Session E01: Poster Session (4:30 pm - 6:30 pm ET)

Validating a measure of STEM Students' SE for a Mixed Methods Research Design

Self-efficacy, crucial for academic achievement in STEM, is typically assessed using pre-post measurements from surveys. However, developing a way to assess in-the-moment impacts to students' self-efficacy would create many opportunities for further study and intervention. With this goal, we devised a mixed-methods approach combining the Experience Sampling Method (ESM) with individualized daily journal prompts. While rich, this design poses validation challenges. To address these challenges, we analyzed three ESM survey questions, indicative of task-level self-efficacy, for skewness. Deviations from normal distributions suggest that traditional validation techniques may not apply. We further examined the Pearson's coefficient of skewness for each item and participant that does not assume the normal distribution of data. We would expect that if all ESM survey items are aligned with self-efficacy then the skewness for each individual should be consistent across those items. In this [talk/poster], we will present the shifts in skewness between items and discuss the data from validation interviews that expand on our quantitative findings.   

Leveraging the Bernstein-Vazirani Algorithm for Intuitive Quantum Computing Education

The Deutsch algorithm has been the algorithm of choice for those seeking to educate the public and undergraduate students about quantum computing.  For a single, self-contained introductory lecture, it has no equal.  However, while the algorithm itself was a ground-breaking achievement and a valuable teaching tool, its results can seem abstract and unsatisfying to students.  We argue that the Bernstein-Vazirani algorithm can pick up where the Deutsch algorithm left off.  That is, it is an algorithm that might be mathematically more complex, but is likely to be seen as more intuitive; as well as a more satisfying demonstration of the superiority of quantum computing.  Furthermore, in the context of a full undergraduate course, the Bernstein-Vazirani algorithm can be broken down into a series of programming assignments, each of which will force the student to confront fundamental concepts of quantum mechanics and quantum computing.  We present a detailed summary of the Bernstein-Vazirani algorithm and compare its complexity to the classical analog.   

Exploring Scientific Ethics Requirements for U.S. Undergraduate Students in Physics

In the scientific professions, ethics address interpersonal topics (e.g., equity and inclusion; workplace conduct) as well as topics surrounding scientific conduct (e.g., preventing data falsification). The APS Ethics Committee was established in 2018 in order to address these issues within the American physics community. Additionally, the APS adopted a robust written stance on scientific ethics in 2019 with Statement 19.1, Guidelines on Ethics. As professional scientific bodies work towards creating a more ethics-conscious community, one might wonder whether U.S. colleges are preparing STEM students to join such a community. At Eastern Michigan University, PHY 406: Ethical Issues in Physics is required for a B. S. in Physics. We examined whether this is a common requirement of Bachelor degrees in Physics among 10 institutions in each of four categories: large private, large public, small private, and small public. Searching each course catalog for physics courses using keywords "ethics," "research conduct," etc. yielded few results: when available, these courses tend to be electives–not requirements. We repeated this process for Biology and Chemistry B.S. degrees. Our data suggest that when scientific ethics courses are offered, they tend to be offered only to Biology or Chemistry students; when they are offered to Physics students, such courses are not required for graduation.   

Cybersecurity of Process Control Systems

Chemical processes are controlled using control systems. However, these can be attacked. In this poster, we will highlight our group's work in using the process dynamics as a means to aid with cyberattack detection, including strategies for handling attacks on all sensors and simultaneous actuator and sensor attacks.   

Unlocking the Future of Phi-bits: Advancing Quantum-Inspired Computing

Understanding the control of phi-bits, akin to qubits, is crucial for developing quantum-inspired computing. Phi-bits, or two states of an acoustic wave in coupled waveguides, can be in a superposition of states. Our experiments showed that external drivers' frequency, amplitude, and phase influence phi-bit states. We developed a discrete element model to predict phi-bit responses under varying nonlinear conditions, influenced by the intrinsic medium coupling the waveguides and external factors like signal generators and transducers. The study reveals that nonlinearity and damping significantly affect the amplitude and phase of phi-bit states, with a notable impact on their predictability and stability, particularly at high damping levels. These findings are crucial to manipulating phi-bits for quantum-inspired information processing, highlighting the importance of optimizing nonlinearity and damping to control phi-bit states.   

Advances in Next Generation Cyber-Physical Systems: Cybersecurity and Quantum Computing

Two topics of my doctoral thesis will be discussed in this work. The first topic develops cyber-secure control strategies by taking advantage of process physics based on the conservation of mass and energy. Theoretical guarantees of safety and stability are made while accounting for practical challenges such as handling process disturbances, noise and changing process dynamics. The processes considered are characterized by nonlinear dynamics, which represent the majority of real-world processes, using a control algorithm based on an optimization-based model predictive control algorithm that is modified to also detect for cyberattacks. Multiple detection strategies, such as comparing state predictions/estimates to measurements or regularly probing for cyberattacks on control elements. The comparison of state predictions/estimates to measurements has seen limited success in terms of the duration for which theoretical guarantees hold, especially when changing process dynamics are considered. The detection strategy that probes for cyberattacks has seen significant use in making long-term guarantees of stability and feasibility until a cyberattack is detected.

The cyberattack detection strategies are computationally expensive and motivate the consideration of quantum computing to implement control algorithms. Two major obstacles encountered with quantum computations include non-determinism introduced by quantum noise inherent to quantum devices and the determination of a control algorithm that outperforms its classical counterparts. The first step to address these challenges was a detailed analysis of simple control algorithms to study the impacts of quantum noise on stabilizing control strategies in various scenarios. This was achieved using a quantum simulator provided by IBM’s quantum experience, Qiskit. The long-term goal of exploring this topic is to build a foundation to identify scenarios where quantum computing can be taken advantage of for applications in the field of process engineering and control.   

Exploration of the Quantum Coherent States Through Hertz-Type Classical Nonlinearity

In quantum computing and information technology, the coherent superposition of states is an essential topic for realizing the physical state of data processing and storage. The fundamentals of current technology, a quantum bit, have limitations due to the collapse and decoherence of wave function, which hinders the superposition of states. We eliminate the limitations by introducing the elastic bit generated through the Hertz-type nonlinearity of granular beads. This study shows the experimental formation of the elastic bit in a coupled granular network manipulated by external harmonic excitation. The excitation generates a phase-dependent dynamic movement, and mapping onto the energy states of the linear vibration modes forms the coherent superposition of states. This state vector component comes from the amplitude of the coherent states, which is projected into the Hilbert space through time dependency. The coherent states represent an actual amplitude, which makes the elastic bit susceptible to decoherence. The elastic bit also demonstrates quantum operation, showcasing the Hadamard gate, which maps one superposed state to another. These characteristics of the elastic bit pave the way for sustainable quantum computation and data storage.   

PIC Simulations to Investigate the Influence of Initial Target Conditions on Short-Pulse Laser Neutron Production

Neutrons generated through tabletop laser-driven interactions offer benefits and complement traditional methods, allowing for progress in many applications such as neutron imaging and next-generation medical treatments. This study utilizes Particle-In-Cell (PIC) simulations to explore how initial conditions influence neutron generation from interactions between high-intensity, short-pulse lasers and deuterium oxide (D2O) targets. Several initial conditions were investigated, including the target thickness, target temperature, and chamber background pressure. Additional features were added such as heating profiles to mimic laser pre-pulse heating within the target. Findings indicate that these initial conditions play a significant role in neutron yield. Future work will further explore initial conditions and work towards benchmarking simulation results with experimental data to enhance neutron production for real-world applications.   

Meta-Analysis of Laser-Ion Acceleration

We showcase in-depth data extraction and analysis of existing laser-ion particle accelerator experiments, utilizing tools like Plot Digitizer for accurate data extraction from graphs. We compiled over 400 data points from 26 scientific papers on laser proton acceleration. Features collected include wavelength, intensity, pulse duration, target thickness, and the resulting maximum proton energy for each experiment. This will be the largest publicly available dataset, which will ensure organization and accessibility for ongoing and future research. We analyzed this dataset using a variety of statistical methods including correlation matrices, principal component analysis, and linear regression to uncover underlying patterns, outliers, and insight into designing future experiments, simulations, and machine learning models.    

Impact of Nuclear Level Densities on Astrophysical Reaction Rates

Nuclear Level Densities (NLDs) are key inputs for calculating reaction rates for uses in astrophysics, medical physics, and industry. The NLD is defined as the number of energy levels per energy interval for a nucleus, which increases as energy increases. We calculate NLDs for various energy levels using the Moments Method. We then use our results and the program TALYS in order to calculate nuclear reaction rates in astrophysical environments. More specifically, we calculate the cross sections of (n,gamma) reactions, describing the probabilities of the reaction occurring under given conditions. These reactions take place in environments with an abundance of neutrons, such as neutron stars, core collapse supernovae, and neutron star mergers. We compare our cross sections from TALYS with available experimental data as well as other theoretical models, including the Fermi Gas model and the Constant Temperature model.   

Numerical Solutions to McVittie Timelike Geodesics

Numerical solutions are provided to timelike geodesics within the Schwarzschild and McVittie metrics. The Schwarzschild metric represents a static, non-spinning black hole. The McVittie metric appears to be Schwarzschild close to the origin, but an expanding FLRW space (cosmology) far away. The main goal of this research is to show the difference in the orbital and gravitational wave patterns between static and expanding spacetimes. Both FLRW and Schwarzschild-DeSitter spacetimes are discussed within the numerical context of calculating geodesics. The numerical method used is an 8th order Runge-Kutta coded within Python.   

Detection of Near-Earth Asteroids Using the LISA Spacecraft

In this project, I will examine a secondary of the European Space Agency's future space mission, the Laser Interferometer Space Antenna (LISA), as an asteroid detection device. To this end, I will create a simulation of the LISA spacecraft and an asteroid with variable mass and orbit. To analyze the model and the detection of the asteroid, I will measure the gravitational acceleration of each individual spacecraft due to the asteroid using the orbital parameters of various known asteroids. Using this data, I will examine the influence of mass and closest-approach distance on the gravitational force applied to the spacecraft. Based on my simulation results and analysis, I will determine the validity of this use case of LISA. If LISA can reasonably be used in this manner, I will report the limitations found in testing. With a positive yield of data, this project can contribute to filling NASA's Small-Body Database and give LISA the secondary purpose of detecting asteroid threats to Earth.   

Simulations of Interference Effects of Dual Laser Pulse Plasma Interactions

We used the open-source simulation code WarpX to model dual lasers colliding with a thin plasma target. These simulations are useful for studying high magnetic fields like those expected with magnetic reconnection in stars. We explore how changing the separation between the interaction points of the lasers affects magnetic fields and particle dynamics. We find that the lower the separation, the higher the particle energy generated.   

Measurement of the Optical Transfer Function Using Single Pixel Imaging

An optical transfer function is a function that describes how well a system can capture different spatial frequencies and it is possible to calculate the optical transfer function of any system with just four images. We started by projecting 4 sinusoidal illumination patterns with phase shifts of π/2 onto the object plane of the camera lens and by choosing a single pixel on all 4 images we then calculated the optical transfer function. Then we calculated the inverse Fourier Transform of the optical transfer function to determine the point spread function of the camera lens. Furthermore, by testing multiple different points on a set of images it is possible to prove that the choice of pixel while imaging is arbitrary and all pixels in the image work equally as well. Lastly, by performing the process over a large range of the sinusoidal frequencies the performance at higher frequencies can be determined because as spatial frequency increases high detail imaging becomes more difficult.   

Electrical Anisotropy in InSe/MoO3 heterostructure 2D Field Effect Transistor

Van-der-Waals heterostructures can exhibit electronic properties that are not present in their constitutes due to proximity effects, making them of great fundamental and technological interest. We investigate the gating behaviour of a heterostructure of CVD-grown low-symmetry MoO3 stacked on the top of high-mobility isotropic InSe. We fabricate four-probe devices and investigate the emergence of electrical anisotropy in the heterostructure by estimating the field effect mobility along two different directions.    

Fractional Resistivity Change Of Au(111) Thin Films Dosed With Diethyl Disulfide

At a base pressure of 2.5 x 10^-10 Torr, the surfaces of 150 nm thick Au(111) films were Ar+ ion sputter cleaned and annealed to ~500℃. The resistance change of the Au(111) films due to diethyl disulfide dosing was measured using a four-probe, lock-in technique. The adsorbate-induced change in resistance determined the fractional change in resistivity. Auger spectroscopy determined surface composition, and the effect of cleanliness of the sample on the fractional change in resistivity was studied.   

Nanostructured Materials for Advanced Rechargeable Battery Systems

As the demand for portable devices and electric vehicles continues to skyrocket, there is an urgent need to develop rechargeable batteries with enhanced capabilities. These include high energy density for extended runtimes, increased cycle life for longer usage, advanced discharge performance to provide an optimal power output, low thermal decay to protect internal stability, minimal environmental impact and ethical resource collection, and many more.

 

This research conducted at the University of Arkansas aimed to improve the performance of Nickel-rich Lithium-ion batteries (LIBs), as well as address their associated challenges (i.e. capacity fade, voltage decay, dendrite formation). The findings of this research provide insight into the development of high-performance, reliable, and safe Nickel-rich LIBs, and contributes to the advancement of renewable energy storage systems.   

Practical Control Laws with Quantum Computation

Quantum computing has been receiving interest in chemical engineering. Chemical engineering involves a broad range of science and engineering problems related for example to transport phenomena, optimization, design and modeling of complex systems. In addition, quantum computing has been receiving engineering attention for applications such as control, where proportional control algorithms implemented on quantum computers were investigated.  In this presentation, we will discuss how an integer program might be developed for attempting to learn an algorithm for quantum chemistry computations for a single qubit, and how the single qubit would not be able to take advantage of all of the properties of quantum computers. We expand the discussion here to present how integer programs might in general be developed for gate selection for specific state measurement/control output relationships to find the gates which can represent a specific action desired by a control law. However, we also will discuss how this does not constitute learning an "algorithm" like the Quantum Fourier Transform-based addition algorithm, because such algorithms are able to adapt a relationship that can be coded to different numbers of total qubits and should correspond to different state/input relationships without the need to "re-code" them.   

Predicting Synthesis–Structure Relationships in Epitaxially–Grown Semiconductors with Quantum and Classical Supervised Learning

In this work, data detailing hundreds of plasma–assisted molecular beam epitaxy (PAMBE) thin film material growth experiments each of ZnO and various nitride semiconductors have been organized into separate, composition–specific data sets. For each experiment, the complete set of PAMBE growth parameters are associated with binary measures of crystallinity (1 for monocrystalline, 0 for polycrystalline) and surface morphology (1 for atomically–flat, 0 for uneven) as determined by reflection high–energy electron diffraction (RHEED) patterns. Additionally, a Brag–Williams measure of lattice disorder (S²) is included as an additional figure of merit for investigation. Quantum as well as conventional supervised learning algorithms – including logistic regression, tree–based algorithms, and quantum support vector machines – are trained on the data to study which growth parameters are most statistically important for influencing crystallinity, surface morphology, and S². The probabilities of obtaining monocrystalline and atomically flat thin film crystals are predicted across processing spaces of the two most statistically significant synthesis parameters. S² is also predicted across the same growth spaces. The predictions indicate that different growth conditions are of interest depending on whether a single crystalline sample, a flat surface, or a well–ordered lattice is desired.   

Considerations of Closed-loop Control on Quantum Computers using a Modified Grover's Algorithm for Simulation of a Chemical Process

Quantum computers are of increasing interest in engineering applications such as modeling, optimization, and machine learning. Another field is process control, which involves the use of automated computer systems to manage process operation, where the method by which quantum algorithms might be implemented is unclear. The use of quantum computations in control may introduce factors that need to be accounted for such as nondeterminism, noise, and rounding effects. Our initial research sought to understand the effects of rounding and nondeterminism when implementing control on quantum computers. This was addressed though the design of a quantum circuit, which utilized many sequential Grover’s algorithm gates to encode a lookup table representing a controller, and through a control-theoretic study using an advanced control framework called Lyapunov-based economic model predictive control. This work focuses on extending our work through the application of the algorithm and theory using a chemical process example. This includes multiple objectives, namely concerns raised from the creation of the lookup table, a method of modifying the quantum circuit to prevent selecting unwanted control inputs, and the use of auxiliary classical control to ensure system stability.   

Determining the Motion of an Anisotropic Particle Around an Optical Nanofiber

We theoretically explore the interaction between electromagnetic radiation and an optically small anisotropic particle by deriving the torque and force due to any electromagnetic field on such a particle. We apply the general torque and force equations derived to the case of an evanescent field outside an optical nanofiber. We analytically determine and graph the particle orientation and velocity vs. time for various polarization states of the electromagnetic  field.  We find that, for all polarization ellipticities ranging from linear up to and including a nearly circular threshold state, an anisotropic particle will assume an equilibrium orientation with respect to the polarization ellipse of the electric field, and the particle will move along a helical path at a constant speed.  For a purely circular polarized field, an anisotropic particle will spin at a constant rate, but the trajectory remains helical.  For polarization ellipticities between threshold and purely circular, an anisotropic particle will spin at a non-constant rate, and a component of the velocity will vary sinusoidally with time.   

Active Phytochemical Components in the Momordica Charantia and Alkaloids for the Type 2 Diabetes Mellitus Treatment

Momordica charantia has been used in traditional medicine to treat various diseases, including Type 2 Diabetes Mellitus (T2DM). This therapeutic effect is attributed to its rich content of bioactive phytochemical components and alkaloids. The active compounds in Momordica charantia help improve insulin sensitivity and offer potential protective effects against diabetes-induced complications via various biochemical pathways. This paper studied how Charantin acts synergistically with other compounds in bitter melon and contributes to its hypoglycemic effects by enhancing glucose uptake and metabolism. Vicine, which is a pyrimidine nucleoside that occurs naturally in Momordica charantia, was also studied to determine the genetic effect. Although its mechanism of action is not fully known, there is evidence suggesting that vicine induces hypoglycemia by mimicking insulin action.

Using computer simulations, various compounds such as Momordicin, Polypeptide-p, and Alkaloids were modeled and analyzed for their functional efficacy. Their structural similarity to insulin allows them to interact with insulin receptors, promoting glucose uptake by cells.

In this research, an open-source molecular editing program with an auto-optimization feature that calculates the theoretical values of a molecule’s physicochemical properties was used to model the nanoscaled compounds.    

Biophysical Properties of Nanoparticles Used in Drug Delivery Systems for the Treatment of Alzheimer's Disease

Dyshomeostasis of redox-active metals and subsequent detection of elevated levels of redox-active metals such as iron and copper in the brain suggests that such metals play a significant role in the pathogenesis of neurodegenerative disease. Iron homeostasis is a key factor in maintaining brain health and preventing common neurodegenerative diseases such as Alzheimer’s Disease (AD) and Parkinson’s Disease (PD), as well as macular degeneration. Under the circumstance that iron is not properly stored away or exported inefficiently, there can be various neuronal complications as a result of an accumulation of redox-active metals. Excess iron can lead to overabundant reactions in the brain, which in turn produce reactive oxygen species (ROS) such as hydroxyl radicals. These cause not only DNA and protein damage but also lipid peroxidation and even cellular death, all of which can contribute to detrimental neurodegenerative diseases.

In this paper, various nanoparticles that have shown promise in Alzheimer's treatment by delivering drugs across the blood-brain barrier were first reviewed. Some polymeric nanoparticles have been explored and modeled using a molecular editing program to check their stability and activity. Improving drug efficacy and reducing toxicity are also studied. This paper aims to model several metal chelators through computer software and investigate the optimal design for such chelators that can be utilized in potential neuro treatments.   

AGN Photometry: UV-IR Color Relations & Automation

This research project involves the analysis of 27 Active Galactic Nuclei (AGN) sources with Ultraviolet (UV) data obtained from a pipeline in various filters. The primary objective was to reproduce the pipeline data using the Swift UVOT software and its diverse methods. However, discrepancies between the measured magnitudes and the original pipeline data were observed, indicating potential differences in aperture sizes.

To address this issue, a Curve of Growth model was employed, where magnitudes were measured for various aperture sizes to study how the magnitude changes with aperture radius. The count rates increased with the aperture radius until reaching a plateau, signifying the inclusion of more background and less of the source beyond a certain radius. Comparison of the Curve of Growth model with the pipeline data demonstrated that the pipeline magnitude values were approached with an increase in aperture radius.

The project further involved comparing the UV data with the Infrared (IR) magnitudes obtained from the Two Micron All Sky Survey for all aperture radii. This comparison provided valuable insights into the star formation rates of galaxies. The difference between the UV and IR magnitudes, along with the corresponding color, served as an indicator of star formation rates. 

An ultimate goal of this project was to automate the entire photometry process to streamline data analysis and ensure more consistent and reliable results. Automating the photometry process would enhance efficiency and reproducibility, allowing for larger sample sizes to be analyzed systematically.   

Using a light-sensitive reaction-diffusion system to visualize the black hole horizon effect

We are using light-sensitive chemical reaction-diffusion waves to visualize a black hole event horizon. The event horizon of a black hole is the radius from its center of gravity at which not even light can escape its gravitational pull, but light is able to enter this region. In our table-top analog, we employ a light-sensitive chemical Belousov-Zhabotinsky (BZ) reaction to create visible fronts moving in a quasi-two-dimensional system. The black hole is created through a radially symmetrical light gradient with increasing intensity going outward until the black hole horizon, where the light intensity drops back to zero. BZ waves created outside our circular light gradient can pass the sharp intensity jump and enter the center regions. Outward moving BZ waves, created at the center, experience an uphill light intensity gradient and die before reaching the maximal radius. Our experiments are supported by Python simulations using a two-variable, light-sensitive reaction-diffusion model, replicating our experimental observations.   

The Search for Nuclear Isomers in Neutron Star Mergers Using GRBs

The observation of the binary neutron star merger GW170817A, marked by both gravitational waves and electromagnetic signals, including the gamma-ray burst GRB 170817A, presents a unique opportunity to study these astrophysical events. The gamma-ray spectrum of GRB 170817A features two distinct components: a hard short pulse followed by a softer thermal emission, with the origin of the hard pulse still widely debated. Our research explores the hypothesis that gamma-ray de-excitations from isomeric transitions contribute to this spectral feature. By establishing a selection criterion for isomers based on solar element abundances, we aim to match the observed GRB spectral characteristics. An interactive, python based webpage has been developed to display gamma-ray spectra from key isomers, enhancing our understanding of nuclear processes in cosmic events. This study not only sheds light on the intricate mechanisms behind gamma-ray bursts but also contributes valuable insights to the field of astrophysics.   

Optical absorption of a stack of razor blades: theory and application

We have analyzed the optical absorption properties of the surface of a stack of double-sided razor blades.Our analysis predicts the amount of light absorbed as a function of the angle of light incidence.In addition, we explore the effect of varying cavity wedge angles.All internal reflections are assumed to be specular.Reflection off of blade edges is ignored, as are any cavity resonance conditions.The predictions are compared to detailed measurements using a laser beam. We relate our findings to issues of laser safety, reference blackbodies, and discuss specialty coatings such as Vantablack.