris newly collected during the 2023 wet season. The next step is to analyze the samples and compare to an existing library of polymer spectra. The Match student research assistant will learn and assist with the following techniques to assist with this study: (1) prepare the samples; (2) learn how to use the FTIR; (3) analyze samples; and (4) compare sample spectra to a polymer library. The student will work closely with the faculty mentor at each step to learn techniques and protocols. The student will also be encouraged to attend weekly lab group meetings and will have the opportunity to present their findings to our lab group.

Ferritin is the iron storage protein responsible for iron homeostasis in humans. It is composed of an inorganic mineral core of ferrihydrite, about 8 nm in diameter, encapsulated within an organic hetero-polymeric shell of amino acid chains of two types, H and L. The H/L chain ratio varies depending on the organ from which the ferritin is extracted and seems to influence the kinetics of iron deposition within the ferritin core. Ferritin malfunction can lead to serious iron-related diseases and has been implicated in heart disease and neurodegeneration. The ferritin shell, or apo-ferritin, forms a nanotemplate within which iron is biomineralized to form the holo-ferritin. The apo-ferritin nanotemplate has also been used for the synthesis of a variety of iron based magnetic nanoparticles, other than ferrihydrite, which have important applications to nanomedicine and nano-biotechnology.

The physical properties of holo- and apo-ferritin heteropolymers engineered in E. coli bacteria and reconstituted at various iron loading levels and phosphate content are being investigated by Atomic Force Microscopy (AFM) and Magnetic Force Microscopy (MFM) to delineate differences in protein surface stiffness/elasticity and core magnetic moment size as a function of H/L ratio and iron loading. Transmission Electron Microscopy (TEM) measurements are also conducted to determine the iron core size and size distribution.

Our studies aim to a better understanding of the physiological function of ferritin heteropolymers in health and disease and the design of apo-ferritin nano templates to produce nanoparticles with a narrow size distribution, for applications to nanotechnology and bio-nanomedicine.

The student will work at the low dimensional materials lab housed within the department of Physics. They will be trained to prepare biological samples for AFM or MFM characterization by dispersing highly diluted ferritin solutions onto silicon oxide substrates for image visualization. They will work alongside more advanced undergraduate researchers in the group to become proficient in the operation of the AFM/MFM instrument, obtain a series of topographic images of the samples and proceed to measure the Young’s Modulus, degree of deformation and magnetization of the ferritin molecules. The student will also be introduced to the use of the Transmission Electron Microscope housed within the imaging center of Villanova University to investigate the size distribution of the iron ferritin core by obtaining TEM micrographs of the ferritin samples. They will then use the ImageJ software package for analysis.

Graphene – a single layer of carbon atoms arranged into a honeycomb lattice – is the most efficient carrier of electric current yet discovered. It has been the center of 15 years of intense research and industrial interest since its discovery. Placing other layers around graphene can further enhance its conductivity and lead to quantum behavior of macroscopic devices. This project aims to quantify how these quantum states are effected by the presence of auxiliary layers of other 2D materials. We use the amplitude of resistance oscillations in graphene in a changing magnetic field as a metric of the quality of graphene devices. At high magnetic fields, we also observe the quantum Hall effect - a regime where the energy levels in the graphene are quantized much like atomic orbitals. Characterizing the gap between these energy levels as a function of temperature also provides a metric for the quality of graphene devices.

All measured structures are built from scratch by the student. Students will begin by exfoliating bulk graphite and hexagonal boron nitride crystals to obtain single and few-layer flakes. The thickness and quality of these flakes will be studied using an atomic force microscope (AFM) then stacked to form heterostructures (layered stacks of different materials). Students will learn about the cleanroom nanofabrication techniques used contact these heterostructures and then begin measurements at low temperatures and strong magnetic fields in the lab of the mentor.

The main objective of this project to apply passive sampling method to measure air pollution. We will use Ogawa passive samplers to measure air pollution around Villanova Campus. Passive sampling methods have major advantage over other techniques because these are inexpensive, simple, and they don't require electricity and pump to draw air. Passive sampling can be used to map air pollution in the region. Two major criteria air pollutants - nitrogen dioxide and ground level ozone will be measured in this project. Both these gases are regulated by the United States Environmental Protection Agency. These gases besides being the pollutant itself also have other environmental effects.

Student will be responsible for preparing the reagents and passive samplers, deploying the passive samplers, and analyzing the collected samples.

Lifestyles of over-consumption and a throw-away culture are some of the biggest contributors to the climate crisis.  A big part of over-consumption stems from “fast fashion,” the consumption and discarding of cheaply made garments on an enormous scale. This problem is so massive it can be seen from space.

This culture of consumption has been glorified in internet videos referred to as “hauls,” where creators show off large quantities of recently purchased goods. As a counterpoint, some creators post videos of “thrift hauls” showcasing secondhand goods to promote environmental awareness. However, some critics argue that despite showcasing secondhand goods these videos still promote a culture of over-consumption. While the human-computer-interaction (HCI) community has studied other internet video phenomena, to date no work has been done on “hauls”. 

This project will consist of three parts:

a. Data Collection:  Using web scraping libraries we will collect comments from two sets of YouTube videos, those under #sheinhaul (Shein is a notorious fast fashion company) and those under #thrifthaul.  
b. Qualitative Analysis: We will manually review a subset of the comments using inductive coding to understand how viewers conceptualize both consumption and sustainability.  
c. Automated Analysis: Given the enormous amount of available data we will make use of basic language processing in Python to analyze and visualize themes in the comments and make comparisons across the two types of videos. 

We will additionally source popular and academic articles related to the haul phenomenon as well as the growth of the secondhand clothing market and its connection to technology.  

Using the background literature and comment analysis, we will apply an HCI lens, to understanding whether thrift haul videos constitute an effective use of technology for promoting environmental action and will produce guidelines for effectively using online platforms to promote responsible consumption.
 

The student research assistant will have several responsibilities:

1. Data Collection: The student will work with Python web scraping libraries to set up a script to scrape comments from videos under #sheinhaul and #thrifthaul. They will also work on post processing to clean the data for analysis.

2. Qualitative Analysis: The student will learn about inductive textual analysis methods and will perform analysis on a subset of the comment data to uncover themes related to consumption and environmental sustainability.

3. Quantitative Analysis: The student will use Python to perform basic language processing and visualization techniques, including, but not limited to, word count, LDA, and dictionary-based methods. The student will program and run these algorithms and learn how to interpret the results. This will include becoming aware of the limitations of various techniques and weighting results appropriately.

The student will also be responsible for reading both popular and academic articles to gain a background understanding of the underlying problems and phenomena being studied.

Finally, the student will be responsible for writing a post-project report in the style of an academic paper by summarizing the literature review, data collection, processing methodology, and findings.

X-ray Binaries (XRBs) are binary star systems in a close orbit containing a regular star and a companion that is a compact object – a black hole or neutron star. These exotic systems are given their name because they are very bright at X-ray frequencies. The basic picture of an XRB consists of a compact object surrounded by hot plasma that orbits and falls onto the compact object in the shape of a disk, called an accretion disk. At the innermost regions around the compact object is a coronal plasma producing X-ray emission at even higher frequencies, sometimes close to the gamma-ray regime. In addition to the accretion disk and corona, many XRBs also contain collimated jets that emit at radio frequencies. Each component of the XRB environment is dynamic: the inner edge of the accretion disk contracts and recedes, the corona can grow or shrink, and the radio-emitting jet may appear and disappear. The evolution of the emission over time offers a particularly powerful window into the structure of the accretion environment and the relationship between different emitting regions in the system. Astrophysicists can use techniques from time series analysis to connect the variations in brightness over time to the dominant physical mechanisms at play.

This project will focus on analyzing X-ray data of a black hole XRB from the Rossi X-ray Timing Explorer using nonlinear time series analysis and machine learning. The results will produce snapshots of the XRB at different points in time to explore how the brightness variability of the source connects to the geometry and changes in the accretion environment. The project will also involve creative approaches to visualizing or representing the X-ray data and results, such as through sonification.

The student will meet with the mentors on a weekly basis, and as needed, during which time the student and mentors will discuss the project and how to proceed with each step of the analysis. The student will perform the analysis and data visualization work using a coding environment that is suitable for a personal laptop. At the end of each week, the student will submit a 1-page summary to the mentors detailing the accomplishments and challenges that occurred during the week and goals for the following week. The student will keep the 1-page summaries as a work log and compile it into a final report at the end of the semester. The only prerequisite for the student is an enthusiasm for astrophysics. Programming experience is not necessary but may be helpful, as will familiarity with physics concepts (but again, not required). Majors in subjects outside of physics or astronomy are welcome!

Language is at the core of human culture. Not only is it the primary system of communication used by humans, language is also a key component through which individuals transform personal identities, reinforce group memberships, and propagate ways of thinking, by adopting variations of language that are structured according to particular characteristics.

The goal of this project is to develop a descriptive study of written language evolution, analyzing structural changes over time in fiction and in society alike. This means the project aims at explaining "what" has changed in the language over time in different domains of fiction and society, as opposed to explain the "why" of that change.

The text datasets (corpora) will include collections of historical documents, literature, film and TV transcripts, news articles, and social media posts. The methods that will be used to describe language change include lexicon-based approaches, frequency and stylistic measures, semantics, and pre-trained natural language processing models. The final results will be obtained using statistical analysis, clustering and unsupervised learning techniques. The expected outcome is a description of the evolution of the linguistic structures over a predetermined period, and how this evolution compares across different domains (e.g. in fiction, historical and news). With these results, we will seek to answer questions such as "which structures are more prominently changed in fiction in comparison to news"?

The expected tasks to be executed in this project are:

a. Pre-processing the text data via tokenization, lemmatization, and removal of irrelevant tokens.
b. Designing and developing the features to measure the structural changes.
c. Obtaining the data by extracting the structural features.
d. Performing the data analysis and collect the results.

Writing a report describing the utilized methods, results and findings of the work.
 

The Match student will be responsible for designing and developing the code for the data processing and analysis as outlined in the description. The student will also work on a technical report describing the study.

When people consider pollinator conservation, they often think of actions that can be taken in rural agricultural systems. However, pollinators are equally important in cities where residents often rely on gardening as a supplemental source of food and income. This is particularly true in many African cities where urban gardens contribute to food security during the dry season when less food can be produced in rural areas. Although the impacts of urbanization on pollinator abundance are beginning to be understood in many parts of the world, very little research has been conducted on this topic in Africa. This study aims to understand the relationships between urbanization and bee abundance and diversity in the West African city of Cotonou. Bees were collected in community vegetable gardens along an urban-rural gradient in the Greater Cotonou Area. These specimens now need to be identified to species before we can conduct analyses to understand how urbanization is impacting bee communities in this part of the world.

The Match student research assistant would be focused on the identification of bee specimens. They would spend the first couple of weeks working with me to become familiar with bee anatomy and learning to use the dichotomous keys that are needed for identification. After that introductory period, their first task would be to key the specimens out to genus. Then, they would begin working on the species identifications, moving through one genus at a time. Periodic training and a high level of support would be provided throughout the semester given that bee identification can be quite challenging and requires specialized knowledge. Towards the end of the semester, the student would work on entering the specimen information into a database of the type that is used for museum collections and preparing a set of specimens for long-term storage at the International Institute of Tropical Agriculture in Benin. This would be a great position for a detail-oriented student with a strong interest in entomology who wants to begin to acquire some of the skills and knowledge needed for insect identification.

Traumatic brain injuries (TBI) are the leading cause of death among youth, with roughly 1.5 million people in the U.S. suffering from TBI annually. If one could accurately predict the outcome of a TBI patient’s future, families would be better prepared to focus on a patient’s steps to recovery and physicians could devise appropriate treatment strategies. This project uses supervised machine learning to predict mortality and functionality status scores (FSS) of patients affected by TBI. The FSS is a physiological function measure that evaluates patients in six domains: mental, sensory, communication, motor, feeding, and respiratory. Each domain is scored from 1 (normal) to 5 (extreme dysfunction). The total FSS ranges from 6 to 30.                                                                                                                                         

The dataset used for supervised learning contains anonymized data of 300 TBI patients with features derived from medical history and hospitalization details. Mutual information with the target variable is used to rank the clinical features of patients. Mathematical models are then trained on the top clinical features, validated by a medical expert. After model evaluation, the support vector machine (SVM) algorithm is used to predict mortality, and the random forest algorithm is used to predict FSS scores of TBI patients.

Student responsibilities include: 

1. Analyze the dataset to select features that best predict TBI.

2. Develop machine learning models using all or part of the dataset to predict TBI.

3. Extending the research to Artificial Intelligence models such as supervised/unsupervised neural network models to evaluate their feasibility for the problem.