Redox flow batteries (RFBs) offer promising solutions for large-scale energy storage, boasting high-power density, scalability, and safety. Despite their potential, current RFBs face challenges like resource constraints and high costs. Aqueous organic RFBs could address these issues if expenses are reduced. This study optimizes a promising total aqueous organic RFB chemistry using cost-effective and sustainable materials. Specifically, utilizing (SPr)2V as an anolyte and 4-HO-TEMPO as a catholyte, with benign KCl as the supporting electrolyte functioning through an anion exchange mechanism. (SPr)2V, derived from the viologen class, shows promise due to its high solubility, negative redox potential, and ability to accept 2e- reversibly. The objectives include understanding viologen species degradation, characterization of the degraded products, exploring oxygen and pH susceptibility, and enhancing its performance through modifications like alternative supporting salt anions. This study aims to double the compound's storage capacity by leveraging its 2nd electron. The findings will reveal low-capacity fade rates under certain parameters, with insights into the effects of oxygen, concentration, heating, and pH on dimerization and capacity loss. This study assesses the electrochemical properties using cyclic voltammetry and rotating disk electrode voltammetry. The (Spr)2V/4-HO-TEMPO ARFB has an exceptionally high cell voltage, 1.25 V (1e-) and 1.9 V (2e-). Prototypes of the organic ARFB can be operated at high current densities ranging from 20 to 100 mA cm-2 and deliver stable capacity for 100 cycles with 99+% Coulombic efficiency. This research could pave the way for cost-effective organic−organometallic RFBs, facilitating grid-scale electricity storage and renewable energy integration. 

So far NASA has sent orbiters, rovers, and drones to the surface of Mars. However, these missions were slow and focused on exploring the surface. The harsh conditions on Mars create constraints for what the robotic explorers can or can’t do. One limiting factor is Mars’s rough terrain and harsh environment limit rovers to incredibly low speed. Exploration at low speeds increases mission cost and timeline. However Martian winds are capable of reaching speeds of more than 60 miles per hour, a Martian resource that can be utilized to conduct exploration much faster if the robotic system is designed to utilize it. A Dandelion-inspired robot will utilize the Martian winds to collect various types of data such as climate monitoring to assist in planetary exploration and scientific experimentation. Dandelion seeds are capable of traveling from a few meters to a few kilometers all through wind energy. The vortex generated by a dandelion seed recycles the wind and pushes on the bristles with just enough force to keep the Dandelion seed stable and afloat. A mechanical design that takes inspiration from a dandelion’s geometry can benefit from Martian winds. Micro sensors can be placed in these dandelion-inspired designs and gusts of winds on Mars will carry them forward both in Martian lava tubes where exploration with a rover is challenging and above surface at higher speeds than conventional exploration vehicles are capable of. The micro sensors would be released from a drone or capsule designed for flexibility in the mission requirements.

Additive manufacturing (AM) offers significant advantages in creating complex, customized parts with minimal waste. However, metal AM, particularly with Al-Cu alloys, suffers from defects like cracks and porosity caused by solidification. Computational modelling of the non-equilibrium solidification encountered during AM of Al-Cu alloys can aid in mitigating these defects. This study uses atomistic simulations to model the extreme conditions of AM solidification. We explore how the interface between solid and the melt behaves under extreme thermal gradients, revealing crucial information about interfacial free energy and its anisotropy. We see that the interfacial energy increases linearly with applied thermal gradient, while the anisotropy stays relatively constant. These information are useful for making accurate predictive models for solidification during AM of metals and alloys. 

Quadruped robots have the potential to navigate complex and unstructured environments where wheeled and tracked robots struggle. In underground caves, where lighting conditions are limited and terrain is highly uneven, traditional sensor-based locomotion approaches face significant challenges. This paper explores the use of reinforcement learning (RL) to enable quadruped robots to learn effective locomotion strategies in such constrained environments. An actor-critic algorithm is implemented and trained on a variety of simulated terrain types to ensure robustness and adaptability. The proposed method allows the robot to rely on proprioceptive feedback rather than visual inputs, enabling stable locomotion even in complete darkness. Experimental results demonstrate that the RL-trained quadruped can effectively traverse rough, irregular terrains, maintaining stability and efficiency despite sensor limitations. This study highlights the potential of RL-driven locomotion for autonomous exploration in subterranean environments, contributing to advancements in robotic mobility for search-and-rescue and exploration applications. 

Human Phospholipase A2 (PLA2) has been identified as a potential biomarker for atherosclerosis, cancer, sepsis and other inflammatory conditions. PLA2 catalyzes the hydrolysis of phospholipids. Lipoprotein-associated PLA2 (Lp-PLA2 ),an isoform of human PLA2 , has emerged as a selective biomarker for early diagnosis of cardiovascular disease (CVD). Even though several CVD screening and diagnostic methods are available, they have inherited limitations that prevent selective and sensitive early diagnosis of CVD. Currently, enzyme linked immunosorbent essay (ELISA) is considered the state-of-the art technique in detecting Lp-PLA2 . In ELISA a two-step sandwich immunoassay is utilized to target antibodies to the Lp-PLA2 to measure either the enzyme concentration or activity. Though ELISA-based techniques are generally sensitive, they have inherent drawbacks for early diagnosis that must be addressed by developing new approaches. In this study we will present the development of a microfluidic-based detection method for PLA2 . A simple microfluidic device with supported lipid membranes(SLMs) is considered using soft lithographic techniques. The SLM consist of a mixture of lipids, including anionic, zwitterionic, and fluorescent lipids, which are reactive with PLA2 . The decrease in fluorescence due to the disintegration of SLMs upon hydrolysis is monitored using fluorescence microscopy followed by Image J analysis. In this presentation, we will discuss our success in the detection of PLA2 via microfluidic approaches. 

This study presents a multiphysics approach to advance knowledge about mechanoluminescent (ML) light emission characteristics through a custom-built uniaxial tension test for multiphysics empirical data, image processing, and advanced data processing using machine-learning. The approach predicts spectral data from red, green, blue (RGB) video frames, enabling the investigation of dynamic emission behavior in ZnS-based ML samples. RGB data from multicolored light-emitting diode (LED) lights, paired with spectrometer readings, is utilized to train a neural network. The trained model is applied to analyze RGB video data of ZnS-based ML samples subjected to mechanical strain, with predictions validated against spectrometer data to ensure reliability. Continuous spectrum plots can then be generated by synchronizing video frames with the oscillation cycle of the ML samples. This non-invasive technique is envisioned to provide valuable insights into the temporal progression of emission characteristics under strain, supporting various applications that can benefit from the ML-composites’ real-time and non-invasive attributes. 

The Quesnel Trough, British Columbia, has significant mineral resources and ranks second in mineral commodity revenue in Canada. This project targeted the well-known Cu-Au belts' northern continuations by synoptically analyzing multiple data sets to analyze the terrain. The aim was to capitalize on recent exploration and infrastructural incentives for critical minerals to explore new, high-grade, economically viable deposits under shallow sedimentary cover. The team leveraged on its disciplinary diversity to assess Cu-Mo-Au mineralization potential in the Hogem Plutonic Suite within British Columbia’s Quesnel Trough by integrating geophysics, geochemistry, structural geology, and geometallurgy. The team identified and ranked target areas based on key indicators including intrusive rock age, high-magnetometry and high-gravity anomalies, coupled with geochemical pathfinders as a guide to ranking exploration targets. Geometallurgical analysis emphasized the impact of graphite on gold recovery - recommending a three-stage flotation system. ESG concerns were incorporated to enhance sustainability by including components of tailings management and indigenous land rights. By utilizing AI-driven data analysis and an interdisciplinary approach, this research proposed and demonstrated an optimized exploration strategy, promoting responsible and efficient resource development in BC. By integrating these data sets, we were able to prioritize potential exploration targets and propose drill sites that were maximally likely to yield economically viable Cu deposits. 

Geodesic path planning is crucial in applications such as robotics, computer graphics, and autonomous navigation, focusing on finding the shortest path between two points on a curved surface while accounting for intrinsic geometry. Traditional methods, including energy function minimization, heat flow, and curvature-based techniques, often face local minima and computational inefficiencies, particularly in irregular environments. This paper presents a novel method that integrates the exponential map with the injectivity radius, ensuring globally optimal paths. Our approach avoids local minima, guarantees the shortest path, and provides real-time computational efficiency. Simulations show that our method outperforms existing techniques in optimality, local minima avoidance, and computation time. 

Biofilms are three-dimensional associations of microorganisms and extracellular compounds. Biofilm infections are difficult to treat because they grant antimicrobial resistance and protection from the host immune system when compared to planktonic cells. Both Pseudomonas aeruginosa, an ESKAPE Pathogen, and Stenotrophomonas maltophilia, an emergent pathogen, are biofilm-forming bacteria that are often multidrug resistant, and co-cultures of these bacteria are synergistic in nature. Biofilms formed of both bacteria demonstrate greater surface adherence of P. aeruginosa and greater persistence of S. maltophilia (McDaniel et al., 2022; Brooke, 2021). P. aeruginosa in particular forms biofilms with eDNA as a matrix component, and it been previously demonstrated that the destruction of eDNA can destabilize the biofilm and allow for better access of antimicrobials and immune system cells (Kovach et al., 2020). Here, the effects of phleomycin, a DNA-cleaving antibiotic, on the biofilm forming abilities of P. aeruginosa and S. maltophilia are investigated. Concentrations as low as 0.625 mg/mL phleomycin damaged DNA in vitro, while 50 μg of the compound did not visibly impede P. aeruginosa or S. maltophilia growth. However, phleomycin did not appear to reduce biofilm formation of most P. aeruginosa strains tested or of S. maltophilia SmViv. Only one strain, P. aeruginosa AR356, demonstrated clearly reduced biofilm formation when treated with phleomycin. This suggests that the impact of phleomycin on biofilm formation is strain-specific, which highlights the challenge of treating infections caused by these pathogens. 

Dandelion seeds remain airborne longer than most seeds due to their porous, filamented structure and the formation of a stable vortex ring. This study investigates how the seed’s color affects its thermal and aerodynamic performance. Using 2D axisymmetric CFD models in COMSOL, we compared white, gray, and black seed analogs under varying solar irradiance and ambient temperatures. Results show that increased absorptivity leads to higher surface temperatures, stronger buoyant plumes, and eventual collapse of the stabilizing vortex ring. These findings support the hypothesis that seed whiteness is an evolved trait that maximizes aerial suspension time by minimizing heat absorption. 

Plumes, a fluid phenomenon occurring both naturally and in human industry, have relevance in wildfire detection, volcanology, combustion, pollution monitoring, and numerous other areas. This study employs Computational Fluid Dynamics (CFD) to simulate plume behavior and characteristics. CFD is valuable for obtaining and predicting quantities that can be difficult to measure experimentally. Key results include velocity profiles, velocity plots, and normalization of data. Validating numerical results against established analytical studies contributes to a more comprehensive understanding of plumes and fluid dynamics. Improved descriptions of reality allow for engineered systems and predictive models to be more precise and useful. 

Culverts are essential structures that channel water across or alongside roads and other infrastructure. Their primary function is to facilitate proper drainage, preventing water accumulation, stagnation, erosion, and potential flooding that could compromise structural stability and longevity. Regular inspections and maintenance are crucial as culverts deteriorate over time. However, traditional manual inspections often involve stringent conditions, making them hazardous, time-consuming, and labor-intensive—especially in culverts with poor structural integrity, limited visibility, or confined spaces. These challenges highlight the need for predictive models to automate the inspection process, improving efficiency, accuracy, and safety. In this project, we investigated the use of transfer learning to automatically classify culvert silting conditions based on culvert images. Transfer learning leverages a pre-trained model from a large dataset for a specific task and applies it to a new, related task, significantly reducing training time and computational resources. Our study employed pre-trained convolutional neural network (CNN) models, including ResNet50, EfficientNet, MobileNet, DenseNet121, and VGG11, to extract features from culvert images. The extracted features were then fed into a customized classification head to predict silting conditions. We evaluated model performance using a culvert image dataset collected in the state of New Mexico, categorizing silting conditions into three levels: low (60%). Our results show that the model using EfficientNet as the feature extractor achieved the highest classification performance. Additionally, the customized classification head significantly outperformed traditional machine learning classifiers, including Support Vector Classifier (SVC), Random Forest, and XGBoost. 

The aquatic environmental pollution by microplastics (MPs) has been an emerging threat to whole biosphere. MPs undergo physiochemical degradation in the environment which alter the surface characteristics such as porosity and charge, ultimately converting them as vectors of various chemical and biological pollutants such as heavy metals, toxic polymers, pharmaceuticals, pesticides etc. by adsorbing them through numerous mechanisms from the surrounding cocktail. Hence, these contaminated MPs can be considered as silent killers which cause complex health issues in humans as well as adverse impacts on the entire planet. Therefore, detection and separation of contaminated MPs with heavy metals and other pollutants is a crucial requirement. This study investigates the acoustic behavior of selected heavy metals adsorbed synthetic microplastics and MPs derived from common plastic sources, using acoustofluidic devices fabricated from microfabrication techniques and steel tubes. The adsorption of heavy metals onto MPs was confirmed through Inductively Coupled Plasma Optical Emission Spectroscopy analysis. Acoustofluidics is a simple powerful technology which enhances the automation, high accuracy, precision, rapid real time analysis and low cost. We observed that heavy metal-adsorbed MPs exhibit different acoustofluidic behavior compared to metal-free MPs, making their separation challenging. However, we discovered that a simple calcium pre-treatment can standardize the acoustofluidic behavior of MPs, facilitating uniform separation regardless of metal adsorption. These findings provide valuable insights for developing acoustic-based technologies aimed at isolating heavy metal-contaminated MPs from various water bodies, contributing to more efficient water purification strategies. In this presentation, we will further discuss significant findings achieved. 

R-loops are three-stranded nucleic acid structures with DNA-RNA hybrids and displaced single-stranded DNA that form during transcription and function as double-edged swords. R-loops play a crucial role in various biological processes including gene regulation, DNA replication, and antibody production. However, their persistent accumulation promotes DNA damage and genomic instability resulting in neurological disorders, cancer, and autoimmune diseases in mammals. Consequently, biological systems evolved multitude of intricate mechanisms that utilize hundreds of proteins to counteract detrimental effects of R-loops. In humans, XRN2 (5′–3′ exoribonuclease 2) protein degrades RNA component of R-loops and promotes transcription termination. Dysfunction of XRN2 results in increased R-loop accumulation, heightened DNA damage, and genomic instability, which contribute to cancer development. XRN2’s role in RNA metabolism is well understood, however, its function in preserving genomic stability is incipient. Previous work from our laboratory identified biochemical as well as genetic links between XRN2 and a central DNA damage sensing enzyme, poly (ADP-ribose) polymerase 1 (PARP1), and uncovered an interplay of these two proteins. However, detailed understanding of this interplay in preventing R-loop-induced genomic instability remains elusive. Here, we propose systematic structure-function analysis of XRN2 and PARP1 to gain mechanistic insights into their interplay in genome maintenance. Our preliminary work using biochemical and bioinformatics approaches led us to identify a set of potentially important amino acids to mutate in these proteins. This work will provide fundamental knowledge for developing potential anti-cancer therapeutic strategies. 

The exploration and research of cave and karst systems requires highly-skilled and technical researchers with caving and rock climbing experience. To achieve the critical goal of minimizing impact on delicate ecosystems and physical danger to personnel, a robot is being developed. Robotic systems have been used in other challenging natural environments such as the development of water and dirt resistant actuators. The extension of this robotic method for environment monitoring and research for cave and karst environments is the development of a climbing robot. The Mayhem Robotics Lab is developing a limbed robot capable of navigating vertical surfaces, which will achieve the climbing kinematics found in primates and humans. Climbing kinematics are achieved through the research, design, and implementation of actuators such as pneumatic artificial muscles along with Capstan drive joints. Continuous actuation is developed by using compliant mechanical surfaces to reduce power and computational requirements of the system. A combination of designed and controlled compliance in specific joints while other joints are designed to exhibit more traditional kinematics through controlled actuation methods. The combination of actuation methods allows the robot designer to take advantage of each method's strengths while overcoming its weaknesses. Initial testing for a climbing robot is achieved in a controlled environment with climbing rungs and tethered control and diagnostic systems. The development of a design process for climbing robotics lays the foundation for alternative limbed robots to be fielded in a variety of unknown, and unfriendly environments.