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.

Produced water (PW) generated during oil and gas production poses significant environmental and management challenges due to its large volumes and complex composition. It typically contains dissolved and dispersed hydrocarbons, high salinity, and trace metals, requiring rigorous treatment before discharge or reuse to protect water resources, ecosystems, and human health. In this study, a Direct Contact Membrane Distillation (DCMD)–crystallization system was evaluated for high salinity PW desalination, followed by membrane adsorption for selective lithium recovery. PW from the San Juan Basin in southeastern New Mexico, with a total dissolved solids concentration of 120,000 mg/L, was treated using DCMD operated with a 60°C feed and a 20°C permeate stream. The system achieved a permeate yield of approximately 60%, producing a concentrated brine of 259 g/L and salt rejection greater than 99.9%, confirming robust separation performance under harsh conditions. The concentrated brine was then processed using a functionalized membrane adsorbent for lithium extraction, yielding an adsorption capacity of 17.55 mg/g and a desorption capacity of 12.48 mg/g, indicating effective recovery and regenerability. Overall, the DCMD–crystallization and membrane adsorption approach offers a promising pathway for simultaneous water purification and critical mineral recovery from PW, supporting more sustainable oil and gas operations.

Understanding how and why rocks fail is essential for improving safety in underground mines, where sudden rock collapses or seismic events can put workers and infrastructure at risk. In many cases, failure occurs along faults, which are natural weak surfaces in the rock where movement can happen. This study investigates how the geometry of these faults—whether a single fault or a system of interacting faults—controls how rocks slip under stress. Laboratory experiments were performed on rock samples subjected to confining pressure to simulate underground conditions. During testing, acoustic emissions were recorded. These signals are similar to very small “micro-earthquakes” and provide early clues about internal damage in the rock before visible failure occurs. Because these signals are complex and numerous, we used clustering, a machine learning technique that automatically groups similar patterns, to identify different stages of rock deformation. By converting the signals into visual time–frequency representations and analyzing them with a deep learning model, we were able to detect patterns linked to stable and unstable behavior.

The results show that more complex fault networks produce more variable and distributed damage, while simpler faults lead to more concentrated and predictable failure. Importantly, when complex fault systems fail, they release greater amounts of energy compared to single-fault systems, indicating a higher potential for destructive events. This work demonstrates how combining laboratory experiments with data-driven methods can improve early warning systems and stability assessment in underground mining and other geotechnical applications.

A widespread concern among consumers and policymakers is that expanding renewable energy will make electricity more expensive. This study tests whether states that increased their share of renewable energy generation, including wind, solar, hydroelectric, and geothermal, paid more or less for electricity between 1990 and 2020. We hypothesize that after accounting for pre-existing differences between states, renewable energy expansion is not associated with higher electricity prices. Using publicly available data from the U.S. Energy Information Administration covering all 50 states and Washington D.C., we compare electricity prices across residential, commercial, and industrial customers against each state's renewable energy mix over a 30-year period. To isolate the effect of renewables from unrelated factors like geography and national fuel price swings, we use a statistical method that controls for both state-specific characteristics and economy-wide trends. We find that states with growing renewable shares did not experience systematically higher electricity prices. The relationship between renewables and prices shifted from slightly positive in the 1990s to slightly negative by the 2010s, reflecting falling wind and solar costs. Wind energy shows the most consistent price-reducing effect among individual technologies. These results suggest that the clean energy transition need not come at the expense of affordability, and that expanding wind and solar generation may increasingly benefit electricity consumers.

Robotic-assisted surgery has significantly advanced modern medical procedures by enabling trained professionals to perform minimally invasive operations that reduce trauma and accelerate patient recovery. However, current surgical robotic platforms are extremely expensive, limiting their availability to large, well-funded medical institutions. This financial barrier restricts global access to advanced surgical technologies and reduces opportunities

for research and development in under-resourced arise. This study investigates whether a cost-effective robotic-assisted surgical system can be developed using open-source hardware and software frameworks without compromising precision, reliability, or safety. The motivation for this research is to expand accessibility to

robotic surgical tools and promote innovation in educational and research institutions with limited funding.

The project follows a systems engineering and mechatronics-based design framework. Its primary objective is to demonstrate stable communication between robotic hardware and a ROSS 2-based control architecture, enabling a more accessible development platform. Early testing shows consistent repeatability and accurate end-effector positioning within controlled environments. The system integrates a JetCobot 7 robotic arm with a Jetson microcomputer and an onboard vision system. Operating on Ubuntu with ROSS 2, the platform supports motion planning, inverse kinematics, and real-time control. A virtual reality interface enables

intuitive user input, while software constraints ensure operational safety. Experimental evaluation focuses on positional accuracy, repeatability, latency, and system stability. Results are expected to demonstrate a functional, low-cost prototype capable of precise

manipulation tasks. This research suggests that affordable robotic-assisted surgical systems are feasible, with future work focusing on safety, improved precision, and surgical simulations.

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.

Hydrogen sulfide is a highly toxic and corrosive gas commonly encountered in oil and gas operations, posing serious risks to human health, infrastructure, and the environment. Due to these hazards, stringent regulations limit the venting and flaring of H₂S in many jurisdictions, promoting alternative management strategies such as acid gas injection, where H₂S is co-injected with carbon dioxide into subsurface formations for safe disposal and potential long-term storage. This study investigates the effects of reservoir conditions on acid gas–brine–rock interactions, with a focus on their influence on CO₂ mineralization processes. A numerical core-scale modeling approach is employed to evaluate the coupled effects of varying H₂S–CO₂ mole fractions, brine salinity, reservoir pressure, and temperature on reactive transport behavior. The simulations capture key geochemical processes, including mineral dissolution, precipitation, and changes in fluid composition. Results indicate that the presence of H₂S significantly modifies reaction pathways and kinetics compared to pure CO₂ systems, influencing both mineral assemblages and trapping mechanisms. Variations in salinity, pressure, and temperature further impact the extent and rate of mineralization, highlighting the importance of site-specific reservoir conditions in predicting long-term storage performance. This work provides a core-scale perspective on the geochemical behavior of acid gas systems and offers insights for optimizing AGI operations while ensuring environmental safety and storage integrity.

Human-robot collaboration (HRC) in modern manufacturing continues to grow. Most of the current shared autonomy systems employ non-adaptive control techniques. These non-adaptive control techniques are based on fixed robot behaviors, and thus do not adapt robot actions based on environmental risk or operator's cognitive state. These approaches enhance operator situational awareness and operators are required to make decisions regardless of their mental workload. Therefore, while nearly all of the existing HRC systems are risk-aware, very few have incorporated operator's cognitive status into the autonomous control of robots. This proposal develops a Cognitive Aware Adaptive Autonomy (CAAA) framework that dynamically regulates robot autonomy by integrating both the predicted task risk and the cognitive readiness of the operator. The CAAA framework includes two primary elements. First, an XR-enabled digital twin simulates the collaborative work space, and generates a task risk score (R). Second, a cognitive monitoring module measures operator workload and attention using various behavioral metrics to generate a cognitive score (C). Finally, a decision module integrates R and C to dynamically regulate autonomy of the robot and select one of four operating modes: manual assist, shared control, robot lead, and safety restriction. The proposed system will be tested in a precision assembly study with a 6 degree-of-freedom robotic arm working under disturbances, while also varying the operator's cognitive load. The expected benefits of this system include improved safety, increased precision, better workload balance, and greater operator confidence.