2020 Multi Campus

Research Experiences for Undergraduates

(MC REU)

www.sites.psu.edu/mcreu2020

PROGRAM INFORMATION The Penn State MC REU program supports Penn State undergraduate students who conduct research with Penn State faculty. Students complete their proposed engineering research project in conjunction with two Penn State faculty members — one from the student’s home campus and one based at University Park. The objectives of the MC REU are to promote undergraduate students participating in research early in their academic program to broaden their education and increase their chances of entering graduate studies, and to promote mutual awareness and collaboration among faculty across the Commonwealth. The program it has served more than 400 students over six years. The 2020 MC REU includes 102 students from 12 unique campuses participating in an 8 to 10-week REU.

SYMPOSIUM Kickoff - Wed 7/29, 9:30 am ET - 11:30 am ET Zoom session 939 7225 1299, password MCREU2020 Program available asynchronously from 7/29 through 8/5

KEYNOTE SPEAKER Dr. Stephanie Danette Preston Associate Dean for Graduate Educational Equity, the Graduate School at Penn State Dr. Preston has administered, coordinated and evaluated comprehensive recruitment programs and retention activities which enhance the professional development of

underrepresented graduate students. In her current role, Dr. Preston leads the Big Ten Academic Alliance Summer Research Opportunities Program and the Ronald E. McNair Scholars Program. She has represented Penn State as a participant in programs such as the National Science Foundation's Alliances for Graduate Education and the Professoriate, designed to broaden participation in science technology, engineering and mathematics disciplines. Additionally, she has coordinated the Alfred P. Sloan Foundation Scholars Program. Dr. Preston completed her Ph.D. at Penn State in curriculum & instruction in science education. Her graduate research focused on the underrepresentation of ethnic minorities and women in STEM fields. She received her bachelor’s degree in biology and her master’s degree in curriculum & instruction (science education) at Xavier University of Louisiana. Dr.

Preston taught high school biology, chemistry, and human anatomy & physiology in New Orleans.

ABSTRACTS Kavan Adeshara Campus Affiliation: Penn State Abington Major: Computer Science Anticipated Graduation Date: Spring 2023 Mentors: Vinayak Elangovan (Penn State Abington), Amatur Rahman (Penn State University Park) Project Title: Face Recognition using Principal Component Analysis integrated with Delaunay Triangulation Face Recognition is most used for biometric user authentication that identifies a user based on his or her facial features. The system is in high demand, as it is used by many businesses and employed in many devices such as smartphones and surveillance cameras. However, one frequent problem that is still observed in this user-verification method is its accuracy rate. Numerous approaches and algorithms have been experimented to improve the stated flaw of the system. This research investigates one such algorithm that utilizes a combination of two different approaches. Using the concepts from Linear Algebra and computational geometry, the research examines the integration of Principal Component Analysis with Delaunay Triangulation; the method triangulates a set of face landmark points and obtains eigenfaces of the provided images. It compares the algorithm with traditional PCA and discusses the inclusion of different face landmark points to deliver an effective recognition rate for the designated algorithm.

Emaan Ali Campus: Penn State Harrisburg Major: Chemical Engineering Anticipated Graduation: May 2023 Mentors: Abu Asaduzzaman (Penn State Harrisburg), Jose Fuentes (Penn State University Park) Project Title: Oxidized Mercury Adsorption on Snow Surfaces in Polar Regions: A Computational Study Mercury (Hg) is a heavy metal that is present naturally in various chemical forms. It forms toxic methylmercury compounds and bioaccumulates in aquatic food chains, identifying it as an environmental pollutant of global concern. Depending on the mercury species involved, they can undergo long and short-range transport, atmospheric physical and chemical transformations and interact with Earth’s surfaces such as polar environments. In polar regions, upon being oxidized by ultraviolet light, these oxidized species can deposit onto snow surfaces, which act as reservoirs for these pollutants. As a result, the Artic seasonal snowpack is suspected to contribute to the contamination of aquatic life during snowmelt. However, the pathway by which mercury accumulates and the extent of atmospherically deposited mercury influencing the polar ecosystem through snow remains unknown. To better understand mercury deposition, we performed an atomic level investigation by mimicking an optimized structure of snow and observed the interaction of various oxidized mercury compounds with the snow surface. Density-functional theory was applied to analyze the strength at which the mercury compounds bind to the snow through their adsorption energies. It was observed that the compounds BrHgXO/BrHgOX (X representing a halogen) had the strongest interaction with snow. Improved understanding of the fate of mercury in the Artic environment will lead to advancements that can reduce exposure to mercury’s neurotoxic effects.

Yasmin Ali Campus Affiliation: Penn State University Park Major: Biomedical Engineering Anticipated Graduation Date: May 2021 Mentors: Scott Medina (Penn State University Park), Atip Lawanprasert (Penn State University Park) Project Title: Commensal Biocapsules as a Bacterial Delivery System to Reengineer the Microbiome Antibiotic resistance poses a major cause for concern in the medical treatment of bacterial infections, as it emerges through the accumulation of resistance genes in drug-exposed flora of the gastrointestinal (GI) tract. In particular, the GI pathogen C. difficile exemplifies a common hospital-acquired infection that colonizes the dysbiotic gut resulting from antibiotics. Treatment of this deadly pathogen requires both the removal of the microbe and the reestablishment of the

native, healthy microflora of the gut. To address these demands, this research presents a model for commensal-loaded biocapsules that not only possess an antimicrobial coating to clear pathogenic bacteria, but can also reflourish the environment with a defined consortium of encapsulated microorganisms known to support GI health. For Study 1, Comsol computational modeling is used to investigate the formation of the antimicrobial coating. In the formation of the particle, it is hypothesized that hyaluronic acid (HA) from the core reacts with polylysine (PLL) in solution to form the outer coating, termed the “corona.” The objective is to identify an optimal concentration range for HA corona growth with careful consideration of the solubility limitations of the biomaterial. Results of this model indicate that increasing HA concentration steepens the concentration gradient for HA diffusion and, accordingly, reaction with PLL. Moreover, preliminary data demonstrates that the biocapsule interior fosters a suitable environment for the growth of E. coli. With that in mind, Study 2 seeks to develop a model for the E. coli growth within the capsule using MATLAB software. This model is then expanded upon to model the expected growth kinetics of commensal microorganisms that could be loaded into the capsule. Results of Study 2 illustrate the expected growth patterns of commensals within the capsule prior to release into the human gut. Overall, this research will allow us to refine and improve the formulation of the biocapsules in preparation for preclinical studies.

SaiGanesh Asapu Campus Affiliation: Penn State Harrisburg Major: Mechanical Engineering Anticipated Graduation: May 2021 Mentors: Esfakur Rahman (Penn State Harrisburg), Daniel Cortes (Penn State University Park) Project Title: Topological optimization to reduce stress shielding The purpose of this research is to find and compile ways to reduce stress shielding effect cause due to mismatch in stiffness between the bone and the implant. The paper focuses on using several ways to reduce the material used, decreasing stiffness of the implant and using bio compatible materials to get the most optimum design for the implant used in total hip replacement surgeries. This could potentially save the amount of material used in manufacturing the implant, reducing the frequency of a revision surgery and reducing the stress shielding effect caused by the implant. Many implants right now induce stress shielding because of a relatively high stiffness including the material, so this research focuses on using a biocompatible material and a design that could lead to relatively less stress shielding. Currently this research is limited by theoretical data so most of the findings in the paper makes sense theoretically. A lot of experimental data has been collected to make sure the design is compatible in the real world. 3 different designs were created and analyzed using finite element method in ANSYS to see how the traditional implant compares to the other designs such as hollow or semi hollow stems, topological optimization was performed to get a model that saves 30percent of the material. Combination of materials were created using bio ceramics and polymers to get a composition that is bio compatible and is relatively less stiff than a traditional metal that are commonly used such as Titanium and Co-Cr alloy. It is found that the combination of materials provided a less dense material that can still withstand the loads of walking, standing up and other activities. Hollow and partially hollow implants still were able to withstand the loads without a significant deformation compared to a commonly used implant that is completely solid. It could also be noted that atleast 30 percent of the material could be saved based on the boundary conditions used for the loading in the implant. This work could provide a compiled list of things that induce stress shielding and proposed solutions that could minimize this issue, also this research compares several design models to choose from that could save material and induce less shielding after a total hip replacement surgery. Experimental combination of materials have also been proposed that are more biocompatible than other metal implants that are commonly used.

Austin Azar Campus Affiliation: Penn State Lehigh Valley Major: Civil Engineering Anticipated Graduation Date: May 2022 Mentors: Tracey Carbonetto (Penn State Lehigh Valley), Sez Atamturktur (Penn State University Park) Partner: Joseph Fantuzzi Project Title: The Pursuit of Non-Invasive Scaffolding Solutions for the Restoration of Lehigh Valley Heritage Sites: Number Two Machine Shop The focus of this research project is to establish non-invasive scaffolding solutions for the rehabilitation of heritage structures. This summer’s study specifically focuses on the Number Two Machine Shop as a case study application in the Bethlehem Steel Complex located in Northampton County, Pennsylvania. The saving of this historical site is crucial in preserving an integral part of the history of the Lehigh Valley. Many of the surrounding buildings are in disarray. Utilizing our ability to survey the building site, integrate AutoCAD toolset, and incorporate OSHA-compliant scaffolding design software, we worked to determine the safest and most efficient scaffolding design solutions. We have identified scaffolding requirements, the most damaged/vulnerable sites in the building, and the overall environmental layout that surrounds the building. Blending all these elements together allowed us to develop a design proposal to safely and effectively rehabilitate this culturally significant heritage building site.

Renusree Bandaru Campus Affiliation: Penn State Brandywine Major: Computer Science Anticipated Graduation: May 2022 Mentors: Martin Yeh (Penn State Brandywine), Hannah Nolte (Penn State University Park) Project Title: Using Hidden Markov Models to Detect Learning Stages by Difference in Electrical Activities of the Brain Learning has been described by cognitive scientists as three different stages, i.e. declarative learning, transition, and procedural learning. Identifying the learning stages can be correlated to one’s mastery level. This allows us to determine whether the individual requires more time to practice and train or if the individual is ready to apply their knowledge with greater accuracy. Researches also identified that we have different brain systems controlling the process of declarative and procedural learning. However, there is limited research on how brain activity registers these learning stages. This research aims to take advantage of various implications of Machine Learning technology to examine and study how people cognitively learn. Because of the amount and the complexity of the EEG data, traditional statistic models may miss subtle but critical signatures. We explored variations of Hidden Markov Models (HMMs) to better understand which parts of the human brain and what characteristics of a brainwave indicate differences in the cognitive learning stages. Currently, we are removing any background noise such as blinking of the eye from various suitable public EEG data sets for our analysis. Ultimately, we will design an experiment to collect data for the project and use the HMMs previously developed to investigate learning stages in coding. We expect to identify cognitive characteristics of the learning stages through the use of the HMMs and deepen our understanding about the areas of the brain that relates to how humans learn a subject repeatedly.

Tyler Barry Campus Affiliation: Penn State Greater Allegheny Major: Civil Engineering Anticipated Graduation Date: May 2022 Mentors: Alandra Kahl (Penn State Greater Allegheny), Nathaniel Warner (Penn State University Park) Project Partner: Lukas Seibel Project Title: Measuring Surface Water Quality with the AWQUA Now more than ever, it has become important to monitor water sources to ensure their safety for use and consumption. With an ever-growing population and ever-expanding industries, water resources have become much more important than ever before. Monitoring smaller waterways can be difficult due to the expensive nature of most sensors. We have created a device that uses a more affordable Arduino board and software system, as well as 3D printed housing to minimize costs while still collecting accurate data. The software monitors temperature, turbidity, and conductivity. Temperature and conductivity are used to help determine the salinity of the water. Turbidity is a way of measuring the overall clarity of the water. With these, we can gather an overall understanding of the water quality, and the affordability will allow for a wide deployment of these devices. In the future, citizen scientists to contribute data from their local waterways through using these sensors.

Ethan Baxter Campus Affiliation: Penn State Behrend Major: Computer Science Anticipated Graduation: May 2022 Mentors: Faisal Aqlan (Penn State Behrend), Hui Yang (Penn State University Park) Project Partner: Trevor Knox Project Title: Multiplayer Virtual Reality Games for Manufacturing Paradigms Manufacturing is increasingly important to the engineering education in all disciplines. Several approaches can be used to introduce engineer students to manufacturing concepts. Although previous studies show the effectiveness of virtual reality (VR) for a single participant, very little has been done to focuses on manufacturing education of multiple participants with an instructor monitoring and guiding the participants. Multiplayer VR games for different manufacturing paradigms (i.e., craft production, mass production, lean manufacturing, mass customization, and personalized production) are created. The instructor hosts the VR simulations on a server, allowing the participants to use the simulation together in real time. Participants start in a central hub surrounded by multiple rooms; each simulates one manufacturing paradigm. In the VR room, participants build car toys with the instructor present to provide assistance. This hands-on approach to teaching manufacturing methods gives direct feedback to the participants, which results in a more engaging and immediate learning experience. Another benefit of the study is the cost effectiveness and the remote learning capabilities. Participants do not need to be on a factory floor, or even in person to learn manufacturing processes. With VR headsets and proper infrastructure, participants can collaborate on the completion of simulation tasks from different locations. This can also improve their teamwork and communication skills.

Brodie Baxter Campus Affiliation: Penn State Berks Major: Computer Science Anticipated Graduation Date: May 2022 Mentors: Joseph Mahoney (Penn State Berks), Joseph Cusumano (Penn State University Park) Project Title: Immersive VR System with Realistic Motion Virtual reality (VR), a computer-generated simulation of a three-dimensional environment that can be interacted with in a seemingly real way, can be utilized to create a wide variety of scenarios in which to record human data. VR can allow for immersive experiences that simulate real-world situations, such as walking down a hallway, which allow researchers to make observations and collect data in a controlled environment such as on a treadmill. Recent studies have shown a connection between subject age and their ability to maintain proper balance in walking while visual feedback is disrupted and have shown the need to determine further data collection using the modern technology of VR. Here, we report the design and implementation of such a VR environment, allowing for more accurate data collection on this topic. We have modeled and designed a hallway of infinite length in a virtual space to be used with a VR headset and a treadmill, allowing for accurate data to be collected and recorded by further studies. With this infrastructure in place, further research can be done on the topic using these tools to assist walking balance in older subjects. Ritu Bhalodia Campus Affiliation: Penn State Harrisburg Major: Aerospace Engineering Anticipated Graduation Date: May 2023 Mentors: Richard Ciocci (Penn Stare Harrisburg) and Saurabh Basu (Penn State University Park) Project Title: Evaluating Possible Alternatives to Recycling of Paper and Finding Its Application in Improving Sustainability Practice at Penn State Harrisburg Recycling of paper has long been practiced in United States, with the rate of recovered paper for recycling being 66.2 percent as of 2019- a rate that is being consistent since 2009. One of the reasons in seeing no increment in the recycling rate is the overall cost of recycling of paper and unavailability of paper recycling firm at some places across the nation. Thus, this research provided a review on understanding different alternatives to paper recycling proposed by various researches. This study then proceeded on finding an application of the above acquired knowledge in improving the sustainability practices adopted by Penn State Harrisburg (PSH) campus since PSH has low paper recycling rate due to high cost of the recycling process near the campus. To better understand the recycling practices at PSH, a comparative analysis of waste segregation practices was made between different Penn State campuses. It was observed that among all the campuses, University Park campus has the highest recycling rate. To better understand the reason behind this, few estimations were made. Thus, this study seeks to determine a systematic approach based on these estimations in finding an effective solution to the paper waste management at PSH.

Mihir Raj Bhatnagar Campus Affiliation: Penn State Harrisburg Major: Mechanical Engineering Anticipated Graduation Date: May 2022 Mentors: Esfakur Rahman (Penn State Harrisburg), Ze Feng Gan (Penn State University Park) Project Title: A Structural, Aerodynamic and Material Comparison of Candidate Materials for a Commercial Airplane Variable Camber Morphing Wing Trailing Edge Morphing wings are a formidable piece to the puzzle of optimizing aerodynamic and fuel efficiency that ultimately converges towards increased accessibility to air transportation, and consequently, increased global connectivity. Due to the single jointless, seamless and continuous nature of morphing wing profiles and structures in general, they mitigate aerodynamic drag induction and large deviations from ideal wing configurations for particular flight conditions, which are otherwise prominent in conventional commercial aircraft due to gaps from discontinuous discrete wing structures and rigid wing shape change mechanisms respectively. While previous developments into morphing wings have seen

research and testing of various types of morphing implementations such as variable wing sweep, wing twist and camber modifications; much of such research and testing of morphing technologies has been limited to military based aircraft, private jets, helicopters and unmanned aerial vehicles (UAVs). Additionally, only limited research has been conducted on the analysis and implementation of suitable materials in a morphing wing system. This paper uniquely combines the under-researched area of adaptive trailing-edge based variable camber morphing wings in a conventional commercial fixed wing Boeing 737 aircraft, with a list of potential candidate materials that includes a novel class of materials that were previously unemployed in a morphing wing system. This combination yields a comprehensive comparison of potential candidate materials that includes material, aerodynamic and structural analysis. Structural and aerodynamic assessments are additionally extended to establishing a general comparison between morphing and conventional wing systems. The material assessment uses known material properties and formulated comprehensive equations to construct material comparison matrices that feature scores representing the specific and overall favorability of candidate materials in a morphing wing environment. Structural and aerodynamic assessments entail ANSYS simulation analysis, where testing is conducted on designed 3D morphing and conventional wing models along with 2D morphing and conventional wing airfoils respectively. Consequently, separate observations are made on lift to drag ratio vs angle of attack, wing deformation angle and airspeed, to assess aerodynamic performance, while observations on von misses stress and load calculation for given deformation are made through structural assessments. Based on these observations, comparisons are made between various morphing wing designs corresponding to each candidate material, in addition to an ultimate comparison between morphing wing systems and conventional wing systems. From these comparisons, it is determined whether the extent of derived advantages of morphing wings if present, are significant enough to substantially enhance fuel efficiency and yield profits in the commercial fixed wing aviation industry.

Chang Zhe Bickel Campus Affiliation: Penn State Altoona Major: Electro-Mechanical Engineering Technology Anticipated Graduation: May 2021 Mentors: Sohail Anwar (Penn State Altoona), Hussein Abdeltawab (Penn State Behrend), Constantino Lagoa (Penn State University Park) Project Title: Improving Object Detection Robot Performance Detection Robots are used for many applications, such as the floor cleaning robot that avoids obstacles in its path. The output of the robot sensor is considered a Gaussian random variable in the presence of an object. The mean value of the output is one when the object is present and zero when the object is absent. The variant for the probability density function (PDF) is dependent on the quality of the sensor used for the robot, while low quality sensors usually have larger output variance. To improve the detection quality, this research proposes a decision algorithm that defines a specific threshold value. When the sensor output value is above this threshold, the robot will decide an object is present and vice versa. Utilizing high quality sensors is expensive which increases the robot cost. As an alternative solution, we are aiming to improve the performance of robot detection by the proposed algorithm without using high-quality sensors. By combining the sum of PDF and weighted sum, calculating the weighted sum of multiple robots’ outputs will improve the detection system’s performance. The improvements were found by comparing the receiver operating characteristic (ROC) curves for different systems. The system will need to assign the best weights to each robot to ensure optimum performance. This system can be used in the future to make detection robots more accurate without the need to buy higher quality sensors.

Ryan Bingaman Campus Affiliation: Penn State Berks Major: Mechanical Engineering Anticipated Graduation: May 2021 Mentors: Azar Eslam Panah (Penn State Berks), Bo Cheng (Penn State University Park) Project Title: The Aerodynamics of LMP1 Vehicles and the Relation to Consumer Technology Le Mans Prototypes Class 1 (LMP1) automobiles are highly advanced race cars that compete in long distance races in multiple countries across the globe. The innovative aerodynamic components utilized on these vehicles influences the

car’s aerodynamics; allowing these prototypes to achieve ultimate track performance. Although these prototypes and other racecars utilize this advanced technology, many consumer vehicles that are currently on the road today are excluded. In this work, a literature review of the history, physics, and current use of these aerodynamic components on racecars was conducted with the intention of connecting and revamping consumer technology with LMP1 technology. It was found that using this technology on road vehicles could increase the negative lift coefficient of consumer vehicles, allowing an increase of stability and safety under hazardous and non-hazardous conditions. This application of LMP1 technology to consumer vehicles allows the future of automotive design to be safer than ever before.

Thomas Bish Campus Affiliation: Penn State Altoona Major: Electro-Mechanical Engineering Technology (EMET) Anticipated Graduation: May 2022 Mentors: Sohail Anwar (Penn State Altoona), Derek Hall (Penn State University Park) Project Title: Development of a Mobile Photovoltaic System for Aid in Disaster Relief The aim of this project is to develop a mobile Photovoltaic (PV) system that can adequately power at-risk hospitals along the U.S. coast. Dispatchable power systems are a form of disaster relief with the capability of saving lives especially if they are used to support hospitals. We propose to develop a mobile PV system that will consist of a solar array and supporting equipment that can be transported to locations in need. As natural disasters like hurricanes both increase the need for medical services and wreak havoc on the grid, our system can provide a means to keep basic services energized until normal grid operations are returned. Here, we quantify key system parameters of a mobile PV system using computational software to understand requirements such as system components and needed land area. By matching the electricity consumption data from a typical hospital with the output of our PV system we demonstrate the feasibility of this concept. Future efforts can use our framework as a basis for sizing additional mobile PV systems based on their deployment location and expected electrical demands.

Savanna Carr Campus Affiliation: Penn State Behrend Major: Mechanical Engineering Anticipated Graduation: May 2022 Mentors: Adam Hollinger (Penn State Behrend), Michael Hickner (Penn State University Park) Project Partner: Zane Smith Project Title: Cost Analysis of Injection-Molding Electrically Conductive Polymers for PEMFCs Hydrogen fuel cells are an up and coming source of clean energy. Fuel cells can be used in many applications from vehicles to buildings and can even be grid-independent which is an appealing option for critical operations. However, the bipolar plates of fuel cells are primarily made from graphite, which can be costly and difficult to machine due to its brittle properties. An alternative is to use an injection-molded polymer composite that has comparable electric conductivity. Material samples were injection molded with various mixes of Nylon-6,6 and nickel-coated carbon fiber. Not much is known about the cost of producing this specific composite, so it is hard to compare to other production methods. By researching three different methods of producing bipolar plates (stamping, machining, and injection molding), the analysis shows the difference in costs of the production methods. By developing such a composite, a more cost-effective substitute for graphite can be used in the mass production of hydrogen fuel cells.

Pengchang Chen Campus Affiliation: Penn State Abington Major: Computer Science Anticipated Graduation Date: May 2022 Mentors: Vinayak Elangovan (Penn State Abington), Vittaldas Prabhu (Penn State University Park) Project Title: Object Sorting using Robot Arm Based Neural Network

In a factory production line, different industry parts need to be quickly differentiated and sorted for further process. Parts can be of different color and shapes. It is tedious for humans to differentiate and sort these objects in appropriate categories. Automating this process would save more time and cost. In automation process, choosing an appropriate model is more challenging to detect and classify different objects based on specific features. In this paper, three different neural network models are developed for the object sorting system. They are namely CNN, FRCN and Faster R-CNN. These models are tested, and their performance are compared for efficiency and effectiveness. Moreover, for the object sorting system, an Arduino controlled 5 DoF (degree of freedom) robot arm is programmed to grab and drop objects to targeted zone. Objects are categorized into classes based on color, defective and non-defective objects.

Isaac Cherico Campus Affiliation: Penn State DuBois Major: Mechanical Engineering Anticipated Graduation: May 2024 Mentors: Neyda Abreu (Penn State DuBois), Katherine Crispin (Penn State University Park) Project Partner: Alexander Gianvito Project Title: Products of aqueous alteration in CM carbonaceous chondrites Carbonaceous chondrites are a type of stony meteorite which are often used for research to better understand early solar processes. CM chondrites are parts of meteorites, specifically carbonaceous chondrites which have undergone variable levels of aqueous alterations. Analyzing chemical and petrological compositions of these chondrites provides important information for understanding early solar processes. We use these methods in order to classify chondrites and alteration level. Primary characteristics of these meteorites are still undefined, in particular those materials in which are highly susceptible to aqueous alterations. This subject is one that will hold value to other scientists/researchers in the field who rely on each other in order to further develop knowledge on this topic. It has also been hypothesized by some that asteroid bodies could one day house lucrative business opportunities such as asteroid mining. This study is also of personal importance to me because it grants me an opportunity to learn about a topic that is deeply rooted in my interests and major. Chondrite research is a topic where many conventions still need to be set in order better identify and classify materials and processes. The goal that my team is trying to accomplish is to get an early exposure to topics and build off of them. We are looking to be able to determine the mineralogical features of our samples and using this and chemical composition mapping, determine what level of aqueous alterations took place. We used chemical composition data from samples LAP02333, LAP04514, LAP04527, LAP04565, and QUE97 in order to perform our operations. The data included element and oxide weight percentages contained in a given sample. We can use this information and information from the recently discovered Paris chondrite to determine what mineral group is contained in the chondrite and what level of alterations it has undergone.

Larry Covington Campus Affiliation: Harrisburg Major: Mechanical Engineering Technology Anticipated Graduation: May 2021 Faculty Mentors: Brian A. Maicke (Penn State Harrisburg), David L. Lyons (Penn State University Park) Project Title: Analyzing the Axial Flow of Swirl Effect in a Hybrid Rocket The main objective of this research is to further address the difficulty of generating large thrust in a hybrid rocket by producing a swirl effect inside the fuel chamber. Theoretically, the solid fuel would burn more efficiently with a swirl effect than a traditional hybrid rocket. This would be caused by having swirl injectors in specific placements to allow the liquid oxidizer to swirl around the solid fuel in a cylindrical chamber. However, the boundary layer thickness of the velocity will cause the swirl velocity to slow down. Because of this, swirl injectors will need to be placed along the cylindrical chamber to keep the developed velocity constant along the axial distance. In this research, variations of swirl injector placements are analyzed along the axial distance of the cylindrical chamber. The axial distance of the swirl effect was calculated to determine if additional swirl injectors are required along the axial distance and the exact point they should be placed to maximize the swirl effect. The flow through the cylinder was modeled using Converge software for

computational fluid dynamics [CFD] in the laminar flow regime. Swirl effect is the combination of the equations, swirl number (SN) and swirl angle (SA), which were used to validate the CFD models.

Conner Delancey Campus Affiliation: Penn State Behrend Major: Software Engineering Anticipated Graduation Date: May 2021 Mentors: Omar Ashour (Penn State Behrend), Christopher Mccomb (Penn State University Park) Project Partner: Maura Wells Project Title: VR Dementia Virtual Reality (VR) creates a ‘near-reality’ experience by allowing humans to physically interact with computer-generated simulations of three-dimensional environments. This project aims to develop a VR app to mimic dementia-related symptoms (DRS). Understanding of the concepts behind different types of DRS is essential for all caregivers engaged in dementia-related work. The complexity of DRS is not easily understood by caregivers through second-hand experience and theoretical learning. Due to the caregivers’ difficulty of understanding such concepts, many caregivers lack essential management skills to handle patients with dementia. This leads to increased stress in caregivers, and in the worst-case scenario, preventable hospitalization. Our VR app simulates a typical home environment, and caregivers of people with dementia (PWD) complete daily tasks such as taking medication and making the bed while experiencing different DRS including tremors, memory loss, and difficulty with coordination and motor functions. This simulation allows the caregivers to experience some of the difficulties of PWD for themselves. The complexity and difficulty of dementia-related symptoms is an unchangeable factor; however, caregivers’ failure to understand various concepts may indicate that current teaching methods are ineffective. This novel app is an invaluable new teaching method because it allows caregivers to gain first-hand experience to improve the quality of care they provide for PWD. The expected outcomes of this research would be caregivers’ increased awareness, empathy, and understanding of dementia-related symptoms, which will result in lower stress and fewer hospitalizations.

Aaron Dominick Campus Affiliation: Penn State New Kensington, Penn State University Park Major: Engineering Science Anticipated Graduation Date: May 2021 Mentor: Ibrahim Ozbolat (Penn State University Park) Project Title: Digitally Controlled Needle Array Platform Aspiration-assisted bioprinting (AAB) is a type of three-dimensional (3D) bioprinting that utilizes aspiration forces to precisely bioprint a wide range of biologics in both scaffold-based and scaffold-free arrangements. It has a wide range of future applications ranging from organ-on-a-chip devices, regenerative medicine, and the study of fundamental biological phenomena. However, the current AAB model is not time efficient, placing only a singular biologic at a time. To improve upon the design and increase efficiency, an array of micropipettes will be employed to print the biologics in a layer-by-layer manner while still allowing for individual valve control. The software side was created using a combination of processing and Arduino that allows for precise control over the movement of the printer, manual activation of valves, and pressure monitoring at the location of the biologic. The hardware side is overseen by an Arduino Due microprocessor that modulates the system through connections with stepper motors, logic level MOSFETS, and a Sparkfun BME 280 Atmospheric Sensor. Overall construction and testing are still underway, but the initial results show promise. Once every system is completely integrated, the array based AAB will offer a time efficient, cost effective, and highly precise bioprinting technique.

Joseph Fantuzzi Campus Affiliation: Penn State Lehigh Valley Major: Civil Engineering Anticipated Graduation Date: May 2022 Mentors: Tracey Carbonetto (Penn State Lehigh Valley), Sez Atamturktur (Penn State University Park)

Project Partner: Austin Azar Project Title: The Pursuit of Non-Invasive Scaffolding Solutions for the Restoration of Lehigh Valley Heritage Sites: Number Two Machine Shop The focus of this research project is to establish non-invasive scaffolding solutions for the rehabilitation of heritage structures. This summer’s study specifically focuses on the Number Two Machine Shop as a case study application in the Bethlehem Steel Complex located in Northampton County, Pennsylvania. The saving of this historical site is crucial in preserving an integral part of the history of the Lehigh Valley. Many of the surrounding buildings are in disarray. Utilizing our ability to survey the building site, integrate AutoCAD toolset, and incorporate OSHA-compliant scaffolding design software, we worked to determine the safest and most efficient scaffolding design solutions. We have identified scaffolding requirements, the most damaged/vulnerable sites in the building, and the overall environmental layout that surrounds the building. Blending all these elements together allowed us to develop a design proposal to safely and effectively rehabilitate this culturally significant heritage building site.

Bilal Faye Campus Affiliation: Penn State Altoona Major: Electro-Mechanical Engineering Technology Anticipated Graduation: May 2021 Mentors: James D. Freihaut (Penn State University Park), Sohail Anwar (Penn State Altoona) Project title: Methodological framework for the implementation of a smart grid in a sub-Saharan country: Senegal The growing concern for the basic living needs of African countries is attracting foreign investors and researchers who share a common goal of developing sustainable frameworks for the development of the continent. Acknowledging the rapid growth of African populations, it is important to prepare for a significant increase in energy consumption. In fact, numerous African governments are increasingly implementing policies in favor of renewable energy distribution and encouraging the adaptation of new methods for an expansion of the grid infrastructures. The key objective of this study is to develop a methodological framework for implementing microgrids in developing countries in Africa while considering the high-dimensional variables that could possibly affect the successful introduction of such technology. The study helps identify sustainable means of promoting microgrid implementations while considering the difference in cultural values when it comes to energy consumption. The analysis focuses on sub-Saharan Africa, which is a zone drastically affected by global warming. The scope of the study is set on Senegal which represents an appropriate example of a sub-Saharan country that promises potential in use of renewable energy. This microgrid design guide uses rural community profiles to assess the fitness of distributed energy systems. The simulation of various microgrid architectures highlights the designs that add more resiliency and reserve capacity to the actual Senegalese national grid. The result of this study can help utility companies build assessment tools that will facilitate the integration of microgrids and reduce the cost associated with producing electricity.

Joseph Fioti Campus Affiliation: Penn State Wilkes-Barre Major: Computer Science Anticipated Graduation: May 2022 Mentors: Jeffrey Chiampi and Dimitrios Bolkas (Penn State Wilkes-Barre), Peter La Femina (Penn State University Park) Project Partner: Donovan Gaffney Project Title: Simulating Surveying In VR Surveying has historically been done outdoors, and in order for students to gain hands-on experience, the outdoor laboratories have been a requirement. We present an environment where students can gain hands-on experience under the watchful eye of an instructor in several locations with varying levels of difficulty, all without leaving the comfort and convenience of the classroom. Virtual Reality (VR) technology has made significant leaps of progress in the past few years alone and is now at a point where performing high accuracy tasks in a virtual environment is feasible. We demonstrate how we use this technology to create a much more immersive and interactive environment than previous attempts at VR implementations, spurring visual and tactile learners much better than a conventional lecture. To build this, we modeled multiple environments and surveying instruments, as well as implementing a novel leveling technique

for multi legged 3D models. Surveying is a field where hands-on interaction is paramount to conveying complex concepts to students, and frequently these concepts are taught by traditional lecture and eventually a hands-on laboratory, although these laboratories are weather dependent. These exercises are also limiting. Students typically get experience in a single location where labs can only be done a limited number of times before the actions become repetitive. VR can provide students with a large amount of rich and novel experiences to learn from and hone their skills, regardless of the weather. Many landscapes and locations can be accessed instantly. The students are able to manipulate the same equipment they encounter in the real world, while the instructor can watch over their shoulder through the entire experience and replay it at their leisure. This provides a level of instructive granularity that can only be experienced outside of the physical world.

Jeremy Fischer Campus Affiliation: Penn State Behrend Major: Mechanical Engineering Anticipated Graduation: May 2022 Mentors: Chetan Nikhare (Penn State Behrend), Todd Palmer (Penn State University Park) Project title: The Effect of Microstructure on the Elastic Modulus In the automotive industry, there is a considerable pressure to reduce emissions of greenhouse gases in the atmosphere by reducing the amount of steel in vehicles. The industry is researching ways to reduce weight and improve fuel consumption for their vehicles, while still maintaining the safety and engineering requirements. Currently, the industry uses mild steels that have a large deformation capacity but are limited to having lower strength. As a result, mild steel is primarily used for the bodies of vehicles and no other applications. A higher ductility and strength option would be advanced high strength steels (AHSS), such as DP and TRIP. However, AHSS steels are limited in the automotive industry due to the challenges in forming, tool life, sheet metal coupling, and springback behavior. The springback is the main problem with AHSS steels, which compromises the mass-production of automotive structural components. Various articles relating to heat treatments and other effects of young’s modulus degradation have been thoroughly analyzed to produce a review based on the effects of how the microstructure of the material affects the elastic modulus. The results indicate that the elastic modulus degrades more significantly with respect to pre-strain, decreases with respect to the orientation of the rolling direction, and decreases as ferrite volume fraction decreases or when the austenite volume fraction increases. Further research into this topic can allow various high strength steels to be introduced into the automotive industry, as well as understand the elastic recovery behavior during the forming process. This data can be then fed to numerical model to predict the springback behavior and correction can be engineered in the during stamping.

Joshua Forrest Campus Affiliation: Penn State University Park Major: Aerospace Engineering Anticipated Graduation Date: May 2021 Mentor: Mark Miller (Penn State University Park) Project Title: Designing an aerodynamically scaled model for the NREL 5MW reference wind turbine using XFOIL and XTURB Testing Horizontal Axis Wind Turbine (HAWT) designs in wind tunnels is timely and cost-effective for experimentally improving and verifying aerodynamic designs before implementation. Field experiments are complicated and time-consuming, begging a need for controlled testing of rotor performance and structural response, particularly for yawed flow conditions. However, matching all three experimental aerodynamic non-dimensional parameters: Reynolds number, tip speed ratio, and Mach number, for the model scale matching the geometry of a full-scale wind turbine, has proved challenging in standard wind tunnels. Previously, researchers have matched Mach number and tip speed ratio but sacrificed Reynolds number parity in wind tunnel experiments. Mach number and tip speed ratio are essential for comparable rotor performance. Reynolds number effects have significant impacts locally on the performance of the blades, particularly for the thicker airfoils used for wind turbines. Increasing the air pressure of the wind tunnel to 500 PSI allows for more suitable Reynolds number matching. Experiments at the Princeton University High Reynolds number

Test Facility (HRTF) have demonstrated Reynolds number insensitivity for rotor diameter Reynolds numbers of 2x106 to 16x106, although the overall poor performance of the model rotor. Reaching the threshold of Reynolds number insensitivity would increase the value of wind tunnel data. This research set out to design a stiff model rotor for high-pressure wind tunnel testing that reaches Reynolds number insensitivity and also has comparable rotor performance to the NREL 5MW Reference Wind Turbine using 2D airfoil inputs from XFOIL in XTURB. Stall delay, boundary layer tripping, chord adjustment, and twist adjustment for matching non-dimensional local blade airfoil circulation were explored. The stall delay feature in XTURB simulations and setting tripped boundary layers in XFOIL did not lead to the desired boundary layer effects expected. Matching non-dimensional local circulation along the blade did lead to improved thrust and power coefficient agreement between the model and full-scale simulations. The NREL 5MW rotor uses thick inboard airfoils created by the Delft University of Technology that have limited or no available 2D airfoil data. Experimental validation is necessary for XFOIL airfoil polars used for XTURB simulations. Future work will be to create a model rotor using scaling techniques and findings derived from this paper using airfoils in possession of Penn State to use experimentally obtained airfoil polars. The development of an aerodynamically scaled wind turbine model would allow for wind tunnel experiments with results directly applicable to full-scale HAWT development and design.

Pablo Franco Almonte Campus Affiliation: Penn State Wilkes-Barre Major: Mechanical Engineering Anticipated Graduation: August 2022 Mentors: Timothy Sichler (Wilkes Barre), Richard Auhl (Penn State University Park) Project Title: Proposed Design for a Sail Wind Turbine to Support Evaluation of Different Rotor Sail Configurations An affordable wind turbine will allow small companies and individuals to save money and at the same time help in lessening global dependence on fossil fuels. A physical prototype will allow testing and evaluation of different sail setups and configurations in varied wind conditions to find the most optimal tuning for the specific sail size and shape. In this research project, a two-piece fabric sail wind turbine blade that takes advantage of aerodynamic lift is designed and built to gather data on how the Jib and mainsail system behaves under rotation. For max power generation the rotor needs to self-tune to the incoming wind, while maintaining simplicity in design. A microcontroller will use incoming data from an anemometer to fine tune, with the help of a few servos, the rotor orientation and the double swash plate system. The double swash plate system will consist of one swash plate for controlling collective pitch and the other for fine tuning Jib position relative to the mainsail. Other engineering work such as: computer simulations for torque approximations, mechanical analysis, material selection, and CAD drawings are described and presented. Progress into building the wind turbine has been slow. Although, the project will continue until data can be obtained.

Donovan Gaffney Campus Affiliation: Penn State Wilkes-Barre Major: Computer Science Anticipated Graduation: May 2022 Mentors: Jeffrey Chiampi and Dimitrios Bolkas (Penn State Wilkes-Barre), Peter La Femina (Penn State University Park) Project Partner: Joseph Fioti Project Title: Simulating Surveying In VR Surveying has historically been done outdoors, and in order for students to gain hands-on experience, the outdoor laboratories have been a requirement. We present an environment where students can gain hands-on experience under the watchful eye of an instructor in several locations with varying levels of difficulty, all without leaving the comfort and convenience of the classroom. Virtual Reality (VR) technology has made significant leaps of progress in the past few years alone and is now at a point where performing high accuracy tasks in a virtual environment is feasible. We demonstrate how we use this technology to create a much more immersive and interactive environment than previous attempts at VR implementations, spurring visual and tactile learners much better than a conventional lecture. To build this, we modeled multiple environments and surveying instruments, as well as implementing a novel leveling technique for multi legged 3D models. Surveying is a field where hands-on interaction is paramount to conveying complex concepts to students, and frequently these concepts are taught by traditional lecture and eventually a hands-on laboratory,

although these laboratories are weather dependent. These exercises are also limiting. Students typically get experience in a single location where labs can only be done a limited number of times before the actions become repetitive. VR can provide students with a large amount of rich and novel experiences to learn from and hone their skills, regardless of the weather. Many landscapes and locations can be accessed instantly. The students are able to manipulate the same equipment they encounter in the real world, while the instructor can watch over their shoulder through the entire experience and replay it at their leisure. This provides a level of instructive granularity that can only be experienced outside of the physical world.

Nicholas Gehring Campus Affiliation: Penn State Abington Major: Computer Science Anticipated Graduation: May 2022 Mentors: Yi Yang (Penn State Abington), Yanxi Liu (Penn State University Park) Project Title: Rapid Identification of Concussions Using Ultrasonic Technology At least 1.6 million concussions occur every year in just the United States alone. An ample amount of research has already been conducted to look at ways to treat and diagnose concussions effectively, yet concussions are known to be hard to diagnose as symptoms can vary greatly. MRI or CT scans can be used to identify severe damage to the brain as the damage would involve bleeding or bruising, but these methods are expensive and less viable in diagnosing more minor concussions. However, there’s another method known as the near point convergence (NPC) test which alleviates these issues. This test, which relies on measuring the distance from the patient’s eyes to the focusing point of their vision, also has its own issues as the measurements can prove to be inconsistent or hard to obtain. We can instead acquire more accurate readings using ultrasonic sensors. Performing different tests with Chirp Microsystems’ new CH-101 ultrasonic time of flight (ToF) sensors, we can see that these sensors could potentially be used for an NPC measuring device providing ophthalmologists or sports trainers an easy way to diagnose concussions.

Abraham George Campus Affiliations: Penn State DuBois, Penn State University Park Major: Mechanical Engineering Anticipated Graduation: May 2021 Mentors: Neyda Abreu (Penn State DuBois), Katherine Crispin (Penn State University Park) Project Title: Using Python to Develop Tools to Assist with the Analysis of Chondrites Chondrites are a class of meteorites characterized by chondrules, which are small, roughly spherical, silicate rich inclusions. Chondrules are surrounded by matrix, a small grain material that cements the chondrules together. Chondrite meteorites are over 4.5 billion years old, and their compositions can tell us much about the conditions in the early solar system. Currently, highly precise microscope spectrometers allow for the gathering of vast amounts of data about the compositions and structure of chondrites, mainly in the form of cross-sectional maps. Although these techniques can produce large useful data sets, the ability to analyze the data, especially in a quantitative way, is falling behind. This paper focuses on the development of computational tools, using the programing language Python, to help with the analysis of chondrite cross sections. Using these tools, future researchers will hopefully be able to speed up their analysis and gain more insight from the data they have.

Alexander Gianvito Campus Affiliation: Penn State DuBois Major: Engineering Pre-Major Anticipated Graduation: May 2024 Mentors: Neyda Abreu (Penn State DuBois), Katherine Crispin (Penn State University Park) Project Partner: Isaac Cherico Project Title: Products of aqueous alteration in CM carbonaceous chondrites

Carbonaceous chondrites are a type of stony meteorite which are often used for research to better understand early solar processes. CM chondrites are parts of meteorites, specifically carbonaceous chondrites which have undergone variable levels of aqueous alterations. Analyzing chemical and petrological compositions of these chondrites provides important information for understanding early solar processes. We use these methods in order to classify chondrites and alteration level. Primary characteristics of these meteorites are still undefined, in particular those materials in which are highly susceptible to aqueous alterations. This subject is one that will hold value to other scientists/researchers in the field who rely on each other in order to further develop knowledge on this topic. It has also been hypothesized by some that asteroid bodies could one day house lucrative business opportunities such as asteroid mining. This study is also of personal importance to me because it grants me an opportunity to learn about a topic that is deeply rooted in my interests and major. Chondrite research is a topic where many conventions still need to be set in order better identify and classify materials and processes. The goal that my team is trying to accomplish is to get an early exposure to topics and build off of them. We are looking to be able to determine the mineralogical features of our samples and using this and chemical composition mapping, determine what level of aqueous alterations took place. We used chemical composition data from samples LAP02333, LAP04514, LAP04527, LAP04565, and QUE97 in order to perform our operations. The data included element and oxide weight percentages contained in a given sample. We can use this information and information from the recently discovered Paris chondrite to determine what mineral group is contained in the chondrite and what level of alterations it has undergone.

Gabriella Gonzalez Magana Campus Affiliations: Penn State Berks, Penn State University Park Major: Computer Engineering Anticipated Graduation: May 2021 Mentor: Paris von Lockette (Penn State University Park) Project Title: Developing Magnetic Synapses Mimicking brain function in computing is seen as the next big break through. Traditional efforts that seek to optimize three terminal (transistor-like) architectures are seen as a technology with finite limits, whereas research into two-terminal systems, wherein this work falls, are seen as inherently more scalable. The project seeks to develop analogs of neurological synapses using magnetic material analogs. Research into artificial synapses seeks to recreate fundamental ‘plasticity’ in brain function. This plasticity, or memory of response, is what the brain uses to store memories and process information. To generate the artificial synapse, use of a solid polymer with fluid – filled cavities in which there are free floating polarizable bodies will be investigated. In this approach, magnetic particles suspended in a matrix will be subjected to an oscillating magnetic field. Therefore, this project focuses on developing a mathematical model for spinning polarizable body in an oscillating field.

Rebecca Grey Campus Affiliation: Penn State Behrend Major: Mechanical Engineering Anticipated Graduation: May 2021 Mentors: Charlotte De Vries (Penn State Behrend), Swapnil Sinha (Penn State University Park) Project Title: Universal Design Analysis for Improving the Usability of FFF 3D Printers 3D printing has experienced growing popularity with people including engineering professionals, hobbyists, artists, and teachers. To accommodate a variety of users, the concept of usability, or how easily and effectively a person can interact with the printer, must be considered. With the customizability of 3D modeling, there is great potential for 3D printing to be utilized in the home for various practical and entertaining applications. One possible use explored for the home is older generations printing accessibility aids for aging in place, or those who wish to live in their homes for longer. However, physical limitations such as hand strength, standing, and vision pose numerous challenges for learning this new technology. Design efforts focused on the user such as universal design, the idea of appealing design for all despite age or ability, have largely focused on software improvements. Therefore, there is a need to direct attention to operating the physical 3D printer to expand its field of use. To better gauge consumer needs, a survey was designed to understand difficulties and preferences of 3D printer users. In conjunction with survey development, the physical

aspects of fused filament fabrication (FFF) 3D printers were identified and analyzed in detail for areas in which older users may struggle. Action function diagrams were used to analyze the way in which the human body moves to perform various actions in order to identify the main physical challenges. When paired with universal design, it is easier to address potential physical issues while ensuring the design is valuable for variations in users beyond the elderly. The guidelines and survey results are intended to lead the continuation of research into the design stage for more user-friendly options that broaden the possibilities of consumer 3D printing.

Sakshi Gulgulia Campus Affiliation(s): Penn State Behrend, Penn State University Park Major: Electrical Engineering Anticipated Graduation Date: May 2021 Mentors: Julio Urbina (Penn State University Park), Diego Penaloza (Penn State University Park) Project Title: Electromagnetic characterization of the soil for a ground-penetrating radar to track nesting pollinators Approximately 70 percent of the bee species in the United States nest in the ground. Species such as solitary and bumblebees are experiencing numerous stresses and as a result, their population is declining. The effects of abiotic stress such as those associated with climate change are largely unknown due to a lack of information. The goal of this project is to develop a multi-channel ground-penetrating radar (GPR) to track nesting of several bee species inside various types of soils. A GPR sensor on transmission emits electromagnetic (EM) waves that travel at a specific phase velocity that is affected by the electrical properties of the medium. When the EM waves hit a target, a portion of the EM energy is scattered or reflected to the GPR instrument. This echo is sensed by the receiver system of the GPR. In this work, we carry out computer simulations of EM waves propagating in different types of soil to determine the most efficient range of frequencies for the operation of the GPR. We produced graphs showing how an electric field is attenuated as it travels into an ideal type of soil as a function of both distances and frequencies, using MATLAB. Further simulations were carried out to study EM wave propagation in more realistic soils such as silty loam and silty clay soils over frequencies ranging from 0.1 GHz to 1 GHz, using Python.

Sidney Hagen Campus Affiliation: Penn State Harrisburg Major: Mechanical Engineering Anticipated Graduation: May 2021 Faculty Advisors: Ola Rashwan (Penn State Harrisburg), Zoubeida Ounaies (Penn State University Park) Project Partner: Nik Zakimi Project Title: Study on the Mechanical Properties of PLA/Hemp Bio composite Filament for 3D Printing This research is on modeling of the extrusion process of polylactic acid (PLA) combined with Hemp as a biodegradable composite material to create 3D printing biocomposite filament. Hemp is emerging as a bio-filler. It has a relatively higher fraction of cellulose and comparable lignin to that in wood, thus possessing a greater reinforcement potential. 3D printing is fundamentally less wasteful than traditional, subtractive manufacturing methods, the use of plastics as a feedstock has the potential to exacerbate the global plastic problem unless a sustainable solution is found. Therefore, creating a biodegradable filament is a promising approach to overcome this constraint. Very few researches have been conducted to study the mechanical properties of Polylactic Acid (PLA)/Hemp bio composite. Simulating the extrusion process using finite element methods (FEM) can help predict the properties without the expense of wasting materials and time. There is little research on the extrusion process of PLA and no studies on modeling of the extrusion of PLA/Hemp blend. This project investigates the extrusion process of this biocomposite in a single screw extruder system using the COMSOL multiphysics simulation software. First, an assembly of the melting and melt conveying sections of an extruder was created in Solidworks. The assembly was imported into COMSOL Multiphysics 5.5, and the heat transfer and fluid mechanics physics modules were used. 5 % to 20 % weight percent of hemp are being investigated. For each weight percent of hemp, the different temperatures of the barrel and speeds of the screw are adjusted and tested. The melt flow of the biocomposite is obtained by analyzing the changes in viscosity, velocity and temperature profiles. This knowledge will serve as a platform for future development of sustainable 3D printing filament. The ultimate goal is to use this sustainable and biodegradable polymeric blend to replace polymers with a harsher impact on the environment.

Guanlin He Campus Affiliation: Penn State Harrisburg Major: Computer Science Anticipated Graduation: May 2021 Mentors: Hien Nguyen (Penn State Harrisburg), Corina Drapaca (Penn State University Park) Project Title: Some interactive tools for building a global-scale landslide database in google Cloud Image databases are mandatory in studies of natural disasters. However, the tasks of collecting, curating and annotating images need tremendous human labor. To help automating those tasks, we developed an interactive tool that effectively suggests the image segmentation and annotation by applying transfer learning techniques and convolutional neural networks. We applied the tool to build a database of landslides from Google satellite images. Our models were able to produce highly accurate landslide segmentations and reduce the processing time from 8-10 minutes per image by human to seconds.

Jessica Hege Campus Affiliation: Penn State Harrisburg Major: Mechanical Engineering Anticipated Graduation: May 2022 Faculty Advisors: Ola Rashwan (Penn State Harrisburg), Zoubeida Ounaies (Penn State University Park) Project Partner: Chase Sasala Project Title: Optical Study of Quantum dots Intermediate Band Inorganic Perovskite Solar Cells Solar energy is a key part of improving renewable energy and moving away from finite fuel sources. By increasing the efficiency of solar cells, in an affordable manner, it will be easier to move away from the use of finite fuel sources. Silicon has a limited efficiency, and an indirect band gap which limits its efficiency. Perovskite has a tunable band gap which makes it a good candidate to incorporate intermediate bands into the band gap. Inorganic perovskite has not been researched as much as organic-non-inorganic halide perovskites, however; cesium should increase stability of the perovskite and absorption, making it a good candidate to use in this research.This research investigates the efficiency of CsPbBr3 perovskite cells based on the material, and concentration of quantum dots. The quantum dots were used to make an intermediate band within the perovskite. The intermediate band material used were lead sulfide and germanium, and the concentrations tested were 5%, 10%, 15%, and 20%. Intermediate band Cesium based perovskite solar cells should increase the absorption of NIR photons. The solar cells were modeled and simulated using ANSYS HFSS. The simulations were focused on optical analysis. The solar cell stack consists of gold, Spiro OMe TAD, CsPbBr3, titanium dioxide, and fluorine-doped tin oxide. The model had a hexagon shape that had 8nm base length, and a perovskite absorption layer of 450nm. The material, shape, and concentration were varied to test which configuration yielded the highest absorption. The inclusion of an intermediate band should increase absorption which will increase the short circuit current density which will increase the efficiency. It is expected for the efficiency of the perovskite solar cell to increase, which will provide other researchers with techniques and data to farther increase the efficiency.

Sidney Hopson Campus Affiliation: Penn State Behrend Major: Computer Science Anticipated Graduation Date: May 2021 Mentors: Faisal Aqlan (Penn State Behrend), Hui Yang (Penn State University Park) Project Title: A Virtual Learning Factory for Manufacturing Processes and Production Planning In the engineering world, students often lack real-world experience and training they receive before beginning their careers. With the advancement of virtual reality (VR), students can experience a real-life simulation without the involved risks and costs. Although VR has fueled increasing interests in educational applications, engineering has not realized the full potential of this extremely helpful tool. In this work, we develop a VR simulation of a car manufacturing plant that involves four unique manufacturing processes (e.g., welding, fastening, 3D printing, and painting) as well as the use of

heavy machinery. The VR factory includes both manual and automated manufacturing processes as well as production planning tasks, which provides an effective platform to teach manufacturing to undergraduate engineering students. Based on an exemplary product (i.e., virtual car), production model, participants learn how to apply the different manufacturing processes and factory planning methods. Data collection and statistical analyses will be conducted. Further extension of VR factory towards remote collaboration is proposed and discussed.

Nicholas Houck Campus Affiliation: Penn State Berks, Penn State University Park Major: Chemical Engineering Anticipated Graduation: May 2022 Mentors: Sarah Freeburn (Penn State University Park), Rungun Nathan (Penn State Berks) Project Title: Hydrothermal Liquefaction: A Review Fossil fuels are in finite supply, and thus the search for an alternative is increasingly important. One solution is hydrothermal liquefaction (HTL). HTL processes can be used to convert various biomasses into bio-crude, a potential substitute for conventional crude oil when upgraded. The process is similar to pyrolysis, but HTL can use wet feedstocks, eliminating the need to dry the biomass beforehand. This paper aims to cover the different variables involved in HTL, such as temperature, heating rate, residence time, pressure, catalytic effects, and feedstock composition. Temperatures around 300°C were generally found to yield the most bio-crude, while variation in pressure had negligible effects as its main purpose is to keep the feedstock mixture in liquid phase during HTL. Short residence times and high heating rates (also called fast HTL) typically yield much more bio-crude than conventional HTL. The catalysts covered in this paper provided negligible effects and would make the HTL process less economical if it were scaled to commercial size. Potential yields and higher-heating values can be predicted based on feedstock composition, and most papers agree that a higher lipid content in the feedstock will produce more bio-crude; however the models could be improved upon with a better understanding of the chemical mechanisms in HTL processing. Chemical mechanisms will be covered, but they are not fully understood at this time. Additionally, this paper reviews other challenges faced by the technology and proposes further research that may make the technology more economically viable.

Saira Hussain Campus Affiliation: Penn State Lehigh Valley Major: Mechanical Engineering Anticipated Graduation: May 2022 Mentors: Tracey Carbonetto (Penn State Lehigh Valley), Jason Moore (Penn State University Park) Project Title: Frequency Cancellation Interface for Tinnitus Active noise control is a well-established technology that is based on the concept of destructive interference in which two sound waves having the same amplitude, frequency and 180 degrees out of phase with each other will sum to zero; the sound intensity at that point will be zero creating no sound. Investigation into the use of extrinsic or external noise can be utilized in the cancellation of intrinsic or internal noise. Findings showed that zero noise can be produced from two equivalent, simultaneous frequencies that are 180 degrees out of phase from one another. Further research is suggested in creating sound therapy devices for people suffering from tinnitus, a chronic condition of a subjective, intrinsic, and constant noise in the ears in the absence of auditory stimuli. Since the sound is not extrinsic, the tone of the sound can only be described in a subjective manner. Sound from a range of common tinnitus frequencies was tested along with the opposite audio frequencies simultaneously to validate the noise-cancellation theory. Additionally, a graphical user interface was developed to create a computer-generated pure tone at an inputted frequency so the subject may match it with their intrinsic frequency by adjusting the sound generation until no sound was detected. In this work, it was demonstrated that two simultaneous audio frequencies of pure tones were able to affect the overall sound, either with reducing or cancelling the noise. With a graphical user interface, the subjects can match a computer-generated tinnitus frequency with the tinnitus frequency that they hear inside of their head. This novel approach, along with the findings, will allow subjects to determine the nature of the specific tinnitus frequency allowing for an individual, customized noise-cancellation device that may prove worthwhile in relieving symptoms.

Alain Izabayo Campus Affiliation: Penn State Harrisburg Major: Civil Engineering Anticipated Graduation: May 2022 Mentors: Rajarajan Subramanian (Penn State Harrisburg), Rajabipour Farshad and Melika Sharifironizi (Penn State University Park) Project Title: Investigating the Possibility of Recycling Plastic Waste in Concrete One of the most pressing environmental concerns in the 21st century is the increase in the consumption of plastic materials, yet the disposal of plastic wastes is not only economically challenging but also impacting the environment negatively due to the non-biodegradability associated with plastics. On the other hand, the growth in the world population followed by urbanization and hence the boom of the construction industry, has resulted into an increase in the demand of construction materials. The increased demand has resulted into scarcity, thus leading to increased prices of construction materials consequently increasing construction costs. This research paper presents a review of the previously published researches that have investigated the possibility of recycling plastics in concrete as a partial replacement of natural aggregates. By making a comparative analysis of different research findings, the effect of using plastic wastes on the fresh, hardened and durability properties of concrete were evaluated. It was observed that most previous researchers acknowledge that the incorporation of plastic wastes tends to compromise some concrete properties while improving others. This observation implies that the suitability for the use of the concrete that was made by partial replacement of natural aggregates with plastic ones or with addition of fibers, depends on where the concrete will be applied in construction. It is also worth noting that the magnitude of the effect of these plastic wastes on the resulting concrete depends on the levels of substitution, the types of the original plastic material, the size and shape of the aggregates or fibers substituted in the concrete and the inclusion of the admixtures in the concrete mix.

Adam Jafri Malin Campus Affiliation: Penn State Harrisburg Major: Electrical Engineering Anticipated Graduation: May 2022 Mentors: Nashwa Elaraby (Penn State Harrisburg), Yiqi Zhang (Penn State University Park) Project Title: The Application of Support Vector Machine in eye diagram analysis in High-Speed Serial Links In this research work, we investigate the application of machine learning in the analysis of eye diagrams to evaluate the Signal Integrity of High-Speed Serial Links. An eye diagram is a plot that is constructed by overlaying successive symbol waveforms. An open eye makes it easier to distinguish the different voltage levels of the symbols, while a closed eye indicates a higher probability of misinterpreting the data at the receiver. The Support Vector Machine (SVM) regression is the machine learning algorithm used to model the eye heights based on random training data. The eye heights shrink due to link imperfections such as jitter, Inter-Symbol Interference (ISI), channel noise, which makes it harder to identify the received logic levels. Based on training data, optimal hyperplanes are constructed as decision boundaries to separate two or more classes using support vectors. The work presented in this paper is completed in MATLAB simulation environment using the machine learning onramp module provided by Mathworks.

Shane Kephart Campus Affiliation: Penn State Altoona Major: Electro-Mechanical Engineering Technology Anticipated Graduation: May 2021 Mentors: Gary Weisel and Kofi Adu (Penn State Altoona), Dr. Nestor Lopez (Penn State University Park) Project Title: SWCNT-Based Solar Cells: What’s Absorption got to do with it? Single walled carbon nanotubes (SWCNTs) have recently gained interest for their optical properties. SWCNTs could be the next step in increasing solar cell efficiency through their ability to absorb a much broader band of wavelengths than traditional solar cells. By vertically aligning the SWCNTs rather than horizontally aligning them, the interactions between the tubes will be reduced resulting in a higher efficiency. The optical properties of SWCNTs are directly related to the

efficiency of the solar cell. We review the relationship between reflectance, absorbance, and refractive index of vertically aligned SWCNTs between 2-20 microns. We plan to perform similar investigations in the ultraviolet and visible regions.

Luke Kepner Campus Affiliation: Penn State Wilkes-Barre Major: Telecommunication Anticipated Graduation: May 2022 Mentors: Dimitrios Bolkas (Penn State Wilkes-Barre), Alexander Klippel (Penn State University Park) Project Partners: Jason Kepner, David Neilson Project Title: Enhancing Outdoor Lab Instruction with Immersive Technologies Surveying engineering education requires extensive practice with instruments and equipment that are used for outdoor data collection of 3D spatial datasets. As technology advances, surveying techniques and practices become complicated. Typical introductory labs involve the instructor providing an overview of the instrument in class and then explaining how the students need to complete the laboratory before they go outdoors. However, students often have questions that arise during the laboratory, but it is difficult for the instructor to assist all groups in a timely manner as groups work on different parts of the campus. This creates unique instructional challenges and frustrates students, making outdoor laboratories unpleasant. To address these challenges, we created instructional material using immersive technologies. Using 360-cameras we have developed numerous instructional videos that students can watch before conducting outdoor labs. Immersive videos increase spatial comprehension of complex lab steps that are difficult to depict and explain. In addition, to the 360-videos we developed android applications that show major lab steps in augmented reality. The augmented reality labs use precise GPS coordinates and “shows” objects in desired real-world locations. Using a tablet, students will be able to play the applications and experience major lab steps in the real environment while conducting the lab. The goal is that 360-videos and augmented reality will work in tandem and students, based on their preference, will watch the 360-videos in the field or use the augmented reality apps. The 360-videos and augmented reality apps will be tested Fall of 2020 in a freshman class of the surveying engineering program at Penn State Wilkes-Barre.

Jason Kepner Campus Affiliation: Penn State Wilkes-Barre Major: Telecommunication Anticipated Graduation: May 2022 Mentors: Dimitrios Bolkas (Penn State Wilkes-Barre), Alexander Klippel (Penn State University Park) Project Partners: Luke Kepner, David Neilson Project Title: Enhancing Outdoor Lab Instruction with Immersive Technologies Surveying engineering education requires extensive practice with instruments and equipment that are used for outdoor data collection of 3D spatial datasets. As technology advances, surveying techniques and practices become complicated. Typical introductory labs involve the instructor providing an overview of the instrument in class and then explaining how the students need to complete the laboratory before they go outdoors. However, students often have questions that arise during the laboratory, but it is difficult for the instructor to assist all groups in a timely manner as groups work on different parts of the campus. This creates unique instructional challenges and frustrates students, making outdoor laboratories unpleasant. To address these challenges, we created instructional material using immersive technologies. Using 360-cameras we have developed numerous instructional videos that students can watch before conducting outdoor labs. Immersive videos increase spatial comprehension of complex lab steps that are difficult to depict and explain. In addition, to the 360-videos we developed android applications that show major lab steps in augmented reality. The augmented reality labs use precise GPS coordinates and “shows” objects in desired real-world locations. Using a tablet, students will be able to play the applications and experience major lab steps in the real environment while conducting the lab. The goal is that 360-videos and augmented reality will work in tandem and students, based on their preference, will watch the 360-videos in the field or use the augmented reality apps. The 360-videos and

augmented reality apps will be tested Fall of 2020 in a freshman class of the surveying engineering program at Penn State Wilkes-Barre.

Dillip Khatiwada Campus Affiliation: Penn State Harrisburg Major: Mechanical Engineering Anticipated Graduation: Mentors: Esfakur Rahman (Penn State Harrisburg), Oren Gall (Penn State University Park) Project Title: Design and Mechanical Properties of Total Knee Arthroplasty (TKA) using FEM Simulation The objective of the research is to evaluate and compare the changes the knee joint goes through after Total Knee Arthroplasty (TKA) for a normal weight, overweight, and obese. TKA is an orthopedic surgical procedure where the joint surfaces of the knee joint are replaced. TKA is one the most successful surgeries with a success rate of 85% to 90% but that does not mean it does not have complications. Some of the reasons for failure after the surgery includes aseptic loosening, polyethylene wear, and instability. Even with modern medicine and technology, there are still problems with the implants. There has been lots of research on the TKA but I was notable to find specific research on the effect of the knee if the patient's body changes. The current issue with TKA is patients need to get second surgery if the implant fails to do its job. Understanding how the knee joint changes after physical transformation will potentially help reduce the complications such as pain, aseptic loosening, periprosthetic fractures, and instability. A 3D model of size 3 knee joint was created using ANSYS. Three different loads are applied to the joint to simulate the stress, deformation, and flexion at 125° and 160° for a normal weight of130 lbs with BMI of 22.3, overweight of 160 lbs with BMI of 27.5 and 190 lbs with BMI of 32.6to represent obesity. The flexion/range of motion at various angles will simulate walking, cycling, and climbing stairs to have better comprehension. After comparing the result from three different loads at different angles will give better understanding for TKA patients. This research will help minimize the issues surgeons and patients go through after the TKA and help to minimize the need/cost of the second surgery.

Kaleb Knowles Campus Affiliation: Penn State Altoona Major: Electro-Mechanical Engineering and Technology Anticipated Graduation Date: December 2021 Mentors: Sohail Anwar (Penn State Altoona),Javad Khazaei (Penn State Harrisburg, Penn State University Park) Project Title: Economic Feasibility of Micro-Grid Energy Storage and the Impact of Emerging Technologies on its Viability Currently, energy storage devices show great promise when used in micro-grid applications, and further advancements in this technology will lead to economically viable and environmentally-friendly solutions in regards to residential energy consumption. Creating a 21st century energy infrastructure will be fundamental to an advancing society in the coming decades and ensuring cost-effective means of doing so will lessen the burden on the average consumer. While current research has focused primarily on fundamental battery research, the economic viability for the average American consumer has been neglected in many cases. In this work, current and future methods of home energy storage are analyzed via a thorough literature review, and the most promising current and near-future methods are explored. These methods include current Lithium Ion Battery (LIB) technology, reused LIBs from Electric Vehicles (EVs), Lithium Nickel manganese cobalt oxides (NMC) cathode composition as well as the utilization of silicon as an Anode material. After the potential of these technologies is explored, an analysis of their economic viability for the average consumer is also presented. The literature review demonstrates that the current state of LIBs is very close to economically feasible; reused LIBs are less viable than new LIBs, and future LIB compositions show great promise in viability. This shows that within the next decade micro-grids will be a reasonable alternative to utility energy harnessing techniques, and a major step towards green energy consumption will be realized.

Trevor Knox Campus Affiliation: Penn State Behrend Major: Computer Science Anticipated Graduation: May 2022 Mentors: Faisal Aqlan (Penn State Behrend), Hui Yang (Penn State University Park) Project Partner: Ethan Baxter Project Title: Multiplayer Virtual Reality Games for Manufacturing Paradigms Manufacturing is increasingly important to the engineering education in all disciplines. Several approaches can be used to introduce engineer students to manufacturing concepts. Although previous studies show the effectiveness of virtual reality (VR) for a single participant, very little has been done to focuses on manufacturing education of multiple participants with an instructor monitoring and guiding the participants. Multiplayer VR games for different manufacturing paradigms (i.e., craft production, mass production, lean manufacturing, mass customization, and personalized production) are created. The instructor hosts the VR simulations on a server, allowing the participants to use the simulation together in real time. Participants start in a central hub surrounded by multiple rooms; each simulates one manufacturing paradigm. In the VR room, participants build car toys with the instructor present to provide assistance. This hands-on approach to teaching manufacturing methods gives direct feedback to the participants, which results in a more engaging and immediate learning experience. Another benefit of the study is the cost effectiveness and the remote learning capabilities. Participants do not need to be on a factory floor, or even in person to learn manufacturing processes. With VR headsets and proper infrastructure, participants can collaborate on the completion of simulation tasks from different locations. This can also improve their teamwork and communication skills.

Poojith Kotikalapudi Campus Affiliation: Penn State Abington Major: Computer Science Anticipated Graduation: May 2023 Mentors: Vinayak Elangovan (Penn State Abington), Vittaldas Prabhu (Penn State University Park) Project Title: Obstacle Avoidance and Path Finding Systems for Autonomous Robot Navigation This paper investigates different methods to detect obstacles ahead of a robot using a camera in the robot, an aerial camera, and an ultrasound sensor. We also explored various efficient path finding methods for the robot to navigate to the target source. Methods in robot navigation, obstacle avoidance, and path finding were implemented.

Cooper Kovar Lietz Campus Affiliation: Penn State Berks Major: Electro-Mechanical Engineering Technology Anticipated Graduation: May 2022 Advisor: Azar Eslam-Panah (Penn State Berks) Project Partner: Lisa Panczner Project Title: Flapping Wings in Turbulent Flow Small birds and insects take flight by flapping and rotating their wings to create enough lift force for flight, as well as to maneuver through unpredictable gusts of wind. While several studies have been conducted to measure how birds and small insects fly in laminar flow, little is known about how these animals naturally change their wing’s flapping frequency and amplitude when experiencing turbulence. By studying a plunging, pitching, and rotating wing in a water channel facility that generates structured gusts, we can get a better understanding of what movements make the natural flight so efficient when the gusts become unpredictable. Using a parametric study, the experiments will hopefully prove that natural wing movements can be mimicked in turbulent flow, while also decreasing the amount of energy required by the wing. If successful, this knowledge can be applied to Micro Air Vehicles (MAVs), which currently use a human-designed flight system that involves rotary blades sitting atop the device. While MAVs are becoming increasingly pertinent in

today’s world, it may be time to change their design to something more natural like a flapping wing, if it means increasing their efficiency and preserving energy when the flow becomes unpredictable.

Kenjiro Lay Campus Affiliation: Penn State Abington, Penn State University Park Major: Aerospace Engineering Anticipated Graduation: May 2021 Mentor: Jose Palacios (Penn State University Park) Project Title: Passive Ice Protection for Electrically Powered Vehicles: Preliminary ANSYS Computational Fluid Dynamics Result This research investigates a novel method of ice protection specifically for electrically powered motor which is semi-passive, requiring almost no additional power. By encasing the motor coils, which are capable of reaching temperatures in excess of 250 °C, in a hollow cavity, the rotor downwash will be unable to cool the motor. Cool air will flow over the coils through an intake valve on the motor where the motor cavity will be connected to a channel in the leading edge of the rotor; through centrifugal pumping, the warm air from the motor will enter the leading-edge channel, warming the rotor surface. The inefficiencies of the motor will heat the air in the insulated cavity. Here, we simulate flow of warm air through the leading-edge cavity (15% and 25% of the upper and lower surfaces, respectively) of an NACA 0012 airfoil with a chord length of 1-inch and span of 12-inch. The airfoil is cooled convectively with coefficient of heat transfer of 10 W/(K-m2). Fluid flow was modeled using realizable K-Epsilon turbulent model and calculation was done until it converged and steady state solution was achieved. Various variables such as flow velocity, flow temperature, and environmental temperature were tested. Assuming air and wall temperature to be equal, preliminary computational fluid dynamic using ANSYS shows that this semi-passive approach would maintain the blades above freezing. When subjected to a flow velocity of 15 m/s, flow temperature of 60 °C and environmental temperature of -16 °C, blade temperature is shown to be above 2°C.

Yoon-Jae (Jake) Lee Campus Affiliation: Penn State University Park Major: Aerospace Engineering Anticipated Graduation: May 2022 Mentor: Mark Miller (Penn State University Park) Project Title: Measurement of the Fluid Velocity Fluctuation Using Hot-film Anemometry This research aims to develop a more fundamental theoretical understanding of the hot-film anemometer’s thermal response to velocity fluctuations by implementing prior hot-wire theory to examine the governing equations. The successful development of the hot-film theory will improve the efficiency of the wind turbine and increase aviation safety by detecting airfoil stall and separation. More accurately depicting the sensor’s thermal behavior leads to a more adequate circuit compensator design that allows for faster & more accurate fluid velocity fluctuation measurements. However, the hot film can conduct through the surface it is mounted on, which reduces the sensor response frequency compared to the free-standing hot-wire anemometer which also significantly complicates the governing thermal equations. The proposed method minimizes the aforementioned difficulties by (1) separating the hot-film anemometer into several components with the similar thermal behavior (2) determining the thermal behavior & characteristics of those individual components and developing their heat balance equation (3) combining the resulting heat balance equations into the governing equation. The derivation reduces the complicated heat balance equation of the hot film to the same form as that of the hot-wire system. This result allows us to apply hot-wire theory to the hot film, thereby increasing the potential applications of the hot film sensor

Sining Leng Campus Affiliations: Penn State Harrisburg and Penn State University Park Major: General Biology Anticipated Graduated Date: May 2022 Mentors: Shobha Potlakayala (Penn State Harrisburg), Sairam Rudrabhatla (Penn State Harrisburg) Project Title: Sex Determination in Plants Plants exhibit several sex forms during alternation of generations between sporophytic and gametophytic phases. Plant evolution and sex determination are known to be evolving parallelly since 470 million years. Sex determination in plants resulted in the formation of separate male and female gamete-producing structures. This phenomenon has intrigued scientists such as Linnaeus (1758), Charles Darwin (1877) to the present times. The most common sex chromosomes in plants are XY in males and XX in females. Another set of sex chromosome in plants are ZW in females and ZZ in males. X and Y chromosomes have different effective population size, rate of recombination, and exposure to natural selection. The mechanism of sex determination may differ between different plant species. The sex-linked regions on sex chromosomes, for example SvLG12 and SvLG9 in Silene latifolia is aiding in the study of evolution pathways by displaying the accumulation of repetitive sequences and degeneration of Y chromosome. The current era of genomics is investigating the evolution of sex chromosomes and sex determination genes. Current understanding shows that the formation of male and female floral organogenesis is influenced by an assortment of genes through meiotic cell divisions, transcription factors, small RNAs, hormones and environmental factors. The determinants of sexual phenotype in plants include the presence of sex chromosome (Marchantia polymorpha), hormonal regulation (corn) or pheromonal crosstalk between individual plants. The flowering plants, angiosperms include plants that produce flowers with specialized organs producing microspores or megaspores from which male and female gametophytes develop. Sex determination in these plants is known to be influenced by several external factors such as plant hormones and environments factors. Understanding the sex determination in plants will help us control the gender of economically important plants. For example, the female flowers of industrial hemp are favored for their medicinal value, and male plants of bayberry are preferred in urban parks for landscaping. Understanding the sexual phenotypes in commercially important plants can be helpful for breeding and cultivating new crop species for future generations.

Jacob Leydig Campus Affiliation: Penn State Altoona Major: Electro-Mechanical Engineering Technology Anticipated Graduation Date: May 2022 Mentors: Sohail Anwar (Penn State Altoona), Hussein Abdeltawab (Penn State Behrend), James Adair (Penn State University Park) Project Title: Optimization of 3D-Printed Parts through Manipulation of Layer Height 3D-printing/additive manufacturing is a new area of manufacturing that has many parameters which are very customizable. This customizability is one if its greatest strengths, and it allows for incredible flexibility in the manufacturing process. In order to utilize 3D-printing to its fullest, we must investigate optimizing the parameters available to us. Some of the parameters open to optimization include the speed of travel, the density of the part, and the height of the individual layers. In optimizing these parameters, we will be able to create stronger products faster with less material usage.This paper will be exploring the optimization of layer height using particle swarm optimization (PSO). In 3D-printing, there is a tradeoff between different performance indices such as consumed materials, job time, and the end product precision. Depending to the job and the user preferences, some indices may be more important than others. In 3D printing, layer height can affect these performance indices. The proposed optimizer calculates the best layer height that achieves desired combination of these indices as defined by the user. To design this optimizer, first, a mathematical model for these indices is driven. Second, a particle swarm algorithm is used to calculate the optimal layer height for the user’s preference. Using this optimizer, the user will be able to optimize different performance indices which include printing time, material usage and dimensional accuracy.

Muchen Li Campus Affiliation: Penn State University Park Major: Electrical Engineering Anticipated Graduation: May 2021 Mentor: Jeffrey Schiano (Penn State University Park) Project Title: Digital Integrating Preamplifier Design for Flux Regulation in Powered Magnets Powered magnets presently provide the highest magnetic fields for nuclear magnetic resonance (NMR) applications. A critical component of powered magnet systems is a feedback system that reduces temporal field fluctuations in the magnet caused by power supply ripple and variations in the flow rate and temperature of cooling water. The objective of this project is to improve the performance of a closed-loop flux regulator that utilizes a pickup coil to sense field fluctuations. As the voltage across the pickup coil is proportional to the derivative of the field fluctuations, the system uses an integrating preamplifier to recover field fluctuations. The existing integrating preamplifier is realized using analog electronics that causes the integrator output to drift. In turn, the feedback controller produces an undesirable drift in the magnetic field. This work aims to reduce the drift by designing a digital integrating preamplifier realized using a field-programmable gate array (FPGA)

David Lugo Gonzalez Campus Affiliation: Penn State Harrisburg Major: Mechanical Engineering Anticipated Graduation: December 2020 Mentors: Ahm Rahman (Penn State Harrisburg), Oren Gall (Penn State University Park) Project Title: Determination of Natural Frequencies for Multi-Layered Printed Circuit Boards using FEA Simulation The goal of this project was to find a relationship between natural frequency and material selection for several multi-layered Printed Circuit Board (PCB) designs. When a material is subjected to a vibrational frequency higher than its natural frequency, it can experience resonance, which in turn can cause deformation, malfunction or failure of a part, even if the stress it experiences is lower than its ultimate strength. Because PCB are a part of every modern electronics device, finding materials that raise the natural frequency of a model, allows for stronger more stable PCB designs that can withstand higher vibrational loads. To date, not much available literature exists that shows a correlation between material selection and the natural frequency of a PCB design. To study of this problem, Ansys™ software was used to perform modal analysis, which yields natural frequencies of a model. The designs studied typically consisted of three layers, the outer layers being varied between copper, aluminum, magnesium and gold, while the middle layer was kept constant as FR-4 composite material. Transistors modeled out of silicon material were added to some designs to model somewhat realistic PCB. The results of this analysis did not suggest a perfect correlation between material selection and natural frequency. There was a general trend however, that showed that modeling the outer layers out of copper, typically resulted in a higher natural frequency than that of the other materials. The deformed shapes of the models were almost identical between all four types of materials. The only variation most times was the frequency needed to obtain that deformation. Copper required a higher frequency to cause such deformation more often than the other materials, but not every time. This research will allow for more durable PCB to be designed by suggesting which materials can raise the natural frequency of a model. Nevertheless, there is plenty of space open for more research in this area. An aspect that may be subject of future research for PCB natural frequencies is the choice of materials. While this research focused on four materials for the layers, there are many others that can be explored. Further study in this area is crucial in order to continue to improve PCB designs.

Jay Madaka Campus Affiliation: Penn State Abington Major: Biomedical Engineering Anticipated Graduation: May 2023 Mentors: Mukul Talaty (Penn State Abington), Pratima Saravanan (Penn State University Park) Project Title: Using computer simulation to run induced acceleration analysis on an amputee patient Human gait (walking) has always been the corner stone for human movement. In studying gait, the contribution each joint moment and muscle, in lower extremities, has to the acceleration of the person is very important in determining defects or irregularities that than can be corrected with an assistive device. For our project, we attempted to determine the contributions of these joint moments in an amputee patient. Using a computer software called Open Sim that we downloaded, my mentor and I attempted to find the answer to our question using computer simulation. After completing all the basic tutorials and finding the necessary inputs and outputs, we were ready to use a model of a person with certain specifications to calculate the acceleration in each individual joint moment and measure their contributions to the overall acceleration of the patient’s walking, with the implementation of our amputee motion capture data.

Ian McGorrey Campus Affiliation: Penn State Abington Anticipated Graduation: May 2023 Mentors: Mukul Talaty (Penn State Abington), Ellen Zerbe (Penn State University Park) Project Title: Review of Printing Parameters for 3D Printed Lower Extremity Prosthetic Sockets 3D Printing and prosthetics are two things that have become closer and closer together in recent years since the many advances of 3D printing allowing it to be a viable option for this high strength application. The goal of this paper was to create a background of knowledge in which other papers regarding the 3D printing of prosthetics could be based which was done by reviewing papers completed by other researchers in the 3D printing field, specifically targeting papers that dealt in the creation of parts with strong tensile strength and strong mechanical strength. Based off of the papers we reviewed, we were able to determine optimal print settings for 3D printed sockets for future research however, due to the nature of 3D printing and the extreme variety of 3D printer settings, not all of the strength effecting settings were able to be looked in to due to time constraints of the project. In future research work, we will look more into other strength effecting settings as well as run our own testing of 3D printed lower prosthetic sockets.

Megan McRandal Campus Affiliation: Penn State Behrend Major: Industrial Engineering Anticipated Graduation: May 2021 Mentors: Faisal Aqlan (Penn State Behrend), Hui Yang (Penn State University Park) Project Partner: Alexis Rimpa Project Title: Investigating the Effectiveness of Visual Instructions for Teaching Manufacturing Paradigms To enhance students’ understanding of engineering concepts, there is an urgent need for educators to leverage comprehendible methods to best represent complex ideas, concepts, and theories. In comparison with written instructions, visual instructions can better clarify concepts that are difficult to explain by using only words. This research compares learning methods by creating visual and written instructions for different manufacturing systems. The two types of instructions are analyzed for content and learning using pre- and post-surveys. Five different manufacturing simulations are developed, one for each manufacturing paradigm (i.e., craft production, mass production, lean manufacturing, mass customization, and personalized production). The instructions help improve learning effectiveness of manufacturing systems concepts and it is expected that visual instructions can help students focus thoughts and ideas and think critically. To investigate how visual instructions will guide students, statistical analysis will be conducted to identify the effectiveness of the proposed visual instructions.

Hannany Md Salehuddin Campus Affiliation: Penn State University Park Major: Aerospace Engineering Anticipated Graduation: May 2021 Mentor: Jose Palacios (Penn State University Park) Project Title: Predictions of Ice Accretion on EVTOL Rotor Vehicles: Preliminary Analysis of Aerodynamics Properties using Blade Element Momentum Theory Electric Vertical Takeoff and Landing (eVTOL) rotor vehicles have recently attained increasing focus and popularity for its projected use like air taxis and aerial surveillance. As they are susceptible to icing because of its small size, the performance of rotor vehicles must be tested in icing conditions. The goal of this preliminary study is to determine the aerodynamic properties needed to replicate icing conditions of the rotor vehicle in hover with the same rotor configuration in forward flight. An empirical analysis involving blade element momentum theory (BEMT) using MATLAB was developed to calculate the resultant speed experienced by the NACA 0015 rotor. In this analysis, the rotor blade was divided into 16 equally spaced blade segments and the azimuth angle of 6.08° for each blade movement. The angle of attack variations of the rotor cross sections was also investigated.

Benjamin Moorehead Campus Affiliation: Penn State Behrend Major: Computer Science Anticipated Graduation: May 2022 Mentors: Omar Ashour (Penn State Behrend), Christopher McComb (Penn State University Park) Project Partner: Jacob Smith Project Title: Developing Virtual Reality (VR) Learning Module to Teach Manufacturing Systems Virtual Reality (VR) enables the user to learn about their environment by experiencing it firsthand. VR enhances visualization and interaction. Industrial Engineering (IE) curriculum focuses on understanding systems concepts. Some of these concepts are abstract and difficult to understand. Therefore, it is often difficult for Industrial Engineering students to succeed in their major when the material is being presented via traditional teaching approaches. By giving these students a chance to learn using experiential learning rather than just seeing equations on a board, it improves the chance at understanding and exceling as well as engagement in learning. To achieve this, this project aims to design and develop a VR-based learning module to teach manufacturing systems concepts. The current module involves basic statistics and probability concepts. The user has access to instructions, tools (such as stopwatch and calculator), and a way to verify his/her answers. The VR learning modules are hypothesized to increase the student’s motivation and learning performance.

Naufal Murtadza Campus Affiliation: Penn State Harrisburg Major: Civil Engineering Anticipated Graduation: May 2022 Mentors: Rajarajan Subramanian (Penn State Harrisburg), Farshad Rajabipour and Melika Sharifironizi (Penn State University Park) Project Partner: Sahad Rafiuzzaman Project Title: Evaluation of Self-Consolidating Concrete Mixtures used with Mineral and Chemical Admixtures In this study, we evaluate past academic research on “Self-Consolidating Concrete (SCC) that uses different combinations of mineral and chemical admixtures to achieve similar fresh properties and hardened properties of conventional Portland Cement Concrete (PCC) with flowable characteristics. The mineral admixtures such as fly ash, slag, metakaolin and Rice Husk Ash (RHA) are the waste byproduct materials usually dumped in landfills. Previous studies mainly discussed the effects of fly ash and superplasticizers on improving the workability and flowability of SCC. The use of high-volume fly ash (HVFA) replacements in concrete have generally been known to encounter problems such as low early strength developments and delayed setting time during cold weather. Through the evaluation of the mineral

admixtures such as slag, silica fume, metakaolin, and RHA, it was found that the toughness, stability, and durability of the SCC mixtures were improved when combined with HVFA. Different combinations of admixtures in SCC have shown varying results, such as changing the concentration of certain admixtures may invalidate the concrete mix that it no longer can be classified as SCC. On the other hand, experiments have also shown that the use of optimum SCC mixture combinations improved the fresh and hardened concrete properties. The maximized utilization of fly ash content in concrete with the addition of slag, silica fume, and RHA to achieve SCC properties will reduce the demand for cement mining, hence significantly reducing the carbon footprint of the industry. Also, the use of the byproduct materials such as fly ash, slag, metakaolin and RHA may lower the cost of producing usable concrete, thus can prevent those byproducts from being dumped into the landfills.

Kazi Nafis Campus Affiliation: Penn State Lehigh Valley Major: Aerospace Engineering Anticipated Graduation: May 2023 Mentors: Tracey Carbonetto (Penn State Lehigh Valley), Arash Khoshkbar-Sadigh (Penn State University Park) Project Title: Machine Learning for Marine Engines Machine learning has become an important process in many areas of engineering. As technology throughout the world progresses, the process of automation becomes far more common. Scientists must rely on machines to identify trends, relationships and other critical attributes gathered from large data sets. In engineering processes that depend on programmable logic controllers (PLC) to control the inputs and outputs, the PLC is now recognized as a barrier to optimizing the process. Engineers across the globe are transferring the role of a PLC to the machine learning process in order to further efficiency. This research is focused on a specific application of a four-stroke, diesel-electric, medium-speed Wartsilla engine that powers the Moving Vessel (M/V) Sulphur Enterprise, a U.S.-flagged tanker ported in Tampa, Florida. Marine engineers who crew the engine room on this vessel monitor the performance of all engines on board with emphasis on the main engine. Data is generated on a 24-hour continuous cycle with engineers reviewing the data daily. When timing and phase shift discrepancies are evident, an engineer will either mechanically increase or decrease the fuel intake, change out the valves or the fuel pump itself. Many varying factors make the overall process inefficient. Engineering students will study the data received from the Sulphur Enterprise and look for trends between the many data points. The students will then create a theoretical program loop that would help create a more dynamic and efficient system. The program will indicate when fuel intake needs to be increased or decreased and which pump needs to be adjusted. The theoretical feedback loop will study the general trends among various inputs such as load (break horsepower), RPM, fuel rack (opening for the fuel delivery system), and pressure to create a predictive system for the adjusting process. The research indicates many trends across all data points which can potentially be used to create an optimizing feedback loop. Further research is needed to design the general structure of a machine learning feedback loop for this process. The purpose and impact of this research is to identify the marine industry as one which extensive efforts into applying machine learning processes would benefit not only the organizations who rely on these vessels to perform optimally but also to make gains in reducing the emissions from large marine vessels.

Bogdan Nazaryan Campus Affiliation: Penn State Berks Major: Mechanical Engineering Anticipated Graduation Date: Fall 2023 Mentors: Nathan Rungun (Penn State Berks), Aman Haque (Penn State University Park) Project Title: Electricity generation from body movement The purpose of this project is to study the various biomechanical energy harvesting methods, assess the potential of interconnecting multiple energy harvesting processes, and design/update energy harvesting devices/systems that will be capable of collecting and storing movement-based generated energy. Using research papers, online videos, educational websites, and small personal experiments I learned about different biomechanical energy harvesting methods and learned about the physics that work behind the scenes to make existing energy harvesting machines operate. I was able

to learn about the basic and most often used energy harvesting principles, such as electro-magnetism, piezo-electricity, and tribo-electricity. Additionally, I created designs for several new movement-based energy harvesting devices, which utilize the learned principles. Two directions offer interesting avenues of study. Firstly, testing the machine designs I have made, to assess their capabilities and short comes for improvement is a task I plan to pursue throughout the next year. Secondly, movement-based energy harvesting machines were examined during this project. However, the area that studies thermodynamic-based energy harvesting was left almost entirely unexamined. What I did learn seems promising for research, particularly due to a property displayed by the thermoregulatory system of the body to almost constantly emit heat, possibly allowing for constant energy capture.

David Neilson Campus Affiliation: Penn State Wilkes-Barre Major: Surveying Engineering Anticipated Graduation: May 2021 Mentors: Dimitrios Bolkas (Penn State Wilkes-Barre), Alexander Klippel (Penn State University Park) Project Partners: Jason Kepner, Luke Kepner Project Title: Enhancing Outdoor Lab Instruction with Immersive Technologies Surveying engineering education requires extensive practice with instruments and equipment that are used for outdoor data collection of 3D spatial datasets. As technology advances, surveying techniques and practices become complicated. Typical introductory labs involve the instructor providing an overview of the instrument in class and then explaining how the students need to complete the laboratory before they go outdoors. However, students often have questions that arise during the laboratory, but it is difficult for the instructor to assist all groups in a timely manner as groups work on different parts of the campus. This creates unique instructional challenges and frustrates students, making outdoor laboratories unpleasant. To address these challenges, we created instructional material using immersive technologies. Using 360-cameras we have developed numerous instructional videos that students can watch before conducting outdoor labs. Immersive videos increase spatial comprehension of complex lab steps that are difficult to depict and explain. In addition, to the 360-videos we developed android applications that show major lab steps in augmented reality. The augmented reality labs use precise GPS coordinates and “shows” objects in desired real-world locations. Using a tablet, students will be able to play the applications and experience major lab steps in the real environment while conducting the lab. The goal is that 360-videos and augmented reality will work in tandem and students, based on their preference, will watch the 360-videos in the field or use the augmented reality apps. The 360-videos and augmented reality apps will be tested Fall of 2020 in a freshman class of the surveying engineering program at Penn State Wilkes-Barre.

Natalie Neptune Campus Affiliation: Penn State Harrisburg Major: Civil Engineering Anticipated Graduation: May 2023 Mentors: Shirley Clark, Farrah Moazeni (Penn State Harrisburg), Chen Zhang (Penn State University Park) Project Title: Climate Change Increased Inter-Event Periods Impact on the Behavior of Metal-ligand Complexes in Stormwater Retention Facilities In Pennsylvania, climate change is anticipated to increase the number of dry days between precipitation. These dry days will increase the time stormwater sits in stormwater retention systems. As a result, processes such as resuspension, sedimentation, chemical reactions, and coagulation are occurring and potentially cycling, which affect the settleability, filterability and bioavailability of the discharged water. This research seeks to improve the understanding of metal complexes with ligands (such as nitrate and phosphate in stormwater and chlorides found in snowmelt runoff) that are common in stormwater, especially during times of extended detention. Complexation is anticipated to happen more due to climate change as the time increases that this water sits in detention systems. The creation of complexes negatively affects the treatment systems because the systems were designed to remove the ionic forms of the metals. Metals complexes in stormwater have not been studied, whether at equilibrium or the kinetics of the system until equilibrium has been reached. Changes in certain factors, such as pH, dissolved organic matter (DOM), temperature, and chloride

and their impacts on metal complexes have not been well studied in detention systems. In this research project, the equilibrium model Visual Minteq calculated the different complex concentrations using the data from the National Stormwater Quality Database for the ligand and metal quantities. A review of the data showed that most of the ions had a charge of –1 or +1, not the +2 charge of the uncomplexed metal ion. Using a factorial analysis of these 4 factors, Minitab was used to find which factors were significant in predicting the percentage of complexes in each of the two charge groups. Chloride and DOM were the two factors that affected the percentage of copper and zinc complexes in each of the two charge categories. Since chloride is a salt, this indicates the high possibility that road salt contributes heavily to the increase of metal complexes forming, which then could affect its treatability. DOM from the decomposition of organic materials such as plants and living organisms also has a large impact on metal complexation. This impact would be year-round, as opposed to the winter impacts of chloride. These results address a part of the gap in research to understand what affects the ability of treatment systems to clean up the pollutants from stormwater. This also will help increase understanding of the interactions of metals and ligands, especially when quiescent time is available for these systems to go to chemical equilibrium.

Bryant Niederriter Campus Affiliation: Penn State Behrend Major: Computer Science Anticipated Graduation: May 2022 Mentors: Faisal Aqlan (Penn State Behrend), Hui Yang (Penn State University Park) Project Title: Development of a Virtual Mental Health Clinic Current diagnosis of mental disorders is mainly based on interviews and questionnaires, which can be influenced by interviewers’ and self-report bias. This study focuses on testing the effectiveness of sensor-based virtual reality (VR) technology to assist in the diagnosis of mental disorders such as Alzheimer’s, schizophrenia, and dementia, as well as the improvement of medical training for care and treatment of these disorders. The VR environments consist of tasks for the user to perform and implement eye tracking to analyze patterns in a healthy sample and affected participants. The research intends to create useful tools to assist in diagnosing and modeling mental disorders to improve the decision-making process.

Gregory Nutter Campus Affiliation: Penn State Behrend Major: Mechanical Engineering Anticipated Graduation: May 2021 Mentors: Adam Hollinger (Penn State Behrend), Chris Rahn (Penn State University Park) Project Partner: Tanay Raje Project Title: Drone Battery Replacement Research Drones create the opportunity for objects to be delivered to previously un-deliverable places within a matter of minutes or hours. From medicine to commercial, from delivery agriculture products to kid's toys. These drones can fast-track society from two-day shipping to one-day, while still being a fraction of the cost. However, as society continues its course to achieving perfection, there are many setbacks that we must face. In the case of drones, comes the problem that all things face one way or another, time. A drone is confined to the length and distance dictated by its battery. In this research, our team worked to search for alternative methods to deliver power to the drone while increasing its lifespan. Modem drones are largely powered by a single centrally placed Lithium-Polymer battery. These heavy, yet powerful batteries are, in our opinion, obsolete compared to the power offered by light and less potent Lithium-Ion battery. Lithium-Ions are significantly weaker than Lithium-Polymer batteries, however, basic electronic background tells us that by creating a system of these batteries, we can still create the same power output while reducing the weight of the drone. These batteries, due to their slim geometry could be placed into redesigned arms and spread out throughout the drone. By doing so, we add the additional attribute of increased strength, allowing the drone to withstand a greater impact. In order to proceed with such an endeavor, we began by disassembling and redesigning the drone in order to allow a quick transition from a Lithium- Polymer to Lithium-Ion and proceeded with testing the Lithium-Polymer

batteries in order to get a baseline origin. Following this, we tested the Lithium-Ion batteries and compared the results to either verify or deny our hypothesis.

Ayodeji Odetola Campus Affiliation: Penn State Altoona Majors: Computer Science, Data Science, Applied Mathematics Anticipated Graduation Date: May 2022 Mentors: Suman Saha (Penn State Altoona), Mahfuza Farooque (Penn State University Park) Project Partner: Devon Reed Project Title: Forecasting Product Demand From Historical Data Using Machine Learning Methods. Recently, time series forecasting has become a subject of interest for many data scientists and researchers alike. Both statistical and machine learning methods have been applied to time series data in order to predict future phenomena like the weather and stock prices. In our global climate, one avenue of time series forecasting that continues to become more imperative is the prediction of the demand for perishable goods. Each year, vast quantities of food are wasted globally with the number continuing to grow. To abate this crisis, an accurate time series model could be used to predict the number of resources needed at the store level. However, current forecasting models are plagued with inaccuracies and are only utilized as decision-making tools for upper-level management who ultimately determine the final decision. We propose an exploratory approach by evaluating different models on their ability to predict market demand. As a baseline, a statistical model is included to measure if the machine learning models show superior predictive capability. We observed that the machine learning models performed slightly better than the statistical baseline. This shows that machine learning has the capability of better predicting market demand than statistical models.

Lisa Panczner Campus: Penn State Berks Major: Electro-Mechanical Engineering Technology Anticipated Graduation: May 2021 Advisor: Azar Eslam-Panah (Penn State Berks) Project Partner: Cooper Kovar Lietz Project Title: Flapping Wings in Turbulent Flow Small birds and insects take flight by flapping and rotating their wings to create enough lift force for flight, as well as to maneuver through unpredictable gusts of wind. While several studies have been conducted to measure how birds and small insects fly in laminar flow, little is known about how these animals naturally change their wing’s flapping frequency and amplitude when experiencing turbulence. By studying a plunging, pitching, and rotating wing in a water channel facility that generates structured gusts, we can get a better understanding of what movements make the natural flight so efficient when the gusts become unpredictable. Using a parametric study, the experiments will hopefully prove that natural wing movements can be mimicked in turbulent flow, while also decreasing the amount of energy required by the wing. If successful, this knowledge can be applied to Micro Air Vehicles (MAVs), which currently use a human-designed flight system that involves rotary blades sitting atop the device. While MAVs are becoming increasingly pertinent in today’s world, it may be time to change their design to something more natural like a flapping wing, if it means increasing their efficiency and preserving energy when the flow becomes unpredictable.

Jung Eun Park Campus Affiliations: Penn State Abington, Penn State University Park Major: Aerospace Engineering Anticipated Graduation: May 2021 Mentor: Namiko Yamamoto (Penn State University Park) Title: Relationship Between Nanofiller Structures and Transport Properties of Maghemite-Thermoset Composites Polymer nanocomposite implementation to carbon fiber reinforced plastic (CFRP) can improve multi-functional properties such as high electrical and thermal conductivity for electrostatic dissipation and lightning strike protection.

Such transport properties can be enhanced by providing percolation and minimum inter-particle contacts. Ina previous study with maghemite-thermoset composites, experimentally observed transport properties were smaller than expected due to thermal resistance at inter-particle contact. Yet, the incomplete relationship study of nanofiller structures and transport properties makes the trend harder to understand. In this work, micro-CT scanned images of maghemite-thermoset composites were utilized to quantitively compare the measured electrical and thermal properties with their nanoparticle structures. Here, image processing software, ImageJ, was employed to obtain agglomerate areas and correlate with the number of inter-particle contacts.

Aayushi Patel Campus Affiliation: Penn State Harrisburg Major: Biology Anticipated Graduation: May 2023 Mentors: Shobha Devi Potlakayala and Sairam Rudrabhatla (Penn State Harrisburg), Wayne Curtis (Penn State University Park) Project Title: Advancement and Commercialization of Transgenic Crops Using Genome Editing Technologies Agriculture has supported human life through civilizations despite several biotic (pests, pathogens), abiotic (drought, cold) stresses posing a challenge to meet the ever-increasing global food demand. Understanding the laws of genetics led to conventional plant breeding in the early 20th century giving rise to improved plant varieties. In the past 50 years, the understanding of cellular and molecular mechanisms in plants led to novel innovations in biotechnology for introducing desired genes/traits through plant genetic engineering. Commercial development of genetically modified crops such as tomatoes, corn and soybean led to 185.1 million hectors of global land use in 2016 thereby accelerating the process of conventional breeding to sustain the nutritional need of humans. Most recently, targeted genome editing technologies such as Zinc Finger Nucleases (ZFNs), Transcriptor Activator-Like Effector Nucleases (TALENs), and Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) systems have emerged as powerful tools for crop improvement. For example, genome editing with CRISPR is proving to be an efficient, simple process that involves low cost, can target multiple genes and that can be applicable to most plants. CRISPR is currently being used to engineer plant metabolic pathways to create resistance to viral, bacterial, or fungal pathogens, or abiotic stresses such as drought and cold in preparation for the global nutritional and food security. These novel genome editing technologies are poised to meet the UN sustainable development goals such as zero world hunger and good human health and well-being. In addition, these technologies could be more efficient in developing transgenic crops and speed up the mandatory regulatory approvals and risk assessments conducted by the US Departments of Agriculture (USDA), Food and Drug Administration (FDA), Environmental Protection Agency (EPA).

Serenah Pauliuc Campus Affiliation: Penn State Berks Major: Mechanical Engineering Anticipated Graduation: May 2022 Mentors: Charles Bakis (Penn State University Park), Amir Barakati (Penn State Berks) Project Title: Effect of Moisture on the Tensile Properties of Composites with Bio-based Fibers and Matrix Fibers and matrices made in full or part with plant- or bio-based materials are attracting increased attention in the composites industry on account of their potential for environmental sustainability. However, many bio-based constituents are known to be susceptible to moisture absorption and associated degradation. Additionally, the fibers are naturally hydrophilic while the polymer matrices are hydrophobic, which results in poor strength at the fiber/matrix interface. Investigation is needed on the mechanical performance of composites made with new bio-based resin systems as they become available. This project provides an opportunity to process and characterize existing laminates and their tensile properties. The objectives of this investigation are to evaluate the quasi-static tensile behavior of composites made with different combinations of fibers and matrices, in the as-received condition and following a 24-hour soak in water at 66°C. The fiber materials investigated include woven jute and E-glass. The matrix materials include a relatively new plant-based epoxy (Super Sap ONE/ONF), a conventional bisphenol A epoxy cured with an aliphatic amine. The composites were previously manufactured using vacuum-assisted resin transfer molding. Fiber and void

volume contents were determined for the composites. Tensile strength and modulus were measured on as-received and water-soaked composites. After water exposure, the jute fiber composites suffered a large reductions in modulus with either type of matrix and a large reduction in strength with the bio-based matrix but not the synthetic matrix. The E-glass composite with the synthetic matrix, which was only tested in the as-received condition, had much higher modulus and strength in comparison to the jute fiber composites.

Travis Peters Campus Affiliation: Penn State Harrisburg, Penn State University Park Major: Chemical Engineering Anticipated Graduation: May 2022 Mentors: Shirley Clark and Faegheh Moazeni (Penn State Harrisburg), Stephen Lynch (Penn State University Park) Project Title: Investigating the Heat Transfer through a Crumb Rubber Green Roof Media Heat Transfer Analysis of Crumb Rubber in green roofs use vegetation and a media layer to reduce the heat transfer from solar radiation to the building envelope. These result in energy savings are a complex roofing material that allow for annual energy savings for any building when constructed correctly. The design of these green roofs often has many media layers including its soil and vegetation as are the top layers that to decrease summertime heat from entering the building, thus reducing and increasing energy consumption for cooling. However, the media itself important sub layers, such as crushed shale ore. The crumb rubber in this study, are can be equally as important to reducing the heat transferred into the building. The effect of understanding of a media material using crumb rubber ’s effect on heat transfer through a green roof is studied in this work for the first time has not been previously studied. Previously recorded temperature data at different depths of crumb rubber was used alongside thermal conductivity equations to model crumb rubber’s conductivity. The simulation program, EnergyPlus, was also used to attempt simulations of a green roof setting using crumb rubber to directly see examine its effects on energy savings from lowering heat transfer into the building. Future research will expand Further work with simulations in EnergyPlus would be helpful to see investigate the exact results performance of crumb rubber in green roofs in different weather and building types for reducing energy usage. The conclusion of crumb rubbers effects on green roof’s efficiency will directly help create better green roofs with energy consumption decreasing as well as benefits towards carbon reduction and solutions to the urban heat island effect.

Ashida Pitt Campus Affiliation: Penn State Harrisburg Major: Mechanical Engineering Anticipated Graduation: May 2021 Mentors: Esfakur Rahman (Penn State Harrisburg), Roger Walker (Penn State University Park) Project Title: Evaluating Reinforcement Factor ξ For Hemp Fiber Reinforced Composites A computational analysis is conducted to estimate geometry parameter or reinforcement factor, ξ using the Halpin-Tsai method. With advances being made and more options are becoming available, hemp fiber is surfacing amongst the many options we have in natural fiber. Hemp fiber is more economical to resource; it is renewable and can be cultivated at any time. There are many mathematical models available in the literature to estimate micromechanics of fiber reinforced poly composites. Semiempirical method developed by Halpin-Tsai is one of the common methods to predict the composite properties. A number of empirical equations available in the literature to predict the value of ξ. Halpin-Tsai method is easy to apply and provide better prediction, however, depends on empirical data to evaluate the reinforcement fact, ξ. It is known that the reinforcement factor ξ has a broad range, varying 0< ξ < . A modified version of the Halpin Tsai model can make predictions of moduli better by changing the geometry parameter. In this research, a computational approach is done. The reinforcement factor, ξ was determined using Halpin-Tsai model and from the experimental results for hemp fiber composites. A comparison has been made to validate the effectiveness of the model. The volume fraction of both matrix and fiber are chosen from low to high percentages and fiber orientation for this research was taken at 0° and 90°. With the Halpin Tsai reinforcement factor, it is seen that larger volume fractions deviate results further away from experimental and calculated results. This approach shows with the improvement of

the model, calculating the geometry parameter for the moduli of the composites would be a recommended method when calculating moduli.

Sahad Rafiuzzaman Campus Affiliation: Penn State Harrisburg Major: Civil Engineering Anticipated Graduation: May 2022 Mentors: Rajarajan Subramanian (Penn State Harrisburg), Farshad Rajabipour and Melika Sharifironizi (Penn State University Park) Project Partner: Naufal Murtadza Project Title: Evaluation of Self-Consolidating Concrete Mixtures used with Mineral and Chemical Admixtures In this study, we evaluate past academic research on “Self-Consolidating Concrete (SCC) that uses different combinations of mineral and chemical admixtures to achieve similar fresh properties and hardened properties of conventional Portland Cement Concrete (PCC) with flowable characteristics. The mineral admixtures such as fly ash, slag, metakaolin and Rice Husk Ash (RHA) are the waste byproduct materials usually dumped in landfills. Previous studies mainly discussed the effects of fly ash and superplasticizers on improving the workability and flowability of SCC. The use of high-volume fly ash (HVFA) replacements in concrete have generally been known to encounter problems such as low early strength developments and delayed setting time during cold weather. Through the evaluation of the mineral admixtures such as slag, silica fume, metakaolin, and RHA, it was found that the toughness, stability, and durability of the SCC mixtures were improved when combined with HVFA. Different combinations of admixtures in SCC have shown varying results, such as changing the concentration of certain admixtures may invalidate the concrete mix that it no longer can be classified as SCC. On the other hand, experiments have also shown that the use of optimum SCC mixture combinations improved the fresh and hardened concrete properties. The maximized utilization of fly ash content in concrete with the addition of slag, silica fume, and RHA to achieve SCC properties will reduce the demand for cement mining, hence significantly reducing the carbon footprint of the industry. Also, the use of the byproduct materials such as fly ash, slag, metakaolin and RHA may lower the cost of producing usable concrete, thus can prevent those byproducts from being dumped into the landfills.

Tanay Raje Campus Affiliation: Penn State Behrend Major: Mechanical Engineering Anticipated Graduation: May 2021 Mentors: Adam Hollinger (Penn State Behrend), Chris Rahn (Penn State University Park) Project Partner: Gregory Nutter Project Title: Drone Battery Replacement Research Drones create the opportunity for objects to be delivered to previously un-deliverable places within a matter of minutes or hours. From medicine to commercial, from delivery agriculture products to kid's toys. These drones can fast-track society from two-day shipping to one-day, while still being a fraction of the cost. However, as society continues its course to achieving perfection, there are many setbacks that we must face. In the case of drones, comes the problem that all things face one way or another, time. A drone is confined to the length and distance dictated by its battery. In this research, our team worked to search for alternative methods to deliver power to the drone while increasing its lifespan. Modem drones are largely powered by a single centrally placed Lithium-Polymer battery. These heavy, yet powerful batteries are, in our opinion, obsolete compared to the power offered by light and less potent Lithium-Ion battery. Lithium-Ions are significantly weaker than Lithium-Polymer batteries, however, basic electronic background tells us that by creating a system of these batteries, we can still create the same power output while reducing the weight of the drone. These batteries, due to their slim geometry could be placed into redesigned arms and spread out throughout the drone. By doing so, we add the additional attribute of increased strength, allowing the drone to withstand a greater impact. In order to proceed with such an endeavor, we began by disassembling and redesigning the drone in order to allow a quick transition from a Lithium- Polymer to Lithium-Ion and proceeded with testing the Lithium-Polymer

batteries in order to get a baseline origin. Following this, we tested the Lithium-Ion batteries and compared the results to either verify or deny our hypothesis.

Joseph Razon Campus Affiliation: Penn State Abington Major: Chemical Engineering Anticipated Graduation: May 2023 Mentors: Burcu Ozden (Penn State Abington), Shengxi Huang (Penn State University Park) Project title: Progress on Characteristically Induced Proton-Irradiated 2DMs: A Compressed Review The surge of micro-thin two-dimensional materials (2DMs) provides a promising outlook on a wide variety of applications in radiation hard environments such as space radiation environment, high-altitude flights, military aircraft, satellites, nuclear reactors, and particle accelerators. They offer a relatively new set of unique chemical/physical attributes which includes bandgap tunability, chemical composition variation, and high Young’s modulus despite their atomically thin and light structure. However, the particle radiation significantly affects these properties even though 2DMs show great potential for space applications. A current trend in the field of 2DMs includes irradiation with high energy protons. It is of great importance to further investigate the effects of proton irradiation on electrical, structural, and even magnetic characteristics of these materials. This paper aims to focus on the review of the effects of proton-irradiation on 2DMs, with special focus on MoS2, WSe2, and Graphene.

Devon Reed Campus Affiliations: Penn State Altoona, Penn State University Park Majors: Computer Science, Applied Mathematics Anticipated Graduation Date: May 2022 Mentors: Suman Saha (Penn State Altoona), Mahfuza Farooque (Penn State University Park) Project Partner: Ayodeji Odetola Project Title: Forecasting Product Demand From Historical Data Using Machine Learning Methods. Recently, time series forecasting has become a subject of interest for many data scientists and researchers alike. Both statistical and machine learning methods have been applied to time series data in order to predict future phenomena like the weather and stock prices. In our global climate, one avenue of time series forecasting that continues to become more imperative is the prediction of the demand for perishable goods. Each year, vast quantities of food are wasted globally with the number continuing to grow. To abate this crisis, an accurate time series model could be used to predict the number of resources needed at the store level. However, current forecasting models are plagued with inaccuracies and are only utilized as decision-making tools for upper-level management who ultimately determine the final decision. We propose an exploratory approach by evaluating different models on their ability to predict market demand. As a baseline, a statistical model is included to measure if the machine learning models show superior predictive capability. We observed that the machine learning models performed slightly better than the statistical baseline. This shows that machine learning has the capability of better predicting market demand than statistical models.

Alexis Rimpa Campus Affiliation: Penn State Behrend Major: Industrial Engineering Anticipated Graduation: May 2021 Mentors: Faisal Aqlan (Penn State Behrend), Hui Yang (Penn State University Park) Project Partner: Megan McRandal Project Title: Investigating the Effectiveness of Visual Instructions for Teaching Manufacturing Paradigms To enhance students’ understanding of engineering concepts, there is an urgent need for educators to leverage comprehendible methods to best represent complex ideas, concepts, and theories. In comparison with written instructions, visual instructions can better clarify concepts that are difficult to explain by using only words. This research compares learning methods by creating visual and written instructions for different manufacturing systems. The two

types of instructions are analyzed for content and learning using pre- and post-surveys. Five different manufacturing simulations are developed, one for each manufacturing paradigm (i.e., craft production, mass production, lean manufacturing, mass customization, and personalized production). The instructions help improve learning effectiveness of manufacturing systems concepts and it is expected that visual instructions can help students focus thoughts and ideas and think critically. To investigate how visual instructions will guide students, statistical analysis will be conducted to identify the effectiveness of the proposed visual instructions.

Carlos Rodriguez Campus Affiliation: Penn State Harrisburg, Penn State University Park Major: Aerospace Engineering Anticipated Graduation: May 2022 Mentors: Anilchandra Attaluri (Penn State Harrisburg), Puneet Singla (Penn State University Park) Project Title: Alternative Methods of Power: Improving CubeSat Thermal Control CubeSats are cost efficient and easy to produce small-scale satellites. They generally have dimensions of 10 cm x10 cm x10 cm or 1U and weight less than 1.3 kg. Commonly used CubeSat frames are 1U, 2U, 3U or 6U, “U” referring to unit. CubeSats have a variety of applications such as remote sensing, communications, and are now being used for interplanetary missions. All these applications need their electro-mechanical components to be maintained under certain temperatures for optimal performance. The continuous increasing power density for the electronics are generally managed using active cooling technologies such as electric heaters, heat pumps, and cryocoolers. This, however, can cause problems with reliability and functionality. In addition, the constant required power input for active technologies reduces the capability of the CubeSat by requiring more surface area to dissipate the additional waste heat, therefore increasing its size and weight. Passive Cooling technologies on the other hand require no power input and are more stable because of the lack of any moving parts. Initially, CubeSats only required passive methods to manage thermal loads (insulation, heat pipes, surface coating), but as time proceeded and nano-satellites were being developed with more power, active methods of temperature management became more and more necessary. This introduces the question of whether or not we can maximize the power output of the CubeSat without having to utilize any active cooling and heating technologies, thus maintaining an ideal weight and increasing the reliability by getting rid of moving parts. There were obvious constraints on my research caused by the current crisis, which hindered me in my ability to perform any hands-on tests and access a lab. However, I was able to forward my research through the plethora or articles, documents and references I read through as well and the guidance provided to me by my mentors Drs. Attaluri and Singla. Together, we were able to design a combination of advanced cooling technologies by incorporating passive thermal control techniques and advanced cooling technologies and figuring out composition works best to accomplish our goal. The severity of our research’s importance as well as the impact it will have carries a direct correlation with the increasing popularity of CubeSats and their importance as well.

Camilo Andres Rojas-Balli Campus Affiliation: Penn State University Park Major: Civil Engineering Anticipated Graduation: May 2021 Mentor: William Burgos Project Title: Exploration of origin and remediation techniques of taste and odor issues associated with drinking water in Bucaramanga, Colombia. Reservoir management in developing countries has been and continues to be explored as issues arise from the construction of large dams. For instance, naturally occurring compounds, such as 2-methylisoborneol (MIB), geosmin, are causing taste and odor(T&O) issues in reservoirs recently built in several countries including China and Colombia. These compounds are released by cyanobacteria during environmentally stressful conditions including changes in temperature and nutrient levels. However, these T&O issues have sparse case studies regarding its mitigation techniques. Bucaramanga, a city with over one million residents in the northeastern region of Colombia has been experiencing a seasonal T&O issue since the construction of the Bosconia reservoir in 2015.Through a collaboration with the water authorities in Bucaramanga (AMB)and faculty at Pontifical Bolivarian University Bucaramanga this study

investigates the possible origins of the cyanobacteria and identifies potential causes of the release of MIB and geosmin. The goal of this project is to synthesize dataand present findings to the AMB including possible remediation techniques intended to alleviate the T&O issues impacting over one million citizens of Bucaramanga.

Chase Sasala Campus Affiliation: Penn State Harrisburg Major: Mechanical Engineering Anticipated Graduation Date: May 2021 Faculty Advisors: Ola Rashwan (Penn State Harrisburg), Zoubeida Ounaies (Penn State University Park) Project Partner: Jessica Hege Project Title: Optical Study of Germanium /Lead Sulfide Quantum Dots based Intermediate Band CsPbBr3 Perovskite Solar Cells As advances in solar technology persists, the efficiency of clean energy steadily increases. Currently, most commercialized solar cells are silicon-based. It is understood that Silicon is not an optimal choice for photovoltaic applications due to its indirect bandgap and limited absorption spectrum. With the use of quantum dots (QD) to create an intermediate band, the Intermediate Band Solar Cells (IBSCs) have a great potential to surpass standard silicon solar cells efficiencies. Intermediate Band Solar Cells(IBSCs) of inorganic perovskite photo-absorption layers offer promising characteristics for photovoltaic applications. Currently, most IBSCs research focuses on organic perovskites, such as MA PbBr3 and MAxFA1-xPbBr3. The major issue of the organic based perovskite is temperature and moisture instability. To overcome this problem and to further advance QD based IBSCs, the exploration of Cs- based perovskite is conducted. CsPbBr3 with Lead-Sulfide (PbS), Germanium (Ge) QDs are investigated. With the use of cesium (Cs) as the cation(A), in the perovskite’s ABX3 material structure, it is known that the stability would increase as well as the efficiency. The focus of the research was to find the optimum size (8nm, 9nm, 10nm), volume concentration (0%, 10%, 15%, and 20%) of cubical shaped QDs to maximize light absorption, hence increasing the short circuit current density. The simulations are performed in ANSYS Electronic Desktop. The layers of the IBSC models are: FTO(Fluorine doped tin-oxide)(50nm), TiO2(Titanium dioxide)(40nm), CsPbBr3 (PbS or Ge)(perovskite/quantum dots)(450nm/8-10nm), HTM(hole transporting material)(spiro-OMeTAD)(110nm), and Gold(80nm). The optimum design would result in an increase in overall efficiency of the stable Cs-based IBSC due to the increase of the short circuit current density, which paves the way to commercialization of the intermediate band solar cell s(IBSCs).

Lukas Seibel Campus Affiliation: Penn State Greater Allegheny Major: Civil Engineering Anticipated Graduation Date: May 2022 Mentors: Alandra Kahl (Penn State Greater Allegheny), Nathaniel Warner (Penn State University Park) Project Partner: Tyler Barry Project Title: Measuring Surface Water Quality with the AWQUA Now more than ever, it has become important to monitor water sources to ensure their safety for use and consumption. With an ever-growing population and ever-expanding industries, water resources have become much more important than ever before. Monitoring smaller waterways can be difficult due to the expensive nature of most sensors. We have created a device that uses a more affordable Arduino board and software system, as well as 3D printed housing to minimize costs while still collecting accurate data. The software monitors temperature, turbidity, and conductivity. Temperature and conductivity are used to help determine the salinity of the water. Turbidity is a way of measuring the overall clarity of the water. With these, we can gather an overall understanding of the water quality, and the affordability will allow for a wide deployment of these devices. In the future, citizen scientists to contribute data from their local waterways through using these sensors.

Andrew Shields Campus Affiliation: Penn State University Park Major: Aerospace Engineering Anticipated Graduation: May 2021 Mentors: Eric Greenwood and Mark Miller (Penn State University Park) Project Title: Acoustic Far Field Predictions of Small-Scale Rotorcraft under Increased Air Pressure Smaller scale experiments are an invaluable asset in the ongoing effort to study and reduce the noise produced via rotor aerodynamics. These experiments help to reduce costs in the beginning stages of reducing noise pollution and increasing travel comfort. However, these experiments are usually limited to 1/5th to 1/10th the diameter of the full-scale rotor due to the inability to match scaling parameters such as Reynold’s Number, Mach number, and Advance Ratio under a constant air density. In this paper, the viability of using a pressurized anechoic chamber to match these scaling parameters is explored. PSU-WOPWOP, a computational program developed by The Pennsylvania State University to predict aeroacoustic noise of rotorcraft, is used to predict the acoustic far field starting location of a hovering Boeing Model 360 rotor, scaled down under pressure from atmospheric to 500 psi. These predictions show that the acoustic far field beginning location, in terms of rotor diameter, stays constant despite the changing atmospheric conditions. Because microphone calibration techniques are not designed for use in a high-pressure environment, a vented pistonphone calibrator design is also discussed in this paper. The information obtained here will be used to design a pressurized anechoic chamber to verify that the acoustic signature can be accurately scaled down.

Zane Smith Campus Affiliation: Penn State Behrend Major: Mechanical Engineering Anticipated Graduation: May 2021 Mentors: Adam Hollinger (Penn State Behrend), Michael Hickner (Penn State University Park) Project Partner: Savanna Carr Project Title: Cost Analysis of Injection-Molding Electrically Conductive Polymers for PEMFCs Hydrogen fuel cells are an up and coming source of clean energy. Fuel cells can be used in many applications from vehicles to buildings and can even be grid-independent which is an appealing option for critical operations. However, the bipolar plates of fuel cells are primarily made from graphite, which can be costly and difficult to machine due to its brittle properties. An alternative is to use an injection-molded polymer composite that has comparable electric conductivity. Material samples were injection molded with various mixes of Nylon-6,6 and nickel-coated carbon fiber. Not much is known about the cost of producing this specific composite, so it is hard to compare to other production methods. By researching three different methods of producing bipolar plates (stamping, machining, and injection molding), the analysis shows the difference in costs of the production methods. By developing such a composite, a more cost-effective substitute for graphite can be used in the mass production of hydrogen fuel cells.

Jacob Smith Campus Affiliation: Penn State Behrend Major: Computer Science, Computer Engineering Anticipated Graduation: May 2022 Mentors: Omar Ashour (Penn State Behrend), Christopher McComb (Penn State University Park) Project Partner: Benjamin Moorehead Project Title: Developing Virtual Reality (VR) Learning Module to Teach Manufacturing Systems Virtual Reality (VR) enables the user to learn about their environment by experiencing it firsthand. VR enhances visualization and interaction. Industrial Engineering (IE) curriculum focuses on understanding systems concepts. Some of these concepts are abstract and difficult to understand. Therefore, it is often difficult for Industrial Engineering students to succeed in their major when the material is being presented via traditional teaching approaches. By giving these students a chance to learn using experiential learning rather than just seeing equations on a board, it improves the chance at understanding and exceling as well as engagement in learning. To achieve this, this project aims to design and

develop a VR-based learning module to teach manufacturing systems concepts. The current module involves basic statistics and probability concepts. The user has access to instructions, tools (such as stopwatch and calculator), and a way to verify his/her answers. The VR learning modules are hypothesized to increase the student’s motivation and learning performance.

Tyler Sterner Campus Affiliation: Penn State Berks Major: Mechanical Engineering Anticipated Graduation: May 2022 Mentors: Amir Barakati (Penn State Berks), Charles Bakis (Penn State University Park) Project Title: Finite Element Analysis of Conductive Laminate Composite Plates Subjected to a Low-velocity Impact Advanced composites, such as Carbon Fiber Reinforced Polymers (CFRP), are increasing in popularity throughout many industries like aerospace and automotive. From a growing usage of laminated composites stems the need to further understand and develop methods to study the structural behavior of these materials. In this work, a Finite Element Method (FEM) simulation has been created to study the mechanical response of a composite plate subjected to electro-magneto-mechanical loading. A mechanically loaded composite plate simulation is analyzed using the commercially available FEM software, COMSOL Multiphysics, with results verified in MATLAB. The numerical results were then compared with the FEM mechanical response of a composite plate subjected to a coupled electro-magneto-mechanical loading. This study showed that the application of an electromagnetic field can mitigate the stresses caused by the transverse mechanical load. The FEM simulation model developed in this research is proved to be an efficient model to predict the response of anisotropic materials in fully coupled fields and emphasizes the importance of the numerical study of complicated coupled problems in a continuously expanding advanced composites research field.

Timmy Tushar Rajan Susai Rajan Campus Affiliation: Penn State Abington Major: Computer Engineering Anticipated Graduation: May 2023 Mentors: Vinayak Elangovan (Penn State Abington), Jing Yang (Penn State University Park) Project Title: A Study on Efficient and Reliable Sound Source Localization Using a Three-Microphone Array This research highlights the development of major concepts and influential studies on sound source localization and analyzes the challenges and limitations of sound source localization using a three-microphone array. An idea for an aggregate solution incorporating an amalgam of elements taken from various digital signal processing techniques such as feature extraction and signal denoising is investigated, and the importance of efficient and reliable sound source localization as well as relevant use cases are discussed.

Aniketh Tathachar Campus Affiliation: Penn State Altoona Major: Biomedical Engineering Anticipated Graduation: May 2023 Mentors: Kofi Adu and Gary Weisel (Penn State Altoona), Nestor Perea Lopez (Penn State University Park) Project Title: Challenges of Thermoelectric Generators and Coolers In many underdeveloped regions around the world, drinkable water is scarce. Thermoelectric coolers have been proposed as a way to extract water from the atmosphere in these arid regions. However, the efficiency of said devices depends on three parameters. The interdependency between the variables has impeded the development of thermoelectric materials with high efficiency. Using bismuth telluride and silicon germanium as examples of current high-performance thermoelectric materials, we demonstrate the interdependency of the three parameters: Seebeck coefficient, electrical conductivity and thermal conductivity. Using ceramic thermoelectric modules, we plan to design and build a device that efficiently converts atmospheric water vapor into drinkable water.

Dylan Treaster Campus Affiliation: Penn State DuBois Major: Applied Materials Engineering Anticipated Graduation: May 2023 Mentors: Neyda Abreu (Penn State DuBois), Katherine Crispin (Penn State University Park) Project Title: Carbonaceous Chondrites Carbonaceous chondrites, a type of stony meteorite, originated in the early solar system and can be collected for research as finds or falls on earth. Analyzing the material and mineral compositions of these chondrites provides valuable information about the beginning geological processes that occurred very early in our solar system. This study will focus on CM chondrites for two reasons. First, the water in our planet might have been delivered by the same type of asteroid where this group of meteorites formed. Second, two sample-return missions (JAXA’sHayabusa2 and NASA’s OSIRIS-REx) will soon bring samples back from such asteroids. My work will provide a better understanding of the earliest planet-formation processes. The dataset includes calibrated compositional measurements of oxides contained in several of the least altered CM chondrites collected using an electron microprobe analyzer. I report the composition of different minerals and any element correlations, which will give insight on the origin and alteration process recorded by these samples.

Jonathan Trimpey Campus Affiliation: Penn State Behrend Major: Mechanical Engineering Anticipated Graduation: May 2021 Mentors: Adam Hollinger (Penn State Behrend), Charles Bakis (Penn State University Park) Project Title: Micromechanical Modeling and Optimization of Polymer Composite Bipolar Plates for Fuel Cells Fuel cells that are composed of polymer composite bipolar plates could be a more economical and environmental material choice compared to fuel cells with graphite or stainless-steel bipolar plates. The investigated composite is an injection molded bipolar plate with a recyclable Nylon 6,6 matrix and a nickel coated carbon fiber filler. A Nylon and nickel coated carbon fiber composite is a better alternative to materials currently used due to the composite being lightweight, having better resistance to corrosion than metals, having an increased life span, and being easily recyclable. The composite is electrically conductive due the contact of these short fibers with one another throughout the injection process allowing for a conductive path for electrons to flow. The composite is an anisotropic material because the fibers primarily align in the direction that composite is injected into the it is molded. Micromechanical analysis leads to different numerical values for electrical conductivity and modulus depending on direction in the material, fiber alignment, fiber length and diameter, and fiber concentration, and fiber conductivity and elastic modulus. Properties of the composite are modeled to see if a nickel coated carbon fiber bipolar plate can be used effectively based on the U.S. Department of Energy (DOE) guidelines. The specific properties modeled are ultimate strain, electrical conductivity, and material cost. The Halpin-Tsai equations are used for modeling the elastic modulus and ultimate strain and the Fiber Contact Model is used for electrical conductivity. Development of an objective function for optimizing cost, ultimate strain, and conductivity leads to optimal fiber volume percentages of the composite. Conductivity and modulus are found to be increased in the injection direction of the composite with higher fiber loadings as well as improved alignment of the fibers. The optimization tools developed in this investigation allow for comparison of composite bipolar plates to competing materials.

Dalina Thuy Mi Tu Campus: Penn State Harrisburg, Penn State University Park Major: Electrical Engineering Anticipated Graduation: May 2022 Mentors: Seth Wolpert (Penn State Harrisburg), Bo Cheng (Penn State University Park) Project Title: Autonomous Light-Seeking Robotic System Inspired by Dynamic Insect Vision Exploiting how insects process their world may help advance the visual systems of today’s robots, which aid our manufacturing industries, perform space exploration, and clean our houses. While humans inherently have the spatial perception to effortlessly make mental models of environments and motion trajectories, sharpening such aptitudes in robotics remains a work-in-progress. A model for robotic vision over a human being may be insects, which often have visual acuities that superior to humans although having much smaller, simpler anatomy. This work examines how some of these tiny creatures are capable of incredible visual systems and analyzing three-dimensionally, especially in dim-lit and adverse conditions, and how this knowledge could enable us to at last configure robots with three-dimensional, human-like perception. This project constructs an autonomous, mobile robot, pondering the connections between current visual methods for robots and the structural organization of the photoreceptors of adult insects, compound eyes. The bio-inspired robot’s given purpose falls in the field of navigating vehicles that track and pursue with applications such as surveillance, organization, and search and rescue. It was challenged to adapt to environments, consistently keep up with a moving light and complete courses of that pursuit of various difficulty. In end, it did successfully complete those tasks, but with the constraint of time and resources, satisfactory inclusion of the insect principles was not completed, and it did not move in preferred reaction times. Still, this work brought important discussion. Given greater resources, more ideas from insect vision would be explored, such recreating a compound eye, and it would have more powerful mechanical and electronic parts. Also, a more controllable lighting environment and the capacity for deep learning artificial intelligence would be employed, to dive into the neural network of insects. This approach builds upon existing discussion on the potential usefulness of biological mechanics in automation technology. Vision is an essential sensory for robots to understand and interact with the world and this work may hold critical insight.

Logan Vogelsong Campus Affiliation: Penn State Harrisburg Major: Chemical Engineering Anticipated Graduation: May 2023 Mentors: Abu Asaduzzaman (Penn State Harrisburg), Jose Fuentes (Penn State University Park) Project Title: A Computational Investigation of Oxidized Mercury Deposition on Arctic Ice Sheets Mercury is released into the atmosphere as Hg(0) where its oxidized under ultraviolet light to form oxidized mercury compounds. Concentrations of toxic mercury bioaccumulates in the arctic regions causing harm to the wildlife and native people, but the mechanism of how oxidized mercury is entering the water supply is not well understood. One possible process is the adsorption onto ice sheets and subsequently melting with the ice into the water supply. Density Functional Theory (DFT) was applied to calculate binding energies and to determine if this process is stable. These compounds can interact with ice sheets in different orientations; parallelly or perpendicularly to the surface. Presented in the work are the adsorption energies of various mercury containing molecules. BrHgXO and BrHgOX (X = Cl, Br, and I) molecules tend to have the highest adsorption to the ice sheet when oriented perpendicularly or parallelly. This study will help understand mercury transportation in the arctic aquatic system.

Maura Wells Campus Affiliation: Penn State University Park Major: Computer Science Anticipated Graduation Date: December 2020 Mentors: Omar Ashour (Penn State Behrend), Christopher Mccomb (Penn State University Park) Project Partner: Conner Delancey Project Title: VR Dementia Virtual Reality (VR) creates a ‘near-reality’ experience by allowing humans to physically interact with computer-generated simulations of three-dimensional environments. This project aims to develop a VR app to mimic dementia-related symptoms (DRS). Understanding of the concepts behind different types of DRS is essential for all caregivers engaged in dementia-related work. The complexity of DRS is not easily understood by caregivers through second-hand experience and theoretical learning. Due to the caregivers’ difficulty of understanding such concepts, many caregivers lack essential management skills to handle patients with dementia. This leads to increased stress in caregivers, and in the worst-case scenario, preventable hospitalization. Our VR app simulates a typical home environment, and caregivers of people with dementia (PWD) complete daily tasks such as taking medication and making the bed while experiencing different DRS including tremors, memory loss, and difficulty with coordination and motor functions. This simulation allows the caregivers to experience some of the difficulties of PWD for themselves. The complexity and difficulty of dementia-related symptoms is an unchangeable factor; however, caregivers’ failure to understand various concepts may indicate that current teaching methods are ineffective. This novel app is an invaluable new teaching method because it allows caregivers to gain first-hand experience to improve the quality of care they provide for PWD. The expected outcomes of this research would be caregivers’ increased awareness, empathy, and understanding of dementia-related symptoms, which will result in lower stress and fewer hospitalizations.

Tamia Williams Campus Affiliations: Penn State Altoona, Penn State University Park Major: Electrical Engineering Anticipated Graduation: May 2021 Mentors: Kofi Adu and Dr. Gary Weisel (Penn State Altoona), Ramakrishnan Rajagopalan (Penn State DuBois), Nestor Lopez (Penn State University Park) Project Partner: Chenzhang Zhou Project Title: Particle Size Distribution of Graphene Quantum Dots Graphene quantum dots (GQDs) are nanoscale crystals purely made of carbon atoms that have low toxicity, very stable, and efficient photoluminescent(PL)properties. Compared with other types of quantum dots, the superior thermal, electrical, and mechanical properties of GQDs have made them ideal candidates for state-of-the-art applications such as light-emitting diodes, bioimaging markers, fluorescent polymers, batteries, etc. Most of the unique properties of GQDs are diameter dependent, particularly the PL and other optical properties. Current synthesis protocols always produce GQDs of certain diameter distribution. We investigate the nature of the diameter distribution to better understand its influence specifically on the PL. We extracted the particle size distribution of GQDs from several publications and analyzed the distribution using MATLAB. Our analysis confirmed that the majority of the size distribution follows a log-normal behavior. This is attributed to limitations in synthesizing smaller GQDs than their larger counterparts, leaving a tapering tail on the larger particle side (right side of the distribution). The fitting parameters obtained from the log-normal distribution are used for further work on the influence of the diameter distribution on the PL.

Chase Wirick Campus Affiliation: Penn State New Kensington Major: Aerospace Engineering Anticipated Graduation: May 2022 Mentors: Adam Zottola (Penn State New Kensington), David Snyder (Penn State University Park) Project Title: 3-D Printed Power Electronics for Energy Efficient Renewable Energy Grid Integration and Electricity Transformation With the addition of three dimensional (3-D) printing technology within the power electronic industry, many new capabilities and improvements have been made apparent; from rapid prototyping to increased design flexibility, 3-D printing technologies have the potential to revolutionize and enhance power electronics. Specifically, the study of the applications of 3-D printing of wide bandgap (WBG) power electronics can be used to determine the impact on cost effectiveness and electrical efficiency. WBG power electronics can conceivably dramatically cut back the overall energy consumption, relative to silicon electronics, and with the addition of 3-D printing, low cost prototypes can be created, and power electronics can be directly manufactured in low volumes. While 3-D printing of power electronics has been done before in a roundabout manner, an all-in-one, multi-material power module has yet designed, fabricated, and optimized for additive manufacturing all while staying within material and mechanical limitations. By utilizing a quad-tool head nScrypt Additive Manufacturing system, discrete WBG devices will be integrated within numerous 3-D printed power electronic circuits and modules to test and eventually maximize electronic efficiency.

Xinchang Xiong Campus Affiliation: Penn State Abington Major: Computer Science Anticipated Graduation: May 2022 Mentors: Yi Yang (Penn State Abington), Yanxi Liu (Penn State University Park) Project Partner: Kunze Yang Project Title: Laser-Based Technique Captures 3D Images of Impressionist-Style Brushstrokes Optical coherence tomography (OCT) has introduced 3D reconstruction of oil painting methods to offer user various ways accessing artworks. OCT have been adopted to study the surface features and subsurface structures of objects non-invasively. However, the scanning area is restricted by the field of view (FOV) of OCT. Dr. Yang’s has presented a hybrid scanning platform combined with effective algorithm for real-time sampling and artifact removal to achieve macroscopic OCT (macro-OCT) imaging and spectral 3D reconstruction of an impression style oil painting. We continued to work on this project to optimize this process. The 3D model of the oil painting generated from this work can be utilized through augmented reality (AR), 3D printing. The 3D data set obtained from macro-OCT can potentially improve the accessing artworks online and assist the visually impaired to study art through tactile touch.

Kunze Yang Campus Affiliation: Penn State Abington Major: Computer Science Anticipated Graduation: May 2022 Mentors: Yi Yang (Penn State Abington), Yanxi Liu (Penn State University Park) Project Partner: Xinchang Xiong Project Title: Laser-Based Technique Captures 3D Images of Impressionist-Style Brushstrokes Optical coherence tomography (OCT) has introduced 3D reconstruction of oil painting methods to offer user various ways accessing artworks. OCT have been adopted to study the surface features and subsurface structures of objects non-invasively. However, the scanning area is restricted by the field of view (FOV) of OCT. Dr. Yang’s has presented a hybrid scanning platform combined with effective algorithm for real-time sampling and artifact removal to achieve macroscopic OCT (macro-OCT) imaging and spectral 3D reconstruction of an impression style oil painting. We continued to work on this project to optimize this process. The 3D model of the oil painting generated from this work can be

utilized through augmented reality (AR), 3D printing. The 3D data set obtained from macro-OCT can potentially improve the accessing artworks online and assist the visually impaired to study art through tactile touch.

Nik Zaman Faisal Zakimi Na Rahim Campus Affiliation: Penn State Harrisburg Major: Mechanical Engineering Anticipated Graduation: May 2021 Mentors: Ola Rashwan (Penn State Harrisburg), Zoubedia Ounaies (Penn State University Park) Project Partner: Sidney Hagen Project Title: Study on the Mechanical Properties of PLA/Hemp Bio composite Filament for 3D Printing This research is on modeling of the extrusion process of polylactic acid (PLA) combined with Hemp as a biodegradable composite material to create 3D printing biocomposite filament. Hemp is emerging as a bio-filler. It has a relatively higher fraction of cellulose and comparable lignin to that in wood, thus possessing a greater reinforcement potential. 3D printing is fundamentally less wasteful than traditional, subtractive manufacturing methods, the use of plastics as a feedstock has the potential to exacerbate the global plastic problem unless a sustainable solution is found. Therefore, creating a biodegradable filament is a promising approach to overcome this constraint. Very few researches have been conducted to study the mechanical properties of Polylactic Acid (PLA)/Hemp bio composite. Simulating the extrusion process using finite element methods (FEM) can help predict the properties without the expense of wasting materials and time. There is little research on the extrusion process of PLA and no studies on modeling of the extrusion of PLA/Hemp blend. This project investigates the extrusion process of this biocomposite in a single screw extruder system using the COMSOL multiphysics simulation software. First, an assembly of the melting and melt conveying sections of an extruder was created in Solidworks. The assembly was imported into COMSOL Multiphysics 5.5, and the heat transfer and fluid mechanics physics modules were used. 5 % to 20 % weight percent of hemp are being investigated. For each weight percent of hemp, the different temperatures of the barrel and speeds of the screw are adjusted and tested. The melt flow of the biocomposite is obtained by analyzing the changes in viscosity, velocity and temperature profiles. This knowledge will serve as a platform for future development of sustainable 3D printing filament. The ultimate goal is to use this sustainable and biodegradable polymeric blend to replace polymers with a harsher impact on the environment.

ChaoJue Zhang Campus Affiliation: Penn State Behrend Major: Industrial Engineering Anticipated Graduation: May 2022 Mentors: Omar Ashour (Penn State Behrend), Eunhye Song (Penn State University Park) Project Title: Group Technology based Clustering and Prioritization for Order Picking and Warehouse Management This work focuses on improving order picking in warehouses. Order picking is the process of items retrieval from warehouse locations to fulfill customer orders. Warehouse management is important for businesses of any size. Knowing when to pick certain items, what amount to pick, and how to pick items can easily become complex decisions. Group Technology (GT) is a management theory by which the items data is analyzed to group items based on their similarities to each other. This approach has been proven to improve systems efficiency and productivity. In this work, a dynamic GT-based classification method is used to sort items based on their similarity. With this method, items are grouped as the orders arrive based on critical characteristics, e.g., item type, priority, and order frequency. This method will potentially minimize warehouse management costs and increase efficiency. A case study is implemented where a simulation study is used to compare the current practice with the proposed method.

Chenzhang Zhou Campus Affiliations: Penn State Altoona, Penn State University Park Major: Electrical Engineering Anticipated Graduation: May 2021 Mentors: Kofi Adu and Dr. Gary Weisel (Penn State Altoona), Nestor Lopez (Penn State University Park) Project Partner: Tamia Williams Project Title: Particle Size Distribution of Graphene Quantum Dots: Empirical Modeling of the Influence of Diameter Distribution of Photoluminescence of Graphene Quantum Dots. As a new class of fluorescent carbon materials, graphene quantum dots (GQDs) have drawn increasing attention due to their exceptional properties and potential applications, such as light-emitting diodes. Even though there is extensive data on the photo-response of GQDs, limited studies have focused on how size distribution affects the photoluminescence (PL). In this project, we develop an empirical model that captures such relationship. Using MATLAB, we implement fitting programs to benchmark our model against existing data in the literature. Our preliminary results indicate discrepancies between the model and the experimental results. These could be attributed to conditions including functional groups terminating the carbon bonds, inhomogeneous layer number distributions, and the absorption effect on the PL. In the future, we plan to fabricate our own GQDs and improve our model.

Pengwen Zhu Campus Affiliation: Penn State Abington Major: Computer Science Anticipated Graduation: Spring 2022 Mentors: Vinayak Elangovan (Penn State Abington), Vittaldas Prabhu (Penn State University Park) Project Title: Handwritten text recognition using Bezier curve detection The recognition of handwritten text or text in arbitrary shape and direction is a costly and difficult problem. It has demands of high accuracy to recognize the text and high-end equipment to accomplish the goal. This research investigates multiple models to verify high efficiency of adaptive Bezier curve detection, a method applied in text recognition field. Methods used for text recognition like TextBoxes/++, EAST or FOTS do have ideal effect in this field by providing high accuracy and fast recognizing speed. These methods are good enough to deal with normal situation, when the shape of text become different it would be difficult for them to perform recognition while application of Bezier curve could be a good solution in this situation.

ADDITIONAL PROJECTS David Auerbeck, Penn State University Park Mentors: William G. Van Der Sluys (Penn State Altoona), Xueyi Zhang (Penn State University Park) Trisa Mikhail, Penn State Harrisburg Mentors: Rajarajan Subramanian (Penn State Harrisburg), Melika Sharifironizi (Penn State University Park) Austin Wylie, Penn State Berks Mentors: Rungun Nathan (Penn State Berks), Miller Mark (Penn State University Park)