Category: College of Engineering and Computing

College of Engineering and Computing Making and Creating

Controlled Syringe Pump Extrusion to Create Hydrogel Gradients

Author(s): Elizabeth Clark

Mentor(s): Remi Veneziano, Bioengineering

Abstract

The primary objective of this project is to build upon my previous research where I developed a method to create hydrogel gradients. Hydrogels are comprised of polymer(s) suspended in water. A gradient is the change from one concentration of to another. I used 10% gelatin weight ratio to deionized water mixed with dye. The gelatin was heated and stirred until dissolved and then was split into two portions and dyed two different colors and then placed into syringes while at around 45 degrees. By using a specialized nozzle, I could feed two syringes into one nozzle that has a static mixer at the tip to ensure the gelatin was evenly mixed. Gelatin is a liquid at higher temps (40-50 degrees C) and sets at lower temperatures so the gradients were extruded on a chilled metal plate so the gelatin would set almost immediately. Depending on how fast one syringe extruded versus the other I could change the color and even mix them. After creating several gradients by hand I utilized a syringe pump to have even extrusion rates. The syringe pump was utilized by alternating which pump was extruding so a colleague had to move the plate. This has applications in bioprinters and rather than having somebody move the plate manually the bioprinter will move the plate or the extruder. These results build on the potential of bioprinting gradients for use in bioprinters in regenerative medicine and other bioengineering applications.

Audio Transcript

Hello, my name is Elizabeth Clark and I’m a bioengineering student and my research project was built up on my previous research project which is creating hydrogen gradient as many cellular functions and processes utilize gradient in the human body.

So for keywords and background, hydrogels are comprises a polymers in water a gradient is the change of concentration so in this case in a line and will be represented by the changing color. Gelatin is the hydrogel I used. I use the 10% concentration so 10 mL of water I would use 1 g of gelatin and a syringe pump is the tool that I use that allows for the extrusion rate and you can program different extrusion rates.

So this slide just shows briefly the set up I used in my previous research project and I modified it slightly for the gelatin. Two syringes are being fed into a custom static mixer and extruded by hand. I only did this a few times just to ensure that a different hydrogel would work. Gelatin is a liquid at warmer temperatures so around 40 to 50°C and when placed on a cooler surface, in this case of metal plate that is chilled, it would sit almost immediately set.
I have with the syringe pump and it will alter the color by which one is extruded.

Here’s a video of that. When I wanted to change the color I would just pause one syringe pump and start to extrude on the other and then flip it. And as you can see on the right is the gradient that was just created from the video

Special thanks to Oscar for funding this project as well as my mentor Dr. Remi Veneziano, as well as the other people listed. Thank you.

Laser-Induced Graphene–Nanoparticle Platforms for Plasmonic Enhanced Photosensing

Author(s): Ali Kabli

Mentor(s): Pilgyu Kang, Mechanical Engineering

H

Abstract

This project explores the potential for enhancing the performance of Laser-Induced Graphene (LIG) using metallic nanoparticles (NPs) as a platform for fabricating photosensors with enhanced sensitivity. The main question being addressed is can a Laser-Induced Graphene–Palladium nanoparticle (LIG-PdNP) nanocomposite enhance sensor sensitivity through plasmonic and interfacial effects? Research has been conducted in the past regarding the use of LIG as the functional material in a photosensor, and the rationalization behind using these metallic NPs in a nanocomposite material is to improve the sensitivity of the sensor by improving the photoresponsivity. This is due to the introduction of plasmonic effects from the NPs, which allows for the photocurrent to flow more efficiently. The main novelty behind this particular project’s approach lies in the synthesis of the nanocomposite, where classic means would have the NPs deposited on the LIG surface creating point contacts. The synthesis technique being explored here involved a one-step synthesis via precursors and a polymer substrate, which creates a “seamless interface” between the components of the functional material. This interface allows for the electrons to flow freely between the LIG and NPs, enhancing the photoresponsivity of the device. Two devices were compared, one with 0wt% of PdNPs, and another with 30wt% PdNPs in order to observe any improvements in the performance of the devices when hit with a blue laser (448.2nm wavelength). Future research regarding this project includes using NPs with higher plasmonic effects such as gold or silver, as well as refining the geometric footprint and patter of the sensor itself to increase performance further.

Audio Transcript

Hello everyone, my name is Ali Kabli and today I’m going to present to you my undergraduate research project, Laser Induced Graphene Nanoparticle Platforms for Plasmonic Enhanced Photosensing. This project was advised by Dr. Pilgyu Kang from the Department of Mechanical Engineering.

So to give a brief background and introduction, past research has been done by Dr. Kang and his group, utilizing laser-induced graphene, or LIG, as a sensing element in photosensors. Now, these sensors operate based on the premise of photosensitivity. You basically shine a laser of some known wavelength at the sensor, which will induce some photocurrent. The change in photocurrent can be observed and used for sensing purposes. We want to improve the sensitivity of these devices by introducing metallic nanoparticles, or NPs, to increase the plasmonic effects and photoresponsivity of these devices. Now, for the purposes of this project, the specific nanoparticles that were used were palladium. However, any metal that has known plasmonic effects can be used.

For the purposes of this presentation, or project, we proposed a novel nanocomposite synthesis technique, which resulted in a seamless interface between the LIG and the nanoparticles. Traditional methods would have you deposit these nanoparticles on the surface of the LIG, or whatever substrate you’re using, which results in a point contact between the particles and the bulk surface. The downside to this is the fact that that point contact doesn’t allow for the most efficient flow of electrons. However, through a one-step synthesis technique using precursors and polymer substrate, we are able to integrate these nanoparticles within the surface of the laser-induced graphene itself, allowing the electrons to flow seamlessly.

So, the main question that we were answering with this research project was, can a laser-induced graphene palladium nanoparticle nanocomposite enhance sensor sensitivity through plasmonic and interfacial effects? The plasmonic effects, once again, coming from the fact that we’re using these metallic nanoparticles, and the interfacial effects coming from the seamless interface through our unique synthesis technique.

The methods and procedure for this project involved the actual synthesis of our nanocomposite using the one-step technique. Then we would fabricate the photosensor device using the synthesized nanocomposite. It should be mentioned that the scale of this sensor was 500 millimeters by 500 milliliters, which is actually quite large given the nanoscale. It’s very, very large. So that may have resulted in the data being slightly skewed, which is an improvement that we will go over at the end of this presentation. Then we collected optical data regarding the photoresponsivity of the device by hooking it up to an optical testing apparatus where we would shine a laser on and off at known intervals. The laser’s wavelength was known for the purposes of this project. We were using a blue laser, 448.2 nanometers of wavelength, and we would plot the resulting photocurrent as a function of time. The long-term goals of this project are to one day harness these nanocomposites as a platform for plasmonically enhanced PEC or photoelectrochemical gas sensors.

Now here’s just a brief snapshot of the results. We see on the left side a comparison between the photocurrent resulting from a 30 weight percent nanoparticle nanocomposite and on the right side we have the photocurrent resulting from just pure LIG. As you can see the scale on the left side is in microamps, and the scale on the right side is in nanoamps, which means that we were able to show a drastic improvement, three orders of magnitude to be exact.

In conclusion, the experiment was a huge success in proving that plasmonic effects could enhance the sensitivity of these devices. However, more work is still needed in the future. We can refine the geometry and footprint of the sensor itself so that it’s a lot smaller than 500 by 500 millimeters. We can also test other nanoparticles with known greater plasmonic effects, such as gold or silver. And we can also play around with different laser parameters, focusing the laser’s beam more, increasing the wavelength, etc.

Some acknowledgements. Of course, my advisor, Dr. Pilgyu Kang, Graham Harper, who aided in data collection on this project, and Philip Acatrinei, for being an indispensable help in data collection and in setting up the experiment itself. He actually programmed the software that we were using to collect the data. So without him, this project would not have been possible. Thank you.

College of Engineering and Computing College of Humanities and Social Science Honors College Making and Creating OSCAR Undergraduate Research Scholars Program (URSP) - OSCAR Winners

A Robotic Cat for Examining Camera Clarity and Privacy in Human–Robot Interaction

Author(s): Alexia De Costa

Mentor(s): Eileen Roesler, Department of Psychology

S

Abstract

This project presents the Bioinspired Automated Robotic Cat (BARC), a functional companion robot designed to support research in human–robot interaction and privacy-aware design. BARC features camera-based facial detection, expressive gaze behaviors, audio responses, and various soft and rigid materials to mimic a household cat. Because camera systems can enhance interaction while raising privacy concerns, the ongoing study compares peoples’ responses under two conditions: a clear, high-quality camera filter and a blurred, low-clarity camera filter. Using surveys and observation of touch behavior, the study examines how camera clarity shapes engagement and perceived privacy, informing the design of social robots that are effective while respecting user comfort.

Audio Transcript

Have you ever wondered what a robot actually sees when it looks at you?
Today, social and service robots are becoming increasingly common, and many rely on cameras for facial recognition and user engagement. But as useful as cameras are, they also raise important questions: Do they make people feel watched? Can a robot feel friendly while still respecting privacy?

These questions lie at a key intersection in human–robot interaction, that robots need perception to understand us, yet high-resolution sensing can make people uncomfortable. So I wanted to explore a central challenge: can we reduce privacy concerns without making interactions less enjoyable? And does being transparent about what a robot sees change how people feel?

To investigate this, I designed and built a robot cat from scratch called BARC, the Bioinspired Automated Robotic Cat. BARC is part engineering platform and part research tool. It can switch between two controlled camera conditions: a clear, high-quality camera filter and a blurred, low-clarity filter that still allows for partial facial detection. These interchangeable physical filters let me directly compare how different levels of sensing clarity influence interaction.

BARC is also designed to feel expressive and lifelike. It uses camera-based facial detection for gaze behavior, animated OLED eyes, a speaker for cat-like sounds, and soft and rigid materials that mimic the look and feel of a household cat. Through surveys and observations of touch behavior, my ongoing study explores how these two camera conditions shape user engagement and perceived privacy.

To create BARC, I began with feline anatomical references, studying limb placement, joint spacing, and overall proportions, to inspire the CAD model for the chassis. I laser-cut the acrylic components and assembled them using screws and tab-and-slot joints for a sturdy, lightweight frame.

At the heart of the robot is a Raspberry Pi 4, which handles perception and behavioral control.

A camera provides the main sensory input for facial detection.

Two OLED displays animate expressive eyes that track the user once a face is detected, giving the illusion of attention and social presence.

A speaker and amplifier generate a range of cat sounds, from meows to purrs to alarmed yowls.

An accelerometer-gyroscope detects movement, such as being picked up or shaken, so BARC can respond appropriately.

Servos are controlled by a PCA9685 driver, animate the limbs, jaw, head, and tail.

All behaviors are programmed in Python and organized in a state machine with modes such as Idle, Seeking Attention, Interacting, and Startled. BARC transitions between these states based on sensory input and probability, helping interactions feel natural rather than scripted.

To examine how camera clarity influences engagement and privacy perceptions, BARC serves as a fully capable research platform. Seventy-two participants are currently part of a single-blind study with two groups:

Group 1: interacts with BARC using a clear camera filter

Group 2: interacts with BARC using a blurred, privacy-preserving filter

The physical filter is noticeable, so using filters in both groups keeps the robot visually consistent. That way, any differences we see are truly due to what the robot can or can’t perceive.

Participants interact with BARC, complete a survey measuring constructs such as Perceived Sociability and Perceived Enjoyment, and then are shown a live camera feed so they can see the actual resolution of the robot’s vision. Afterward, they complete a second survey measuring perceived privacy, perceived surveillance, disturbance, and attitudes about robots.

The hypotheses are:
1: No difference in sociability, enjoyment, or touch behavior.
2: The filtered-camera group will report higher perceived privacy.
3: The clear-camera group will report higher perceived surveillance.

This interdisciplinary project connects mechanical engineering, psychology, and human-robot interaction to better understand how people perceive robotic sensing. BARC’s expressiveness, biological inspiration, and controlled camera conditions make it a powerful research platform.

By comparing clear versus filtered camera views, this research explores whether privacy concerns come from what the robot actually sees, or what users believe it sees. Ultimately, the goal is to guide the design of future social robots that remain engaging and respectful of user’s privacy

Special thanks to Dr. Eileen Roesler (Psychology) and Dr. Daigo Shishika (Mechanical Engineering) for their invaluable mentorship. Thank you to Katya Schafer for assistance with data collection, and to Dr. Karen Lee and OSCAR for their support and funding, which made this project possible.

Thank you!

Cells, Individuals, and Community College of Engineering and Computing OSCAR Undergraduate Research Scholars Program (URSP) - OSCAR

Pain, Medication Use and Biomarker Associations in Individuals with Polycystic Ovary Syndrome (PCOS) : Insights from the All of Us Research Program

Author(s): Jannatul Nayeem

Mentor(s): Jenny Phan, CASSBI

A

Abstract

Background:
Polycystic Ovary Syndrome (PCOS) is a complex endocrine disorder often accompanied by chronic pain, yet the biological and social determinants of this pain remain underexplored. Understanding how stress-related biomarkers and healthcare access interact in shaping pain experiences may reveal mechanisms underlying health disparities in PCOS populations.

Objective:
This study examined associations between inflammatory and neuroendocrine biomarkers (C-reactive protein (CRP), cortisol, and body mass index (BMI)) and pain burden among individuals with PCOS, while exploring the moderating role of healthcare access and insurance coverage.

Methods:
Using data from the All of Us Research Program, 2,160 adults with PCOS (identified by ICD-9/10 codes) were analyzed. Pain burden was measured through pain-related diagnoses and pain medication dosage. Biomarker distributions were winsorized, log-transformed, and analyzed via multivariate regression models adjusting for age, race, socioeconomic status, and healthcare variables.

Results:
Pain burden alone was not significantly associated with higher CRP, cortisol, or BMI levels. However, healthcare access moderated these relationships: participants with greater barriers to care exhibited elevated inflammation and BMI with increasing pain, whereas those with adequate access showed flatter or reduced biomarker trends.

Conclusions:
Findings suggest that chronic pain and stress responses in PCOS may be shaped more by social and contextual factors than biological burden alone. Enhancing healthcare accessibility and equity could mitigate stress-related physiological outcomes and improve pain management for individuals with PCOS.

Audio Transcript

0:01 Hello, my name is Jannatul Nayeem. I am a student researcher with the B&LAB, um, at George Mason. I’m working directly with, um, Dr.
0:13 Jenny, um, in her our static load study. Which is the body’s biological stress response, and how it relates to menstrual disorders and chronic pain.
0:23 Um, from that study, I wanted to dive deeper into PCOS, and look at pain medication use and biomarker. Or associations and individuals with that disorder.
0:36 Umm, for methodology, I started off by using, umm, the all of us data set, uhh, database. Umm, it has a ton of data on- individuals, uhh, with all sorts of diseases and, umm, information from their doctor visits, umm, patient records, and also, umm, some survey questions that, the program itself asks
1:06 those participants, umm, and so through that, through that database, I was able to find what 2160 individuals with PCOS, and dive deeper into, uhh, their- biomarkers, uh, specifically for this, I’m using, umm, 3 biomarkers as predictors for inflammation and stress.
1:30 I’m using BMI, C-reactive protein, and cortisol. And then, umm, for- for their outcome variable, I use pain diagnosis along with their medication usage, umm, for medication usage, umm, I accounted for, umm, how many medications they’re taking.
1:52 And, and also what the dosage was for that medication, umm, and then some co-variates, such moderators that, sorry, some co-variates that I used was age, race, uhh, and SES index, and then for moderators, umm, I looked at healthcare access, uh, specifically insurance insurance status and access to care
2:19 . there. And so, I’m going to zoom into the results that I had, umm, hopefully in the video it resumes in two.
2:33 umm, but for my results, I found that, umm, pain alone didn’t start- we predict inflammation or stress, but limited access to care did.
2:45 Individuals with more barriers, such as lack of insurance, showed higher inflammation and BMI with pain, suggesting that health equity plays a critical role in PCOS pain.
2:56 Um, while my insurance data was limited, uh, because, uh, that not many people answered those questions, um, there’s still show some support that the idea- that stress biology and pain in PCOS are influenced by social environment and not just physiology.
3:22 Um, right now, I am continuing this study, um, to- or they’re deep in my understanding and advocate for equitable pain care in PCOS populations.
3:36 Umm, and so one thing I want to focus on more is, uh, imp- moving, um, how we, uh, state these questions, because I do feel like how the, uh, question is stated about access to care and insurance status is pretty sensitive.
3:58 So how can we go about it to, change the way, um, someone feels about answering those type of questions. Um, and so yeah, that was my study.
4:09 Thank you for listening.

College of Engineering and Computing

Comparative Analysis of Mechanical and Surface Characterization of PLA/HA Composites via Different Fabrication Methods

Author(s): Muhammad Sardar

Mentor(s): Shaghayegh Bagheri, Department of Mechanical Engineering

Numerous studies in the field have commonly relied on a singular mixing method for composite formulation, without delving into the comparison of various mixing techniques. Consequently, the primary objective of this research is to identify the most optimal manufacturing process for formulating FDM printed PLA/HA composite structures, achieved through a comprehensive comparison of four distinct mixing methods. These methods include the utilization of a magnetic stirrer, and speed mixer, which will be systematically examined for fabricating PLA/HA material. In order to arrive at a sound conclusion, this study will conduct thorough mechanical and tribological characterizations of the FDM printed PLA/HA structures, aiming to ascertain the best methodology for fabricating PLA infused with 3% wt of HA. By presenting these findings, this research aims to contribute significantly to the collective knowledge surrounding FDM printing and the formulation of PLA-based composites. The data obtained will offer valuable insights for the advancement of this innovative field.

Hello everyone and welcome to my project on mechanical and surface characterization of PLA/HA composites.
The main objective of this research is to find an optimal manufacturing method for PLA/HA composite, based on the comparison of three mixing methods. We have seen that previous studies have only focused on the weight percent of Hydroxyapatite in the PLA samples, but no studies have been found yet to focus on the manufacturing method and how it affects the mechanical characterization and surface area of the sample. So, for that purpose, we have proposed three manufacturing methods that includes magnetic stirring, dry speed mix and wet speed mix.
Now, the dry and wet speed mix are not that different, except, the wet speed uses an organic compound called dichloromethane or DCM, which exists in a liquid state at room temperature. Once the material is ready, it is then used for filament extrusion.
This is machine, called extruder where the raw material is fed into which melts these pellets at about 210 Celsius and we can see the filament is coming out on the other side. This filament is then inserted into a 3D printer to print the sample.
This is the next step called indentation, where the machine applies and gradually increases the load to access the hardness and elasticity of the sample. This step is then later on key to analyze the tribology and surface properties of the sample.
From Electron Microscope images, we can see the indentation that was done on the surface with a closer view on the right side.
Apart from the SEM images, we have also done something called EDS, or electron dispersive spectroscopy. Basically, what it does is the electron beam in the electron microscope emits high energy electromagnetic waves, or x-rays, on the surface of sample which results in ejection of innermost electrons and hence, we can see what the chemical composition of the sample is.
As expected, we have seen an abundance of carbon followed by oxygen and phosphorus. The gold is only present because the sample was gold coated before doing EDS to prevent it from burning. All the colored areas indicate the presence of their respective element, and we are also given a spectrum as to the percentage of each element is present.
I think it is important to mention here about the limitation of our Scanning Electron Microscope as it cannot detect hydrogen. We have seen some cases where the percent composition is not adding up to 100% so it‘s probably most likely that this is hydrogen unless we know that our sample does not have any trace amount of hydrogen in it.
PLA/HA composites have plethora applications in the real world from aerospace to modern medicine to especially in bioengineering where it is used in bone fracture repair, which is the motivation of our research.
Now this concludes my video and if you have any questions about my research, don‘t forget to comment down below and I will reach out to you as soon as possible. Thank you very much!

College of Engineering and Computing OSCAR

Evaluating Linguistic Relativity Through the Grammatical Gender of Inanimate Nouns and their Co-occurring Adjectives

Author(s): Reva Hirave

Mentor(s): Antonis Anastasopoulos, Department of Computer Science

The hotly contested notion that language directly shapes the way that we think is known as the theory of linguistic relativity. One way that linguistic relativity is theorized to present itself is through grammatical gender, specifically the grammatical gender of inanimate nouns. This project aims to examine the relationship between the grammatical gender of inanimate nouns and the adjectives used to describe them. We extract pairs of inanimate nouns and their modifying adjectives from spoken corpora across four languages. These languages span all combinations of pre/postnominal and gendered/nongendered languages: Spanish, English, Basque, and Turkish. We then calculate the normalized mutual information (NMI) between the grammatical gender tags and their modifying adjectives. Preliminary results of Spanish indicate an almost negligible relationship between these two variables, calling the theory of linguistic relativity into question.

Hello, my name is Reva and this is my project on evaluating linguistic relativity through the grammatical gender of inanimate nouns and their co-occurring adjectives. Does language shape the way we think? This is a question that might seem simple enough, but it’s something that’s actually battled linguists for a very long time. The idea that the structures of a language can shape the way its speakers think is called linguistic relativity, and although it can present itself in many ways, we’re going to look at one in particular, and that’s grammatical gender.

For our purposes, we’re going to consider a gender language as one that sorts nouns into two buckets: masculine and feminine. There are languages that have way more than these two grammatical genders, but this is what we’re going to look at for now.

So why are some nouns masculine and others feminine anyways? The thing is, nobody really knows, and it’s generally agreed that these distinctions are completely arbitrary, but still, they exist.

Let’s look at the word “key” in Spanish. The word for key is “La llave,” which is feminine, and in German, it’s “der SchlÃ¼ssel,” which is masculine. So does this mean that Spanish speakers think about keys as being more feminine in nature? So would they describe them as dainty, delicate, pretty, more stereotypically feminine words like that? And on the contrary, would German speakers describe keys as sturdy, jagged, rough?

According to one study by Phillips and Boroditsky, this is the case. They identified a causal relationship between the gender of inanimate nouns and the way that these nouns are used. But still, the legitimacy of the study has been called into question many times, and its results have actually been proven and disproven. So the extent of this relationship is still not fully understood.

We’re going to look at three specific research questions:

Is there a relationship between the grammatical gender of a noun and the adjectives used to describe it?
Is this relationship an appropriate metric with which to evaluate linguistic relativity?
To what extent do the grammatical structures of a language influence the way speakers think?
The first step in this project is to gather our data, and we’re going to be looking at four languages: English, Turkish, Spanish, and Basque, which span every combination of gendered and non-gendered and prenominal and postnominal.

We want to find natural language, something that’s conversational and comparable across these languages. We don’t want one dataset to be super formal academic writing and the other to be super casual conversational data. So we found the two corpora listed below. The one for English is still to be determined.

From there, we cleaned our data, which involved tagging and parsing it with stanza, which is a very useful tool. We saved all these tokens as lemmas, their dictionary-like root form, with accompanying morphological tags of gender (masculine/feminine) and number (singular/plural).

From there, we extracted these noun-adjective pairs and stored them in the format that you see at point two. And then that brought us to the last step of this process, which was animacy detection.

This detection of inanimate versus animate nouns is actually not all that straightforward. So the way we accomplished this was by translating every noun into English and then comparing its set of synonyms and evaluating it against the word “person” and seeing if they share any hyponyms.

After this cleaning process was done, we ended up with a little over three and a half thousand pairs for both Turkish and Spanish. Our very preliminary results are listed here. You can see that we calculated the normalized mutual information for the gender-adjective relationship for Spanish and the number-adjective relationship for Spanish and Turkish. An NMI of zero indicates absolutely no correlation, and an NMI of one indicates complete and total dependence. You can see they’re very close to zero but they’re all sort of within like 0.03 of each other, so there really isn’t much we can conclude from our results so far.

On the right, I’ve included word clouds for adjectives used to describe masculine nouns in Spanish or masculine and feminine nouns in Spanish, as well as all adjectives in Turkish. And as you can see, there’s no apparent pattern there either.

The next steps are just to find an appropriate English corpus, run Basque, and run the Basque and English corpora through the data processing pipeline, and develop a better metric to compare gender and non-gender languages.

I hope to conclude this project by June. And I don’t have time to read all my acknowledgments, but again, a big thank you to everyone who supported me in this project so far. And these are my references.

College of Engineering and Computing OSCAR

Mechanical and Surface Characterization of 3D Printed PLA-HA Composites

Author(s): Muhammad Sardar

Mentor(s): Shaghayegh Bagheri, Department of Mechanical Engineering

College of Engineering and Computing College of Science OSCAR

Optimizing Graphene-Metal Composite Production With Laser Modeling

Author(s): Diego Espinoza

Mentor(s): Pilgyu Kang, Department of Mechanical Engineering

Composite materials are engineered materials made from two or more different composite structures which change physical or chemical properties. Covetics are a type of composite material where graphene is integrated into a metal; these materials show improvement in mechanical strength, electrical and thermal conductivity and increase in material durability. Currently, the formation of covetics has been made using an electric arc method, which gives a non-homogenous graphene distribution, making it more difficult to optimize. Laser-Induced Graphene (LIG) presents a promising alternative. This process employs a laser and a polyimide (PI) film to create porous graphene, infusing it into a metal substrate, resulting in covetic material. Unlike traditional methods, LIG allows for a more controlled and homogenous structure. However, the optimal laser parameters, such as power, wavelength, speed, frequency, and fluence, remain unknown, impacting the quality and efficiency of porous graphene formation in covetics. This research addresses the gaps in understanding the laser parameters’ influence on LIG by developing a photothermal processing modeling of the process. The simulation focuses on correlating laser parameters to the heat distribution during the LIG process. By simulating the thermal changes of the material, the project aims to streamline the determination of optimal laser parameters, bypassing the need for repetitive physical experiments. This computational approach offers a more efficient and cost-effective means of exploring the intricate relationship between parameters and covetic material quality.

Hello, my name is Diego Espinoza and I am physics student at George Mason university working with Dr. Pilgyu Kang. Today I will talk about the work I have been doing under him. Optimizing Graphene Metal Composite Production with Laser Modelling.

We are going to start with defining a composite material, which are engineered materials made from two or more different materials. This new material will have new physical or chemical properties.

A good example of one is steel, which is the combination of Iron and Carbon. One process of making steel is by the use of a furnace which reaches extremely high temperatures and the carbon and iron are mixed.

This composite material obtains improvements in properties like strenght and thoughness.

Now that we know what composite materials are, we are going to see a type of composite material which is obtained by integrating graphene, a 2d layer structure of carbon atoms, into a metal. This new materials can show improvements durability, mechanical strenght, thermal and electrical conductivity.

A way of forming this composite material is by the used of a laser, which can be compare to a furnace for the case of steel. This method allows for a more controlled and homogenous structure. However, the optimal laser parameters like its power, the speed, the lasing time or the frequency are not known. Everytime a study is done, the people working with the materials have to start from 0, constantly creating different samples with different parameters and studying them to see which ones are better.

The project we have been working on intends to adress these challenges. By relating the temperature distribution to the formation of the induced graphene in the metal, we are working on computer simulation that shows us the temperature distribution across the material during the lasing process. During the process we study and used the material properties and how some of them can be changed during the temperature changes. Currently we are still working on the modelling and we are improving it week by week. The overall objective is to have a computational model that, by just changing the materials or the laser parameters, a good approximation of the temperature distribution, and therefore the graphene formation can be obtained.

College of Engineering and Computing Honors College OSCAR

Privacy Concerns vs Product Desires: Unpacking Big Tech and the User Dilemma

Author(s): Mercy Wolverton

Mentor(s): Collin Hawley, Honors College

Data privacy scholarship follows a historical trajectory mirroring the evolution of digital technologies. As technology became ubiquitous in households, studies explored the relationship between its integration into daily life and users‘ data privacy behavior. Researchers found that there was a “Privacy Paradox” where users understand risks associated with their behavior yet continue to behave in an unsafe way (Aivazpour 5).

Studies such as Heinrich and Gerhart‘s found that “while students express concern for their privacy when using mobile devices and express an intent to use additional privacy-enhancing technology, their behavior using mobile device protections does not change, even after an educational intervention” (1). Aivazpour‘s study found that “both the Big Five variables and the impulsivity variables are significant predictors of information disclosure independent of each other” (1). Additionally, research pursues social theories such as the “lemming effect”, investigating how social and peer pressures shaped individuals’ conformity to data-protection norms (Synman et al 1).

However, Synman‘s research has only taken place in Australia (1). Furthermore, the “lemming effect” is not the exclusive social theory to possibly explain the influence of a group on an individual and future research should also be done regarding other social theories (Synman et al 1). My research is dedicated to unraveling the complexities of data privacy behaviors within the broader societal context. Employing a quantitative approach, I utilized a modified Likert scale-type questionnaire, drawing from the Human Aspects of Information Security Questionnaire (HAIS-Q), to gather insights from random GMU students. Through surveys and data analysis, I seek to uncover patterns and predictors of data privacy behaviors, with a particular focus on understanding the enduring ‘Privacy Paradox.’ Preliminary findings suggest a significant disparity between users’ stated concerns about privacy and their actual behaviors, highlighting the need for further exploration into the factors driving this phenomenon.

Have you ever stopped to think about the sheer volume of personal data generated by your online interactions? Think about it. Every search query, every post, every click contributes to a treasure trove of personal information. From our preferences and habits to our locations and interests our digital footprint tells a detailed story about who we are. But who’s collecting all this data? Big tech companies like Google, Facebook, and Amazon are constantly harvesting our information to tailor advertisements, improve user experiences, and even influence our behaviors. In fact, Big Tech enjoys the benefits of network effects, where each additional user improves the value of the product. With this phenomenon, users aren’t paying in monetary values instead users attention funds Big Tech fueling the expansion of their data empires. However amidst this data driven landscape there’s a glaring lack of regulations in place to protect users with current regulations like the FTC Section 5 focusing primarily on antitrust indirectly and unreliably managing the implications of big data and data privacy. This leaves the role of protecting personal data in the hands of the user. Yet amidst growing concerns about data privacy there’s a fascinating paradox at play. While many express apprehensions about the extent of data collection, our actual behaviors often contradict these sentiments. This phenomenon, known as the Privacy Paradox, highlights the complex contradiction between our knowledge and actions in the digital sphere. The question now is why? Research suggests that our peers can play a significant role in shaping our actions. So whether it’s through social norms or direct influence could the choices of those around us impact our own decisions regarding data privacy? Through surveys and data analysis my research this spring at George Mason University delves deep into the Dynamics of data privacy. Utilizing a quantitative approach I designed a modified Five Point Likert scale questionnaire based off the human aspects of information security questionnaire. The questionnaire focuses on key areas such as password management, email use, internet use, social media use, and mobile devices. Data was gathered from a diverse sample of GMU undergraduate students with particular attention to possible social influences from their peers data privacy behaviors. Upon conducting quick analysis of survey responses intriguing disparities emerge. For instance when asked about accessing dubious or suspicious sites 98% of respondents correctly identified the associated risks. However when presented with scenarios related to peer Behavior the disparity between knowledge and actual behavior became evident. For instance when asked about visiting questionable websites for free resources given that their peers do a significant portion of respondents indicated agreement despite potential concerns about safety and legitimacy. These findings underscore the complex interplay between individual knowledge, peer influence and actual behaviors when it comes to data privacy. But an unexpected complexity also introduced itself when looking at the data safe behaviors related to internet use compared to that of mobile devices there were greatly varying extents of the Privacy Paradox effect. While the majority of respondents would contradict their knowledge when it comes to internet use, considerably less would do so when it comes to mobile device security. By unpacking these nuances my research while just in the beginning stages aims to provide information so that we can better understand how to mitigate the risks posed by the ever evolving landscape of data privacy. Moving forward I will continue to analyze survey data conduct further tests to further explore these dynamics specifically pursuing the influence of peer behavior as well as other possible subtopic specific influences. By identifying patterns and trends we can develop targeted interventions to bridge the gap between awareness and action in the realm of data privacy. Ultimately my goal is to contribute to a more privacy conscious society where individuals are empowered to protect their digital identities and navigate the digital landscape with confidence.

College of Engineering and Computing OSCAR

Quantitative Analysis of Surgical Intervention on Strabismus Using Hess Charts

Author(s): Thomas Chang

Mentor(s): Qi Wei, Department of Bioengineering

Strabismus (crossed eyes) is a prevalent vision disorder that involves binocular misalignment. It is usually treated surgically but with suboptimal outcomes. Evaluating the quantitative surgical effects of different operations on treating strabismus is valuable to guide future surgical treatment. Inferior rectus (IR) recession, inferior oblique (IO) recession, and their combination are common operations for treating the most common type of strabismus, Superior Oblique (SO) Palsy. To characterize these surgeries we used Hess Screening Tests (HST) results measured at 21 unique gaze positions from before and after treatment. HST charts were automatically translated through a MatLab program to determine the horizontal and vertical positions of each corresponding gaze. Of the reviewed clinical summaries, 19 patients met the inclusion criteria and underwent either ipsilateral IO recession (N=5), contralateral IR recession (N=4), or both (N=10). Early correction was calculated as the difference between the pre- and post-operation HST changes and late correction was calculated similarly between the follow-up and post-operation changes. Of the early correction caused by each surgical technique, IO recession produced the most incomitant correction. IR recession produced nearly comitant correction with less magnitude than IO recession. Combined IO & IR recession created incomitant correction greatest in IO‘s field of action & double that of IO recession alone. Analysis of the contralateral eye showed the expected, inverted results. This suggests that there is significant correction caused by surgical intervention and a difference between each surgical technique which behaves according to the field of action affected.

Hi, my name is Thomas Chang I’m in the bioengineering department and I will be presenting on the quantitative analysis on superior oblique palsy surgical outcomes using Hess screening tests. My mentor for this project is Dr. Wei.
Some background on the issue; strabismus, also known as crossed eyes, is misalignment of the eyes due to defects in neural control or extraocular muscle balance. On the right is an image of the 6 extraocular muscles. Superior oblique palsy, or trochlear nerve palsy, is when the superior oblique muscle atrophies, hindering the eye‘s ability to move in certain ways causing strabismus. The goal for this project is to quantify some commonly used surgical interventions to better understand how changing the eye will impact its movement and hopefully advise future surgeries.
To do this we used hess screening test results. Here are two examples of Hess tests, where 21 different gaze positions were plotted on a grid where each box represents 5 degrees and the center of the box is the primary gaze. The top image is of a pre-surgery test, and as you can see a lot of the gaze positions are further from the ideal positions marked by the orange dots as compared to the bottom image which is of a post-surgery test. We automatically translated these graphs and subtracted them to find vector fields characteristic of each surgery.
Here are the group and results mentioned. The first group is of patients who underwent recession of the antagonist muscle, the inferior oblique; the second group is of patients who underwent surgery on their unaffected eye‘s inferior rectus which controls downward movement; and the third group is of patients who underwent both of the previous surgeries. Two main things to note are the incomitant correction, which is saying that each gaze position changes a different amount and the general trend towards the ideal gaze positions marked by a blue dot.
Here is the same data but focusing on the vertical displacements scaled onto a heatmap. These show a much clearer correlation between each surgery and the field of action of the inferior oblique groups
Finally, we looked at post-surgery results for patients who came back 3-7 months from the date of their surgery. These all were scaled up by 3 times, so progression especially on the IO & IR group are a little misleading, but the main thing observed was the tendency for IO recession patients to see further correction towards the ideal position.
Some acknowledgments for this project include Dr. Lee and the OSCAR office. Dr. Demer and Dr Lai from the Jules Stein Eye Institute in UCLA, Andrew Ryan who made the program to translate the charts, and Dr. Wei for helping with ideas, figures, code, and being a great mentor. Thank you.

College of Engineering and Computing OSCAR Summer Team Impact Project

RMT PROJECT

Author(s): Karmel Webster, Quentin Sanders

Mentor(s): Quentin Sanders, Mechanical Engineering and Bioengineering

Cerebral Palsy (CP) is the most common motor disability among children. The disorder develops when a child experiences neurological damage during the prenatal, perinatal, or early postnatal periods. As a result, children with CP often have significant sensorimotor impairments, specifically in the upper extremities. Rehabilitation strategies with the strongest clinical evidence for restoration of upper extremity motor function include Constraint Induced Movement Therapy and Hand-Arm Bimanual Training. But, these options may be better suited for individuals with mild to moderate levels of impairment and may not be applicable for more severely impaired children. For children with more severe levels of upper limb impairment, mirror therapy could be a more feasible approach. Conventional mirror therapy consists of covering the impaired hand while the unimpaired hand performs exercises in front of a mirror. However, this limits sensory feedback to the user while simultaneously failing to provide a realistic training scenario that mimics movements used during everyday activities. The use of robotics combined with mirror therapy could potentially enhance the effects of traditional mirror therapy as robotic devices can be used to provide haptic feedback to the user. To explore this concept, this current work presents the design of a bilateral haptic robot with flexion / extension of the elbow joint. Here, measured movement of one limb connected to a sensorized gantry enclosed timing belt controls movement of a timing belt linear actuator connected to the other limb. Future work will evaluate the feasibility of this approach in children with CP.

Summer 2023 Oscar Celebration Video Transcript
July 31, 2023
Mentor: Dr. Quentin Sanders
OSCAR Student: Karmel Webster

INTRODUCTION AUDIO:
The focus of my research project for this Summer was to develop a rehabilitation robotic device specifically for the upper extremities for children with Cerebral Palsy. According to the Center for Disease Control, Cerebral Palsy is the most common motor disability in children today. CP develops after a child has experienced brain damage while in the womb or shortly after birth, which results in a decreased range of motion as well as sporadic movements of either the upper and/or lower extremities. Other rehabilitation strategies have been utilized to treat children with CP (such as Constraint-Induced Therapy), but due to there being limited treatment options and a need for more devices that treat severe cases of CP, additional rehabilitation methods have been explored, such as conventional mirror therapy.
Conventional mirror therapy works by covering the affected hand, while the unaffected hand remains uncovered to perform exercises in a mirror.
but the downside of conventional mirror therapy is that it only provides visual feedback and lacks sensory feedback, which limits the therapeutic benefit while simultaneously failing to provide an accurate training scenario that mimics movements used during everyday activities.
As a result, the objective of this research project was to develop a bilateral haptic robot that employs the concept of traditional mirror therapy by providing both visual and sensory feedback through execution of flexion and extension movements of the elbow.

CONTROLS AUDIO:
In order to develop this device, my mentor, Dr. Sanders, first taught me about the controls needed for this robot ,which first focused on using a Potentiometer (which is a variable resistor, that produces an output value from 0-1023) in order to control the position of a DC/direct current motor.
——————————————————————————————————————————————
Over the course of the Summer, I learned that PID control is a commonly used feedback loop system which stands for Proportional, Integral and Derivative. The proportional term is calculated by multiply the proportional constant (kp) by the error term, and is therefore directly responsible for decreasing the error in the calculated control signal, while the integral and derivative terms are calculated using the finite difference theorem (which is shown in the picture) in order to smooth out the control signal.
——————————————————————————————————————————————
The constants Kp,Ki, and Kd dictate how much each term will be represented when the control signal is calculated.
——————————————————————————————————————————————
Ultimately, I learned that PID controls the position of a motor by measuring the original position of the motor (using the encoders on the motor), and subtracting this value from the target position (which is the position of the potentiometer) in order to calculate the error term.
Once the error term was calculated, it was sent to the controller (which was the Arduino Uno) in order to be converted into a control signal that ultimately traveled to the plant (which is the DC Motor).
——————————————————————————————————————————————
This will turn the motor shaft clockwise or counter clockwise, which is dictated by the control signal and is displayed here in the video:
——————————————————————————————————————————————
learning this information allowed for the robot to be controlled using a DC motor, an Arduino mega, and a rotary encoder (instead of a potentiometer).
——————————————————————————————————————————————
DESIGN:
The next part of this project focused on the design aspects needed for the elbow module of the robot, which is displayed here in the design sketch along with the name of each component. The elbow module of the robot contains a motorized side (right side) and a sensorized side (left side), due to containing a motor and rotary encoder. In regard to the functionality of this robot, as the patient moves the rotary cart hand platform on the sensorized side, the hand cart will move the timing belt along 2 linear shafts. As the timing belt moves, it will simultaneously move the small metal pulley attached to the rotary encoder shaft, ultimately changing its position. This position change will then travel to the microcontroller (an Arduino mega), which will then convey a control signal message to the DC motor. When the control signal reaches the motor, the patient’s hand located on the right platform (which is the motorized side), will move in response to the position change from the rotary encoder.
—————————————————————————————————————————————— While some robot parts were purchased, I learned how to use the Cading Software called Fusion 360 in order to build 3D support models needed for the robot. This included:
A large 3D mount used to hold one side of the pulley and timing belt in place, which is show here in picture from the sketch diagram, as well as the CAD model and the printed 3D model.
——————————————————————————————————————————————
Smaller 3D mounts used to hold the long linear shafts in place.
——————————————————————————————————————————————
The hand clip, hand rotary cart, and hand platform for the patient to grab onto and push in order to move the timing belt along the linear shafts.
——————————————————————————————————————————————
A motor stand and rotary encoder stand to support and prevent excessive movement of the motor and encoder, while also securing the second pulleys on the opposite sides of the timing belt.
——————————————————————————————————————————————

Here is a picture of the right side of the robot (which is the motorized side of the robot). The left side of this robot (which is the sensorized side), is still being developed.
CONCLUSION:
Though the motorized side for this device has been developed, and the development of the sensorized side is still underway, we plan to implement pronation/supination movements to this robot for wrist movement, as well as a grasping motion to the robot for the patient’s hands.

Thank you!

College of Engineering and Computing OSCAR

A Machine-Learning Approach for Studying Disinfectant By-Products (DBPs)

Author(s): Isaac Amouzou

Mentor(s): Ben Seiyon Lee, Department of Statistics

Disinfectant byproducts or DBPs for short are chemical compounds that form when disinfectants in water (ex: chlorine) react with natural organic matter. Chronic DBP exposure can cause significant negative health effects, such as bladder cancer, colon cancer, and pregnancy complications. DBP exposure is difficult to measure so a surrogate is needed to model DBP exposure. In the previous spring we developed statistical models that could accurately model the relationship between specific DBPs (selected using a feature selection method called lasso regression), categorical variables and toxic DBP classes when taking into account the public water system of origin. This project developed a classification framework for detecting at-risk public water systems using a machine learing method known as feed-forward neural networks. We also created an interactive tool for allowing anyone regardless of prior experience to explore the data used for the project, perform predictions using the previously developed models on the DBP classes, and evaluate the models with the DBP classes using either ICR data that contained data from over 13,000 measurements from 295 different public water systems or from data given by the user.

hello I am Isaac Amouzou and this is my project a machine learning approach for studying disinfectant byproducts or dvps disinfectant byproducts or chemical compounds that form when disinfectants in water react with natural organic matter some negative health effects that can come from chronic disinfectant byproduct exposure are bladder cancer colon cancer or pregnancy complications highly disinfectant water sources can lead to DBP exposure to ensure drinking water safety it is imperative that DBP levels and public water sources be properly monitored

dbp exposure is difficult to measure directly so surrogates have been used for example trihalomethanes or THMs are easier to measure and it was believed that THM concentrations are proportional to concentrations of other dbp classes a past study examined the link between THMs and a dbp class Haloacetonitriles or Hans the data consisted of over 9500 measurements from 248 Public Water Systems the result was that thms could only explain 30% of the variance in HANconcentrations

previously in Spring We examined four DBP classes using statistical models for this current project our goals were to create a framework to classify at-risk Public Water Systems given a DBP threshold using machine learning methods and to create a publicly available interactive tool to explore our data to generate and download predictions and evaluate model performance our data is from the information collection request database from the Environmental Protection Agency the data set contains over 13 000 measurements from 295 Public Water Systems

this is our framework for classifying at-risk Public Water Systems for class the classification we use a neural network model in this section we prepare the data to be processed and passed into the neural network for classification here we created a threshold to classify at-risk water systems and to prepare the DBP target class haa9 a specific threshold we used here was 72 micrograms per liter and we did 1.5 standard deviations off that limit so any public water system that is above 72 minus one and a half standard deviations will be classified as at risk and any below will be classified as save here we split the data into training and validation sets this is done because when we want to train our new network model we want to split up the validation sets so we can train with the specific training set and then only validate with data that hasn’t been seen before

here we want to prepare our data loader and batch data and batching is essential for stochastic gradient descent or SGD because the model updates its perimeters after processing each batch instead of updating after processing the entire data set this introduces Randomness in the update process and can lead to faster convergence and better generalization

this is the structure of a feed forward neural network a feed fowardr neural network is consisted of an input layer one or more hidden layers and then to an output layer each layer in the neural network will consist of a neuron which is a node that can take input and pass output a neuron also performs a computation with weights and biases before passing the computation through an activation function which is a function that adds non-linearity to a neural network which helps it the model learn complex relationships some common activation function are relu tanh or softMax here is the training and validation procedure for our neural network

here’s our neural network structure we have 326 inputs and two outputs with where the two outputs are either at risk which is represented by one meaning above the threshold or zero which means not at risk or below the threshold we have one hidden layer of 164 neurons and we have selected railroad as our activation function

here are the metrics we will use we have Precision which is sometimes known as positive predictive rate it is a measure for evaluating how well a model is at classifying positive or in this case at risk Public Water Systems and as well as recall which is sometimes known as true positive rate which is a measure for evaluating situations when for all cases where the data or at-risk Public Water Systems can be identified as positive and how accurate the model is at classifying them as positive

here we have an Epoch which is a single pass through the entire training data set and in the process of turning this model we have we use 18 epochs this is the final evaluation section and here we have a recall score of 0.72 of 72 percent the Precision score of 0.79 or 79 in an accuracy score of 0.83 or 83 which is just checking out how accurate a model was at classification

this interactive tool was made using R shiny the first panel shows a map of the Public Water Systems being used here on the all tab this denotes all public water systems for each DBP class when hovering over a specific marker you will be able to see the average concentrations of these DBP classes the public water system ID and the public water system name when you click on a marker you’ll be presented with a link to the EPA water system report on the public the specific public water system as well as the icr water system report

when you select a DBP from the drop down you it will you’ll be shown all public water systems that reported that specific DBP level and it will denote how close they are to a risk threshold with red being above the risk threshold yellow being within one standard deviation of the risk threshold and green being more than one standard deviation away from the risk threshold

on the predict panel if you have data that you would like to generate predictions for you can upload it as a CSV file like so and the dashboard will automatically remove any rows that have missing DBP or categorical values you can select which model you would like to predict with as well as which DBP class you would like to predict and generate your predictions here you can see the predicted value of the DBP class HKS or haloketones and the actual value although to generate predictions you do not need the actual value and if you want to download the data you can just simply click this button and download it as well

on the evaluation panel similarly you will also have to select a model that you wish to evaluate as well as which class you would like to evaluate the model on and again I’ll select hello ketones and you have two options here if you have data that you wish to test the model on yourself you can input the data as well as a similar process before and I’ll also remove any missing rows this time you actually will need the actual the class value in order to evaluate how well the model performs you’ll still have to click the evaluate button and you will get these metrics that appear the average prediction error is the average difference between the predicted DBP and the actual DBP the average percentage prediction error is the average percentage difference between the predictions and the actual DBP MSE or mean squared error is assesses the average square difference between the observed and predicted values rmsc is the square root of MSE so it’s called root mean squared error and while MSE measures the units that are the square of the target variable rmsc measures this in the same units as the target variable and here adjusted r squared is the proportion of the DBP class with the model can explain and I could also evaluate to see that a linear mix model as well if I wanted to use the icr data I can input seed which allows for random selection and reproducibility of results I can also input the training proportion which is how much of the data set the model will use for training so in this case 0.7 or 70% of the data will be used for training and I can evaluate here

the metrics are going to be the same but instead they are calculated on a test set which is kept separate from the training set here we also again have the test average prediction error which is the average prediction error on the test set the average percentage prediction error on the test set as well and MSE and RMSE on the test set too the new metrics we have here are a conditional r squared which is the proportion of the DBP class and in this case HKS are haloketones that the model can explain and the margin r squared which is the proportion of the DBP class that the fix effects or variables without taking into account public water system levels can explain and this is an interactive tool that you can use to explore the data as well as predict and evaluate the models using the models using neural networks we have created a framework to classify water systems that fall above or below a risk threshold we have created an interactive tool that will allow the users to examine the data that was used predict with and evaluate using the models and we have we will we plan to continue to develop and improve our classification framework and interactive tool we also plan to implement imputation to take into account the missing records for certain DBP classes

and these are citations and thank you for listening to my presentation