Career, Research, and Innovation Development Conference

Series

Career, Research, and Innovation Development Conference

Permanent Link

https://hdl.handle.net/1853/70851

Series Type

Event Series

Associated Organization(s)

Organizational Unit

Office of Graduate Education

Full item page

Publication Search Results

Now showing 1 - 10 of 32

Superfast excretion of viscous particle-laden droplets in phloem feeding insects

(Georgia Institute of Technology, 2024-02-08) Ha, Nami ; Challita, Elio J. ; Harrison, Jacob S. ; Clark, Elizabeth G. ; Cooperband, Miriam ; Bhamla, Saad

Fluid ejection is a tricky problem in biological systems, especially depending on the size scale and rheological properties of the fluid. An example is sharpshooter insects that feed on xylem sap and expel water droplets using a stylus at an ejection velocity of 0.4 m/s. In contrast to xylem feeders, spotted lanternflies (Lycorma delicatula) are insect species that feed on phloem sap rich in sugars and excrete sticky particle-laden honeydew droplets. Here, we explore how spotted lanternflies (SLFs) can flick sticky honeydew microdroplets. We analyze the rheological properties of fresh honeydew samples excreted by SLFs to quantify the viscosity and surface tension coefficients of honeydew. Using high-speed imaging technique, we found that SLFs fling droplets at an ejection velocity of 1.5 m/s using a stylus to expel their viscous liquid waste, seemingly similar to the catapult system of sharpshooters. The scaling analysis, however, suggests that droplets expelled from SLFs are governed by inertia rather than surface tension (Weber number ~ O(101)) and viscous force (Capillary number ~ O(10-1)), implying the ejection concept differs from sharpshooters that exploit the surface tension-driven droplet superpropulsion. Our findings on the superfast excretion behaviors of SLFs will inform us about how insects at small scales can overcome surface tension and viscous forces. This research sheds light on biofluid dynamics and development of self-cleaning bioinspired fluid ejectors at the millimeter-scale.
Ionic Liquids: Understanding Behavior at Electrochemical Interfaces

(Georgia Institute of Technology, 2024-02-08) Parmar, Shehan ; McDaniel, Jesse

Ionic liquids (ILs) are room-temperature molten salts composed of cation/anion pairs. Over the past several decades, the discovery of new ILs has led to exciting battery electrolyte alternatives that improve energy storage capacity and safety. Understanding compatible ILs for battery applications requires a fundamental understanding of the electrochemical interface—the layer of ILs that accumulate and uniquely order near the charged electrode surface. In this work, we examine how a novel, quaternary ammonium-based IL, methyltrioctylammonium bis(trifluoromethylsulfonyl)imide or [N1888][TFSI], rearranges near a gold electrode surface. We showcase the power of statistical mechanics and advanced computational chemistry methods in interpreting macroscopic implications at the application level via microscopic studies. We compare our simulations with experimental results to improve our current understanding of electrical double layers (EDL) for [N1888][TFSI].
Friendly green OWLS and sound sensing BATS: Biodegradable flexible acoustic sensor and a consumer centric approach towards sustainability

( 2024-02-08) Verma, Harsh Kumar ; Hester, Josiah ; Brettmann, Blair ; Arora, Nivedita

With new technological advancements every decade, devices are becoming smaller, faster, and cheaper. The latest advances in flexible and wearable electronic devices have opened myriad opportunities for applications in fields like robotics, safety and security, healthcare, and IoT devices like flexible smartphones. While this has provided an opportunity to add computational capabilities to everyday objects, it has also made us think about their environmental impacts. Unchecked manufacturing and disposal methods still remain a major challenge. Not to mention the harmful waste from batteries and the electronic waste generated every year. To tackle these challenges, we must think about sustainability as a metric beyond performance and functionality. We must talk about sustainability at every stage of the life cycle of a device. In this project, we introduce a Biodegradable Acoustic Triboelectric Sensor (BATS), a biodegradable flexible microphone based on triboelectric nanogenerators. This project focuses on using environmentally benign processes and chemicals for manufacturing, combined with battery-free operation and biodegradable materials like silk, PLLA, and paper for convenient disposal. Additionally, to make sustainability a consumer-centric subject, we present an Open Way to Look at Sustainability (OWLS), a visual representation of sustainability for our microphone, emphasizing chemical usage, emissions, material selection, and the manufacturing and disposal processes. This idea takes inspiration from nutrition labels on food packaging and energy ratings on electrical equipment that allow a consumer to make the right choices for better nutrition or to save energy and can be more broadly applied to other consumer products in the future.
Increasing Awareness of Human Biases during Visual Data Analysis using Visual and Haptic Feedback

(Georgia Institute of Technology, 2024-02-08) Narechania, Arpit ; Paden, Jamal ; Endert, Alex

Human biases impact the way people analyze data and make decisions. Women denied C-suite promotions (gender bias), ailing but younger people denied optimal treatment (age bias), dark-skinned people denied parole (racial bias), etc. are examples of biases rampant in the world. Visual data analysis tools such as Tableau and Excel help users see and understand their data but do not report potential biases exhibited by users (e.g., an overemphasis on the Age attribute). Existing research tools have explored visual means (e.g., highlighting the Age attribute to appear darker than other attributes) to increase users' awareness about (biased) analytic behaviors. We believe that using a single, "visual" modality to present such information is a passive type of guidance that only burdens the user's perception skills, which are already engaged to perform the analysis task. We investigate how a more active type of guidance, a combination of "visual" and "haptic" modalities, can better guide the user. We present a visual data analysis system, wired to a haptic gaming mouse. This enhanced system tracks a user's interactions with data and presents them back via haptic feedback (e.g., "buzz"es or vibrates the mouse whenever bias is detected). Through an exploratory user study, we find that these dual guidance modalities can sometimes actively stimulate and engage the user's attention, making them more aware of their analytic behaviors. However, we also find that the haptic feedback can also distract the user, informing the design of future multimodal guidance-enriched user interfaces.
From Adoption to Disuse: Investigating the Factors Influencing Disuse of Smart Technologies in Older Adults

(Georgia Institute of Technology, 2024-02-08) Gleaton, Emily C.

The purpose of this poster was to elucidate the qualitative research findings about why older adults adopt and subsequently discontinue using conversational agent technology.
KnowledgeVIS: Visualizing what Language Models have Learned

(Georgia Institute of Technology, 2023-02-01) Coscia, Adam ; Endert, Alex
Skin-like, Soft Patch for Continuous Cognitive Stress Monitoring

(Georgia Institute of Technology, 2023-01-26) Zavanelli, Nathan ; Yeo, Woon-Hong

Here, we present a skin-like, wearable patch microfabricated to seamlessly integrate with the human body and provide high fidelity physiological monitoring in a simple, minimally obtrusive platform. Specifically, this device has been optimized to capture minute chest vibrations produced by the heart’s mechanical beats, referred to as the seismocardiogram (SCG). along with the traditional electrocardiogram (ECG) and pulse oximetry (PPG) signals in a single platform located on the sternum; mathematical tools developed in the study of seismology are then implemented to characterize the heart’s mechanical function and arousal state. In tandem with the PPG and ECG signals, this SCG analysis is used to continuously monitor cognitive stress, which is a notoriously difficult challenge because traditional monitoring signals, like heart rate variability and galvanic skin response are modulated by numerous confounding physiological factors. In contrast, preliminary studies with the soft device demonstrated an r2 correlation with salivary cortisol during controlled stressor activities of 0.81 compared to 0.59 for heart rate variability. Additional clinical testing is being pursued, and should this correlation be proven, this device would represent a substantial improvement in long-duration, continuous stress monitoring in daily life over alternative approaches. This in turn would have wide applications in dementia care, pain assessment, high stress workplace management (e.g., for surgeons and pilots), mental health treatment, and simple wellness.
vitaLITy: Promoting Serendipitous Discovery of Academic Literature

(Georgia Institute of Technology, 2022-01-27) Narechania, Arpit ; Karduni, Alireza ; Wesslen, Ryan ; Wall, Emily

There are a few prominent practices for conducting academic literature reviews, including searching for specific keywords on Google Scholar or checking citations from initial seed paper(s). While these approaches serve a critical purpose for academic literature reviews, there remain challenges in identifying relevant literature when (1) different work may utilize the same terminology (e.g., “transformer” in electronics refers to a device that transfers energy between circuits; whereas in computing, it refers to a type of deep learning model, commonly applied to unstructured text data) or (2) similar work may utilize different terminology (e.g., work on “bias” in visualization seldom mentions “uncertainty” even though bias sometimes emerges when people make decisions under uncertainty). We developed a visual analytics system, VitaLITy, to promote serendipitous discovery of academic papers wherein users may “stumble upon” relevant literature, when other search approaches may fail. VitaLITy (1) utilizes transformer language models to help users find semantically similar papers given a list of seed paper(s) or a working abstract, (2) visualizes the embedding space in an interactive 2-D scatterplot, and (3) summarizes meta information about the paper corpus (e.g., keywords, co-authors, citation counts, and publication year). We also curated a comprehensive dataset comprising papers from 38 popular visualization publication venues (e.g., ACM CHI, IEEE VIS) using custom web-scrapers. We have open-sourced the VitaLITy system, dataset, and web-scrapers at https://vitality-vis.github.io/ for the research community to grow the list of supported venues, potentially expanding into other fields, e.g., biology.
Recurrent Localization Networks applied to the Lippmann-Schwinger Equation

(Georgia Institute of Technology, 2022-01-27) Kelly, Conlain

The process of discovering and designing new materials is very costly, both in terms of time and human effort. One of the most expensive parts is experimentation — before a new material can be trusted, it must first be tested extensively to understand all of its properties. These experiments usually take the form of either physical (real-world) tests or computer simulations. Unfortunately, classical physics-based simulations are still quite slow. In the process of designing a new material, a great number (e.g. thousands, millions) of simulations must be conducted. This constitutes a major bottleneck for discovering new materials. This work explores different methods to accelerate materials discovery by replacing slow, physics-based simulations with faster, reduced-order models based on machine learning. Specifically, the original physical governing equation is converted into an equivalent form more amenable for learning called the Lippmann-Schwinger (L-S) form. A recurrent series of convolutional neural networks (CNNs) is then used to approximately solve the L-S equation. This hybrid architecture, called a recurrent localization network (RLN), leverages the strengths of machine learning while still permitting a physics-based interpretation. As a demonstration, an RLN is trained to solve for interior strain fields of an elastic microstructure, producing high-accuracy results a thousand times faster than a classical Finite-Element simulation. Additionally, this methodology is faster, more accurate, and more interpretable than purely-data-driven models for the same problem. Since a wide range of physical systems can be converted into an equivalent L-S form, this architecture has potential applications across numerous problems in materials science.
Soft Sternal Patch to Detect Sleep Stages and Sleep Apnea

(Georgia Institute of Technology, 2022-01-27) Zavanelli, Nathan ; Yeo, Woon-Hong

Obstructive sleep apnea (OSA) affects over 900 million adults globally, and around eighty percent of cases remain undiagnosed. This critical failure leaves millions of people at an increased risk for serious health complications, like hypertension, obesity, diabetes, and cardiac irregularities. Current diagnostic techniques are fundamentally limited by low throughputs, in the case of polysomnography, and high failure rates, in the case of home sleep tests. Here, we report a wireless, fully integrated, soft sternal patch with mechanics optimized to detect the mechanical, electrical, and optical signals that characterize the cardiovascular response to OSA. Analytical and computational studies in mechanics and material interfaces yielded a fully integrated, multi-sensor system capable of capturing ultrafine, low-frequency, sternal vibrations caused by the heart’s motion, cardiac electrical signals, and optical measurements of arterial blood oxygenation from a single location on the sternum, which has not been previously realized. Advanced digital signal processing and machine learning techniques are used to detect apneas and characterize each event’s acute cardiovascular consequences. In trials with symptomatic and control subjects conducted in their homes, the soft device demonstrates excellent ability to detect blood-oxygen saturation, respiratory effort, respiration rate, heart rate, cardiac pre-ejection period and ejection timing, aortic opening mechanics, heart rate variability, and sleep staging, making it the first single patch capable of capturing all the clinically essential metrics for OSA diagnosis recommended by the American Academy of Sleep Medicine. Finally, these metrics are used to autodetect apneas and hypopneas with 100% sensitivity and 95% precision with symptomatic patients compared to data scored by professionally certified sleep clinicians.