As global energy systems transition toward renewable sources such as wind and solar, the demand for durable and cost-effective large-scale energy storage continues to grow. Redox flow batteries (RFBs) are promising candidates due to their independent scaling of energy and power, long operational lifetimes, and use of nonflammable aqueous electrolytes. Aqueous organic redox flow batteries (AORFBs) require active materials with highly soluble and stable redox states under ambient conditions. Viologens are among the most promising anolytes because of their synthetic tunability, high solubility, and access to two redox couples near the water stability window. However, practical two-electron cycling remains unrealized due to the poor solubility, precipitation, and oxygen sensitivity of the fully reduced state (V⁰). Here, we show that replacing conventional alkali-metal electrolytes with tetraalkylammonium cations enables stable two-electron cycling in aqueous systems. Using 1 M TBACl or TBABr, bis-sulfonated propyl viologen ((SPr)₂V) and Dextrosil-viologen (Dex-Vi) exhibit <1% capacity fade per day under ambient air, enabled by enhanced solubility of V⁰ and elimination of precipitation. TBA⁺ also suppresses reactivity with reactive oxygen species, limiting peroxide-driven degradation. Mechanistic studies reveal that TBA⁺ stabilizes reduced viologen species, inhibits radical dimerization, and modifies the interfacial environment to hinder oxygen-related degradation. Additionally, dV/dQ-based diagnostic tools enable real-time monitoring of multi-electron cycling behavior. These findings establish TBA⁺ electrolytes as a general strategy for enabling oxygen-tolerant, high-density AORFBs without controlled environments. 

Antibiotic-resistant pathogens pose a critical threat to global public health. While wastewater treatment utilizes disinfection methods aimed at eradicating waterborne pathogens, such methods can inadvertently bolster the antibiotic resistance of pathogenic bacteria and non-pathogens alike. With wastewater effluent serving as a reservoir, pathogenic bacteria carrying antibiotic resistance genes (ARGs) continue to disseminate across global water sources. This project focuses on utilizing bacteriophages (phages) to combat antibiotic-resistant bacteria in a wastewater treatment process where their application has yet to be investigated: the anaerobic membrane bioreactor (AnMBR). Given the diverse range of potential bacterial groups carrying resistance, the issue of phage host specificity remains outstanding. To address this, methodologies have been developed to isolate polyvalent phages, i.e., those that can infect pathogenic bacteria at the interspecies level. To achieve this, five characterized and sequenced bacterial hosts with multidrug resistance have been cultured from a lab-scale AnMBR and utilized in the process of isolating polyvalent phages. Four phages have been isolated and have shown polyvalency on at least one other host strain, highlighting a promising avenue for safeguarding emerging microbial risks in mainstream anaerobic wastewater treatment systems. 

Efficient face ventilation remains a persistent challenge in underground room-and-pillar coal mines. In the United States, remote-controlled continuous miners frequently employ extended cuts—advancing up to 40 ft beyond the last line of permanent roof supports—to improve productivity. Under these conditions, conventional line-brattice systems often fail to deliver adequate airflow, resulting in elevated respirable dust and methane concentrations and increased safety risks. This research introduces a next-generation, data-driven ventilation enhancement through the integration of passive vortex generators (VGs) onto airflow control devices within a blowing ventilation system. A high-fidelity 3D CAD model of a room-and-pillar face, incorporating the continuous miner, line brattice, and VG assemblies, was developed to accurately represent field conditions. Using ANSYS Fluent, comprehensive CFD simulations examine airflow behavior across multiple VG configurations, varying dimensions, spacing, and surface placement to identify the most effective designs. Results indicate improvements of up to 11.2 % in airflow penetration to the extended face, significantly enhancing dust and gas dilution. This passive, CFD-validated solution offers a practical and impactful advancement for face ventilation, improving safety and operational efficiency in U.S. underground coal mining. 

Methane is one of the most dangerous and environmentally significant hazards in underground coal mines. Methane accumulation can lead to explosions, resulting in fatalities, greenhouse gas emissions, and loss of production. However, current methane monitoring systems are primarily reactive, creating a gap in early hazard detection and prevention. This study aims to build a deep learning model for early methane hazard detection in real-time using data from a lab-scale simulation rig. To achieve the aim of this study, a laboratory-scale underground coal mine simulation rig has been developed. A spatiotemporal graph attention-gated re-current unit (ST-GAT-GRU) model is then employed to predict gas evolution across the sensor network in the simulation rig. This approach enables the proposed model to integrate the mine geometry, environmental measurements, and temporal trends into a unified framework for early hazard detection and scenario analysis. The model was validated using data from the simulation rig, achieving a strong predictive performance with an R-squared of 0.71 and a reliable detection above a specified safety threshold of 1500 ppm. The findings from this study demonstrate that the ST-GAT-GRU architecture provides accurate, stable, and operationally meaningful gas concentration forecasts. Unlike traditional methods, the graph-based model captures how gas propagates through the mine network, allowing it to capture hazardous conditions before they occur. 

This project investigates the transient behavior of a Discontinuous Conduction Mode (DCM) flyback converter operating at high switching frequency. A flyback converter is a DC–DC converter inspired by the buck–boost converter, where the output voltage relative to the input voltage can be either amplified or reduced. A key difference is that a flyback converter provides electrical isolation due to the coupled inductor element, which helps minimize electromagnetic interference within the circuit compared to a traditional buck–boost converter. When the switching frequency increases, parasitic effects such as switching losses and parasitic capacitance begin to affect the desired output performance. To minimize these effects, the circuit is designed on a printed circuit board (PCB) and incorporates a hand-designed coupled inductor, a resistor-capacitor-diode (RCD) snubber circuit, and a fast-switching Gallium Nitride (GaN) MOSFET. Custom measurement tools, including a D-dot probe for voltage and a Rogowski coil for current, are developed to capture output waveforms.The inductor design is experimentally varied using a range of core sizes and wire winding methods to better understand the magnetic components of the circuit. To evaluate the effects of different design parameters on converter performance, the inductor design, input voltage, and switching frequency are varied. Efficiency and switching waveforms are then compared and analyzed. 

Underground mining remains one of the most hazardous environments, especially during emergencies when visibility is reduced, structures collapse, and hazardous gases spread rapidly. In these situations, rapid and informed decision-making is critical, yet conventional tools such as hand-held sensors and stationary monitors often fail to provide the real-time insight needed during a disaster. These environments are also typically GPS-denied, limiting the effectiveness of autonomous platforms such as drones and ground robots.

To address these challenges, this thesis presents the design and deployment of the Sensor Egg, a bio-inspired environmental sensor node developed for autonomous coordination and rapid response in underground settings. Designed to be impact-resistant and self-righting, each Sensor Egg measures airborne particulate matter, gas concentration, temperature, and humidity. The system wirelessly transmits this data, allowing human operators and unmanned vehicles, including UGVs and UAVs, to assess environmental conditions in real time. By supplying environmental data to path-planning algorithms such as A*, Sensor Eggs enable autonomous vehicles to identify safer routes, avoid hazardous areas, and improve performance during rescue and evacuation operations. The overall goal is to reduce human exposure to danger while improving the speed and effectiveness of emergency response. This work contributes a novel deployable monitoring system that connects environmental sensing with robotic navigation. Beyond mining, the Sensor Egg concept may also support disaster response, exploration in GPS-denied environments, and safer autonomous system operations. 

In this study, we present a multifunctional mechano-optoelectronic (MO) thin film fabricated using a novel Super Inkjet (SIJ) printing process to create a conductive and flexible polymeric circuit for a self-powered epidermal strain sensor for measuring in-plane strains on human skin. We hypothesize that the MO thin film can generate a measurable direct current (DC) under light that varies predictably with applied strain, enabling strain sensing without an external power source. The MO thin film generates DC under light, and this output is used as a signal to detect in-plane strain based on its unique MO characteristics. Using the SIJ printer, conductive polymeric thin films are printed onto a biocompatible silicone-based elastomer as a bottom electrode. The MO thin film is then deposited via spin-coating as the active sensing component, and liquid metal is applied as a top electrode. The sensor is tested under cyclic tensile loading while measuring DC output under simulated solar illumination. Results show that the DC output varies consistently with the applied strain, validating its usability as a self-powered strain sensor. 

This research investigates the aerodynamic performance and energy-harvesting potential of various dandelion-inspired micro flyers designed for remote sensing in inaccessible environments. Natural dandelion seeds are known to be light weight. Their unique characteristics such as porosity and pappus angle creates a separated vortex ring that enables it to fly to several miles of distances using only wind. This study utilizes the dandelion seed morphology to optimize the structural and flow characteristics of ultra porous pappus geometries. Attached to these models are micro piezoelectric bending transducers needed to generate low-power energy to provide meaningful electricity to onboard sensors. The generated electrical energy is harvested from the mechanical vibrations of the geometry model during flight. A combined computational and multiphysics approach is employed. Computational fluid dynamics (CFD) simulations are conducted to evaluate drag coefficients and vortex ring formation across the multiple geometric configurations at different flow velocities. The resulting aerodynamic forces are then coupled with structural and piezoelectric models to estimate how deformation and stress distribution produce a meaningful electrical output. Results indicate that specific pappus configurations promote the formation of stable separated vortex rings which enhance drag and improve flight stability. Designs with optimized filament spacing reduce wake instability and produce more consistent aerodynamic behavior. Preliminary piezoelectric analysis suggests that flow-induced deformation of the structures is likely to generate measurable electrical power. 

Radio frequency plasma discharges are an important tool in many industrial processes such as the fabrication of microelectronics. Thus, detailed characterization of their behavior is key to improving the capability of modern manufacturing techniques. Light emission, which is ubiquitous with any plasma discharge, may influence plasma properties via interaction between plasma species and photons, or through light-material interactions. Here, a capacitively coupled plasma (CPP) is generated between 2 cylindrical electrodes with a gap spacing of 3 cm in Argon to study spatially resolved light emission. An RF generator providing a 20 W 13.56 MHz signal with an average reflectant of 2 ± 1 W ignites and maintains the plasma. Visually, it is observed that there are two intensity maxima peaks near the electrodes at 176mTorr. Conversely, only a single intensity peak is observed in the middle of the gap when the pressure is decreased to ~76mTorr. This light emission is quantified using spatially-resolved optical emission spectroscopy (OES). Light emission from the plasma is directed into an Andor Kymera 328i spectrometer using a pair of plano-convex lenses and detected on a Hamamatsu photomultiplier tube. Both total intensity and individual line intensities are examined as a function of distance away from the cathode. The speculation is that there is a transition from γ-mode (observed double peak intensity) to α-mode (single peak intensity) within the gap based on the dependence of the pressure. 

Fluorine-18 (18F) is a crucial isotope for Positron Emission Tomography (PET), conventionally produced with proton cyclotron accelerators with the 18O(p, n)18F reaction. While effective, this method requires expensive equipment that is not widely available. Due to the short, 2 hr half-life of F-18, this decreases the availability of this crucial isotope. This project explores an alternative production pathway utilizing the 19F(γ, n)18F reaction via electron linear accelerators (linacs), which are more widely available in hospital settings and may provide a path for more accessible and local production. A major challenge in photonuclear isotope production is the separation of chemically identical parent-daughter pairs, such as 19F and 18F. To address this, Kinematic Recoil is investigated as a physical separation method. Kinematic Recoil occurs when a target 19F is irradiated with a high energy photon, the daughter 18F nucleus recoils and is physically captured using strategically placed catcher materials. Presented are experimental results for kinematic recoil. Findings aim to establish the feasibility of producing 18F using linacs and physical separation techniques. 

Autonomous precision landing of unmanned aerial vehicles (UAVs) remains a critical challenge, particularly in environments where Global Positioning System (GPS) signals are unreliable or insufficient for accurate localization. This work is motivated by the hypothesis that vision-based perception using fiducial markers can significantly enhance landing accuracy and robustness compared to non-visual approaches. To investigate this, a vision-based autonomous landing framework is developed using AprilTag detection and pose estimation, integrated with a closed-loop control strategy. The system is implemented and evaluated within a high-fidelity simulation environment based on PX4 Software-In-The-Loop (SITL) and Gazebo, enabling controlled, repeatable, and systematic experimentation. Performance is quantitatively assessed using metrics such as landing accuracy, success rate, detection stability, and time-to-land across multiple test scenarios. The results demonstrate that the proposed AprilTag-based approach consistently achieves high landing precision and reliability, significantly reducing positional error and improving success rates compared to baseline methods without visual guidance. These findings validate the effectiveness of vision-based control for UAV landing and highlight the importance of structured simulation-based evaluation. The proposed framework provides a reproducible benchmark for future research and establishes a foundation for subsequent real-world implementation and sim-to-real transfer. 

Traumatic brain injuries (TBIs) affect thousands of people annually in military, sports, and civilian populations. However, TBIs are under-diagnosed and under-investigated due to challenges in monitoring in vivo brain tissue mechanics during head insults. A critical damage location is the gray-white matter interface due to the mechanical stiffness difference between tissues. Utilizing a custom shear device, we deformed ex vivo bovine tissue to identify strain concentrations at the gray-white matter interface and reproduce it with multi-layer hydrogels sharing a cross-linked interface. Strain and strain rate measured with digital image correlation at a frame rate of 10,000 fps demonstrated replication of known damage-inducing parameters. To quantify cellular damage at these interfaces, a cerebral organoid-on-a-chip (COCh) was fabricated. These models consist of a polyacrylamide hydrogel, which mechanically mimics brain tissue, with embedded cerebral organoids. A multi-layer gray-white matter COCh was fabricated to place a cerebral organoid under deformation at interfaces and to quantify damage for comparison to a single-layer COCh model. Under high loading conditions, single-layer COCh models show that embedded organoids experience strains and strain rates capable of inducing cellular damage, whereas the multi-layer COCh did not due to limited interactions at the organoid-hydrogel interface. Ultimately, development and characterization of the COCh under TBI-like loading conditions may allow injury threshold identification by correlating strains and strain rates in organoids with biological response. Finally, these chips can be placed into full-scale brain phantoms and exposed to realistic TBI scenarios informing equipment design and preventative training policies in military and sports applications. 

Capacitively coupled radio frequency (RF) powered plasmas are created by applying a high frequency to a set of electrodes in a gaseous medium. The plasma sheath is a small boundary layer between the plasma and the electrodes and controls much of the transport of energy out of the plasma. Here, the emission spectra propagating through the sheath will be observed through the bottom electrode at varying pressures to study the transition from a collisionless to a collisional sheath region. The experimental setup operates at 13.56 MHz and has a consistent gap distance of 3 cm immersed in Argon gas. Plasma characteristics such as Debye length, electron temperature, pressure, ion and electron distribution functions are key characteristics to defining the sheath. A Langmuir probe is used to measure the bulk plasma density and temperature and validate the size of the sheath. Thus, these results provide insight into the plasma sheath region for further knowledge on boundary layer physics applications.

This work was funded under the US Department of Energy (DOE) Fusion Energy Sciences (FES) EPSCoR program, award #DE-SC0025549. 

The scalable manufacturing of mechano-optoelectronic (MO) strain sensors is limited by conventional methods like spin-coating. This work investigates airbrush spray coating as a scalable alternative for poly(3-hexylthiophene) (P3HT) based MO thin films, focusing on understanding the process-structure-property (PSP) relationships critical for reliable performance. A systematic study evaluated how processing parameters (substrate temperature, nozzle distance, thickness, and multi-walled carbon nanotube (MWCNT) incorporation) affect nanostructure and device behavior. Nanostructure was analyzed using ultraviolet-visible spectroscopy and grazing-incidence wide-angle X-ray scattering (GIWAXS) to quantify polymer aggregation and strain-induced crystallite orientation. Optical microscopy, a convolutional neural network-based analysis pipeline and other imaging techniques were used for film morphological analysis. Device performance was evaluated via current-voltage measurements and cyclic mechanical testing. It was found that airbrush deposition creates heterogeneous, droplet-driven morphologies. While this reduces baseline electrical performance compared to spin-coated films, it enhances strain sensitivity due to increased nanostructural responsiveness. GIWAXS showed greater strain-induced crystallite reorientation in airbrushed films. It was also found that low MWCNT concentrations act as scaffolds, facilitating strain-induced molecular alignment and improving strain sensitivity, while higher concentrations degrade performance. These findings demonstrate that airbrushed MO device performance can be precisely tuned through control of processing conditions. Airbrushed devices exhibit a fundamentally distinct response compared to spin-coated systems and are validated as a promising scalable alternative. 

The nature of the situations in underground mines is complex and high-risk, so human decision-making directly influences survival in an emergency. The paper will provide virtual reality (VR) simulation and graph-based machine learning to model and predict the choices of navigation in these conditions. The hypothesis is that all three factors, movement trajectories, environmental hazards, and indicators of human factors, can be combined to predict human behavior of navigation. A VR system was created that would simulate emergency exit out of an underground mine. Respondents would be exposed to conditions of different degrees of visibility, exposure to hazards, and guidance. Their paths were taken on a graph form, with decision points being the nodes and potential paths being the edges. Prediction of machine learning models was done on navigation decision, decision timing, and task success using Markov models and graph neural networks with recurrent components. Human factors which included fatigue, situational awareness, cognitive load and trust were included based on the post-experiment survey data and were correlated with the behavioral measures such as path efficiency and decision time. The findings reveal that the graph models are superior compared with the traditional probabilistic models, especially in complex environments. Human factors enhance interpretability and behavior variability. The results indicate the possibilities of utilizing VR and machine learning to learn and predict human decisions and improve safety training and decision-support systems in underground mining. 

Changes in the Epidemiological Landscape of Candidemia in New Mexico

Bloodstream infections caused by Candida species, known as candidemia, are serious infections that can lead to prolonged hospital stays and death, especially among immunocompromised patients. Transmission can be healthcare-associated including via intravenous catheters and other internal medical devices which can serve as entry points for infection. In recent years, the clinical landscape has changed from infections primarily due to Candida albicans to non-albicans species including C. glabrata, C. parapsilosis, C. tropicalis, C. krusei and C. lusitanae. This study investigates the epidemiological landscape of infectious agents causing candidemia in Bernalillo County, New Mexico by analyzing species distribution, patient demographics, and clinical outcomes using active population-based surveillance data from the New Mexico Emerging Infectious Program, a collaboration between the University of New Mexico and the New Mexico Department of Health, from 2017-2024. Additional data was provided on antifungal susceptibility testing of clinical isolates using minimum inhibitory concentrations performed at clinical reference laboratories. Candida glabrata was the most prevalent non-albicans species and demonstrated fluconazole resistance in 88.3% of isolates. All Candida parapsilosis isolates were susceptible to fluconazole and micafungin, while Candida tropicalis showed fluconazole resistance in 20% of isolates. Understanding these trends can inform targeted infection control strategies and identify population risk groups. 

While weather stations have been around since the 17th century, they still are not able to measure or predict all weather. This is because ground weather stations have a small radius (5 to 10 meters) requiring big cities, like Los Angeles, New York City and even Albuquerque to have to have multiple stations to pinpoint a storm. If you live in a rural part of a state you most likely don’t have a ground weather station and have to rely on satellite tracking. In spite of this, weather prediction is challenging, because the one thing most ground weather stations miss is tornadoes and flooding caused by relentless rain. This is due to the fact that ground stations can only predict thunderstorms, never the actual amount of rain a region will get. Furthermore, the stations can only predict the region of where a tornado will land, and that region is massive. The worst part is that most weather stations, ground and space, are owned by the government and/or non-profit. Consequently, cut funding from these stations mean that the people in the area don’t get notified when a disaster happens. With the rise of technology, like 3D printers and the internet, anyone can make a ground weather station, which is what we plan to do. This project uses the 3D-PAWS (3D Printing Automated Weather Station) manual to build and test a weather station. 

Radio recombination lines (RRL) are a tracer of ionized gas in Galactic HII regions, which are regions around young, hot stars where hydrogen gets ionized. Recent interferometric RRL observations reveal velocity gradients via Doppler shift across some nebulae, which have been interpreted as bulk rotation of the region. We use the Australia Telescope Compact Array to obtain higher angular resolution RRL maps of several Galactic HII regions in order to test this hypothesis. These data reveal a significantly more complicated kinematic environment, which draws into question the bulk rotation hypothesis. We explore the possibility that the observed RRL kinematics trace turbulence in the ionized gas. We generate synthetic RRL data cubes from random statistical Kolmogorov-like density and velocity fields. Qualitatively, these simple simulations reproduce the observed RRL kinematics in Galactic HII regions. Quantitatively, we use these simulations to estimate the sonic Mach number under the assumption of Kolmogorov-like turbulence. We find that supersonic turbulence is able to explain the observed ionized gas kinematics in Galactic HII regions. RRLs may thus provide a direct tracer of turbulence in massive star forming regions and constrain the scales of energy injection in the interstellar medium. Project direction help from Dr. Trey Wenger at CSUChico and paper writing help from Dr. Brian Svoboda. 

Mechanoluminescent materials as self-powered strain sensors have been a matter of interest for researchers for a long time now. The sensors work by applying strain on the matrix which induces a piezoelectric field in the embedded phosphors that release electrons, which emit visible light. The spectral characteristics of the emitted lights can reveal the type and intensity of the mechanical stimuli acting on the material. However, obtaining said spectral data can be costly and time consuming as spectroradiometers are quite expensive and the measurement requires a proper laboratory setup, which is not practical in practical use cases. Furthermore, the emitted light may not be bright enough for the spectroradiometers to detect properly. Hyperspectral regeneration is a method where the 3 channels of an RGB image can be subdivided into multiple channels that all can have intensity data for different wavelengths and can produce a similar spectrograph like data from RGB images. This project uses hyperspectral reconstruction of RGB images taken from a high-speed camera to generate the spectral information from the light emitted from the stress sensors and observe how the response changes with gradual change in strain. 

Mining operations are critical to the global economy. However, exposure to metals and metalloids in these environments poses significant risks to human health, primarily through inhalation of respirable dust. Mine workers are continuously exposed to fine particulate matter generated during drilling, blasting, and material handling. Among these, respirable crystalline silica (RCS) is of particular concern due to its strong association with silicosis and other respiratory diseases. The physicochemical properties of dust from metal, nonmetal, and aggregate (MNM) mining operations, along with their dissolution behavior in biological fluids, remain poorly understood. This study investigates the physicochemical characteristics and toxicological impacts of inhalable MNM dust to elucidate how metal and metalloid release in lung environments contributes to occupational health risks. Dust samples from four aggregate mines and one metal mine were analyzed, with respirable fractions (<10 µm) isolated using a cascade impactor. Comprehensive characterization was performed using scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD), alongside microwave-assisted acid digestion followed by inductively coupled plasma mass spectrometry (ICP-MS). Bioleaching experiments were conducted in batch reactors using simulated lung fluids, including Gamble’s solution and Artificial Lysosomal Fluid (ALF). Results demonstrate significant dissolution of Si, Al, Fe, Mn, Cu, and Ti, with release patterns closely linked to mineralogical composition and surface chemistry. Cell-based assays further reveal reduced viability and altered immune responses following dust exposure. These findings provide critical insight into the relationship between dust physicochemical properties and toxicological outcomes, emphasizing the need for site-specific risk assessments based on geological variability.