Events

SURE Student Researchers

Spring 2025 will showcase 68 students and over 40 unique research projects at the SURE Poster Fair. Meet the 2025 SURE students and preview their research below.

Note: you can filter projects by department/major.

All Civil & Environmental EngineeringElectrical and Computer EngineeringMechanical EngineeringCIRASystems EngineeringSchool of Biomedical and Chemical Engineering
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A Geospatial Analysis of Wildfire Impacts on Colorado Road Infrastructure and Evacuation Routes

Angel Garcia
Wildfires are unpredictable and destroy Colorado's ecosystems, communities, and infrastructure. As wildfire seasons are becoming longer and more erratic as time goes on due to climate-driven reasons, the ability to predict how a fire will spread within certain conditions, such as wind speed, precipitation, temperature, etc., is essential for the safety of the public and infrastructure. This project focuses on generating wildfire data using FlamMap and Farsite simulation software, due to its computational efficiency, its physical accuracy, and popularity among wildfire simulators. By modeling Colorado’s unique terrain, fuel types, and weather patterns, the research captures essential metrics and details of wildfires, such as: -Arrival Time: At what time will the fire arrive at that spot? -Heat per Unit Area: how hot a fire burns in BTU/ft^2? -Rate of Spread: How fast and far a fire moves? Ultimately, our goal is to overlay our wildfire simulations with the road networks inside Colorado to see which roads are most vulnerable during wildfires, assess the wildfire resilience of Colorado road networks, and see how evacuation routes might be hampered due to wildfires.
Department:
Department of Civil & Environmental Engineering
Faculty Mentor:
Debasish Jana
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Additive Manufacturing and Flash Sintering of High-Entropy Borides

Cooper Lettis
Flash sintering is a faster and more efficient way to densify high entropy ceramics than traditional methods like hot pressing. By focusing a powerful electric field into the sample, flash sintering accelerates mass transport and densification within seconds, offering a promising pathway for the fabrication of materials that are otherwise difficult or time consuming to consolidate. High entropy ceramics are a promising material, where multiple principal elements are incorporated into a structure to enhance thermal stability, hardness, and resistance to oxidation and wear. High-entropy borides represent a rapidly developing subclass of these materials. However, their ultra-high melting points and strong covalent bonding present significant challenges for conventional densification techniques. To address these challenges, this work explores the integration of additive manufacturing and flash sintering. Ceramic pastes are fabricated using a FISNAR precision dispensing system into controlled geometries prior to flash sintering, with the goal of achieving dense, structurally controlled high-entropy boride samples.
Department:
Department of Mechanical Engineering
Faculty Mentor:
Zhe Cheng
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Agentic AI: Adapting Large Language Models to Educate Residents on Time-of-Day Energy Pricing in Fort Collins, Colorado.

Megan Kelly
Observation: In the city of Fort Collins, Colorado, residents may be unaware of the time of day pricing model that Fort Collins Utility utilizes. Residents have an opportunity to better understand how time-of-day pricing influences their monthly electricity bill. We aim to provide an easy system that allows residents to have a better understanding of how they can save money by choosing to run appliances during “off-peak” times. Our agentic AI will provide users with accurate information and guidance on the most cost-effective times to run major household appliances, enabling residents to determine whether the potential savings are more valuable than convenience. Question: To what extent can a Large Language Model based AI agent accurately and effectively educate household residents about the operational costs of major home appliances to reduce energy consumption and costs? Research: Our research encompasses the adaptation of Large Language Models. We have trained previous existing AI models through fine tuning, and Retrieval-Augmented Generation (RAG) to provide Fort Collins residents with accurate information on time of day pricing. Building on others past research, we have developed question-based data sets to support model training and evaluation. We have worked extensively with pulling large data sets from Fort Collins Utility, enabling data-driven analysis and model development. We have combined our original research with established findings to enhance the accuracy and relevance of AI-generated responses. Hypothesis: An AI model that has been trained using a combination of fine-tuning and Retrieval-Augmented Generation (RAG) will be more accurate than a baseline Large Language Model in improving household residents' understanding of appliance operational costs, leading to a measurable increase in energy cost awareness and a reduction in unnecessary electricity expenses.
Department:
Department of Electrical and Computer Engineering
Faculty Mentor:
Tim Hansen
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Agentic AI: Adapting Large Language Models to Educate Residents on Time-of-Day Energy Pricing in Fort Collins, Colorado.

Sierra Nordwald
Observation: In the city of Fort Collins, Colorado, residents may be unaware of the time of day pricing model that Fort Collins Utility utilizes. Residents have an opportunity to better understand how time-of-day pricing influences their monthly electricity bill. We aim to provide an easy system that allows residents to have a better understanding of how they can save money by choosing to run appliances during “off-peak” times. Our agentic AI will provide users with accurate information and guidance on the most cost-effective times to run major household appliances, enabling residents to determine whether the potential savings are more valuable than convenience. Question: To what extent can a Large Language Model based AI agent accurately and effectively educate household residents about the operational costs of major home appliances to reduce energy consumption and costs? Research: Our research encompasses the adaptation of Large Language Models. We have trained previous existing AI models through fine tuning, and Retrieval-Augmented Generation (RAG) to provide Fort Collins residents with accurate information on time of day pricing. Building on others past research, we have developed question-based data sets to support model training and evaluation. We have worked extensively with pulling large data sets from Fort Collins Utility, enabling data-driven analysis and model development. We have combined our original research with established findings to enhance the accuracy and relevance of AI-generated responses. Hypothesis: An AI model that has been trained using a combination of fine-tuning and Retrieval-Augmented Generation (RAG) will be more accurate than a baseline Large Language Model in improving household residents' understanding of appliance operational costs, leading to a measurable increase in energy cost awareness and a reduction in unnecessary electricity expenses.
Department:
Department of Electrical and Computer Engineering
Faculty Mentor:
Tim Hansen
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Anaerobic Digestion of Food Waste with Microplastics: Effects on VFA production

Jeremy Ross
Recently, microplastics have become a prominent concern globally due to their widespread presence in everyday products. They are found not only in plastic products but also in less obvious sources such as food. Additionally, microplastics are linked to potential negative health effects, including cardiovascular disease and cancer. Common plastics such as PET and PVC can enter the human body through ingestion or inhalation and disrupt the gut microbiome; the DeLong lab focuses on using microbes to turn waste into valuable products and primarily uses VFAs (Volatile Fatty Acids) to do so. This project aims to further our understanding of how these microplastics impact a microbe's ability to biosynthesize VFAs.
Department:
Department of Civil & Environmental Engineering
Faculty Mentor:
Susan De Long
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Applying RainNet on AQPI Composite Data for Short-Term Precipitation Nowcasting

Andrea Sanchez Chacon
Machine learning models are used for interpreting weather radar data due to their ability to learn from and apply large data inputs. RainNet is a deep convolutional neural network designed for radar-based precipitation nowcasting. The goal is to prepare the AQPI composite data to fit the model's input format, train the model using this data, and then analyze the results to assess model's performance. Throughout the process, findings, challenges, and potential areas for improvement will be documented to evaluate the feasibility of using RainNet for AQPI composite nowcasting.
Department:
Department of Electrical and Computer Engineering
Faculty Mentor:
Chandrasekar Venkatachalam
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Comparative Analysis of Cyanobacterial Strains to Enhance Soil Water Retention

Kathy Trenado
Water scarcity is a growing challenge for agriculture, where declining soil quality and increasing drought frequency limit crop productivity. Cyanobacteria offer a promising biological solution because they naturally produce extracellular polymeric substances (EPS) that can stabilize soil, retain moisture, and improve nutrient availability. Understanding how different cyanobacterial strains grow and produce EPS is essential for developing sustainable soil‑enhancement strategies. This project investigates three cyanobacterial strains to determine how their biological traits influence soil water‑retention performance. The research combines microbial culture, growth‑curve analysis, EPS quantification, and soil microcosm testing to link laboratory measurements to real soil outcomes. By characterizing strain‑specific differences, the project aims to identify candidates that could reduce irrigation needs, improve soil resilience during drought, and support long‑term agricultural sustainability.
Department:
School of Biomedical and Chemical Engineering
Faculty Mentor:
Christie Peebles
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Computational Modeling of Microbial Interactions in Anaerobic Systems

Sophia Ochoa
Microbial interactions in anaerobic systems play a critical role in both the human gut and waste treatment processes, where they break down organic matter into short-chain fatty acids (SCFAs). Understanding these interactions is essential for improving human health and the efficiency of wastewater treatment. Computational modelling, implemented using Python and metabolic modelling frameworks, provides a powerful way to simulate microbial metabolism and predict system outputs. In this project, the focus is on modelling the microbe Lactobacillus amylovorus to evaluate its behaviour and SCFA production in anaerobic systems.
Department:
Department of Civil & Environmental Engineering,
School of Biomedical and Chemical Engineering
Faculty Mentor:
Joshua Chan
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Creating nano-scale structures to improve laser absorption for micro-scale nuclear fusion.

Cristian Rascon-Perez
Research in inertial fusion energy is important because it offers a potential pathway toward a clean and virtually unlimited energy source while also advancing high‑power laser technology and our understanding of plasma physics. With Jorge Rocca’s team we engineer nano-scale structures through chemical growth called nanowires. These nanowires absorb high‑intensity laser light far more efficiently than flat targets, enabling them to reach extreme temperatures comparable to those found in solar plasma. After fabrication, their structure and quality are characterized using a Scanning Electron Microscope, allowing us to prepare precise targets for fusion‑relevant laser experiments.
Department:
Department of Electrical and Computer Engineering
Faculty Mentor:
Jorge Rocca
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Cybersecurity in Heavy Vehicle and Transportation Systems

Jason Patterson
Cybersecurity in transportation vehicles such as semi trucks is often overlooked. The technology embedded in them is deeply embedded with the rest of the vehicle’s systems, which means it can be a major security risk if not properly guarded. Due to the dependence on transport vehicles, a security risk in this area can have a large impact. The research being conducted in this specific project primarily consists of replicating heavy vehicle cyber attacks, and analyzing/understanding data retrieved from said attacks to assist with prevention.
Department:
Department of Systems Engineering
Faculty Mentor:
Jeremy Daily
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Decellularizing Meniscus Tissue Through Differential Centrifugation

Cadi Chavez
The meniscus is a cushion in the knee joint that can commonly tear and cause injuries, leading to osteoarthritis. Decellularized meniscus tissue shows promise as an improved biomaterial for a partial meniscus scaffold. However, the meniscus is difficult to decellularize due to its high collagen content that causes the tissue to be tough and stringy. Traditionally, harsh detergents, such as SDS or Triton X-100, are used for decellularization, but can remove important extracellular matrix (ECM) constituents such as glycosaminoglycans (GAGs). Differential centrifugation is a process that aims to separate the ECM from the cells based on the density but has shown a low yield of ECM in practice. The goal of this project is to investigate parameters involved in differential centrifugation, including homogenizing, centrifugation speed, and centrifugation time, in order to minimize DNA content and maximize ECM content. It is hypothesized that by optimizing centrifugation time and speed, differential centrifugation can adequately decellularize meniscus tissue while maintaining more GAGs than the traditional chemical decellularization process.
Department:
School of Biomedical and Chemical Engineering
Faculty Mentor:
Kevin Labus
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Deployable Adaptive Wheel

Ti Mo
In cases of rescue, search, and exploration work robots often struggle with mobility through terrains. This research is aimed to address those issues, by developing a deployable variable wheel, this wheel will allow for the robot to move both in flat and rough surfaces. This wheel is able to expand to adapt to smooth surfaces and compress to climb over obstacles. The goal of this project is to test the effectivness of this adaptive wheel.
Department:
Department of Mechanical Engineering
Faculty Mentor:
Jianguo Zhao
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Design and Implementation of an Ultrashort Pulse Stretcher for a Petawatt-Class Laser

Wyatt Saunders
Nearly all petawatt-class lasers use chirped pulse amplification (CPA) to increase their peak power and prevent optical damage. This process involves an ultrashort laser pulse being elongated in time, amplified, and then compressed. The goal of this project is to design and implement an optical assembly which 'stretches' the laser pulse. This includes rigorous optical simulation and design of the assembly, mechanical design of components in CAD software, optical and mechanical tolerance and stability simulation, CNC manufacturing, and physical testing and data collection on assembled components.
Department:
Department of Electrical and Computer Engineering
Faculty Mentor:
Jorge Rocca
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Design and Prototyping of a Perching Mechanism for Flying Robots

Jaden Spatafora
Flying robots are used for surveillance in wildlife environments, where the ability to perch on different surfaces is important for stable data collection and longer operation time. Current designs allow robots to perch on flat surfaces and branches, but they are limited when interacting with rough terrain. This project focuses on developing a new perching mechanism that allows the robot to flip 180 degrees from its default position and adhere to rough surfaces using rapid, controlled heating and cooling of an adhesive. This would increase the range of surfaces the robot can land on and remain secure, improving its ability to capture data such as video. The objective of this work is to assist in the mechanical design and testing of the new perching system to evaluate its performance on rough terrain.
Department:
Department of Mechanical Engineering
Faculty Mentor:
Jianguo Zhao
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Designing Sustainable Futures: Ethics & Innovation Across the Engineering Curriculum

Eleanna Gonzalez
Ensuring the future of responsible and mindful engineers means helping bridge the gap between the technical “how” and the ethical “why” of engineering. It is about making sure that what we build does not harm the world but instead contributes positively to society. As an intern, I have the opportunity to help connect social responsibility with technical innovation. My role involves designing teaching activities that encourage future engineers to recognize the impact their work can have on people, communities, and the environment. A major focus of my work has been exploring ethical dilemmas and case studies to highlight real-world challenges that often push against traditional engineering norms. To support this, I have been developing hands-on activities, lesson plans, worksheets, and projects that help students build an entrepreneurial mindset while also thinking about ethics, health, and sustainability. Overall, my goal is to help bring these concepts into the classroom in ways that are engaging and meaningful. As a student myself, I can offer a perspective on what students are likely to connect with and benefit from during class. I am also contributing to the development of a sustainable engineering curriculum that encourages current and future engineering students at Colorado State University to become not only strong engineers, but also globally conscious problem-solvers.
Department:
Department of Civil & Environmental Engineering
Faculty Mentor:
Pinar Omur-Ozbek
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Detecting Precipitation with Machine Learning

Pascual Carrasco
Weather radar plays a critical role in detecting and monitoring precipitation, but interpreting radar data accurately can be challenging without the proper tools. This project explores the use of machine learning to improve rainfall detection using NEXRAD Level II radar data. Using the Py-ART library, radar scans were processed to extract key features such as reflectivity and correlation coefficient (RhoHV) across multiple elevation sweeps. These features were used to train a Random Forest Classifier to classify radar scans as either rain or no rain. The objective of this research is to develop a reliable and scalable model that can identify rainfall patterns effectively. By incorporating multiple radar sweeps and physically meaningful features, this project demonstrates how machine learning can enhance precipitation detection and serve as a foundation for more advanced weather analysis systems.
Department:
Department of Electrical and Computer Engineering
Faculty Mentor:
Chandrasekar Venkatachalam
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Determining Surface Winds from Hurricane Hunter Aircraft Flight Level Winds

Yelene Barrios
Hurricane forecasters need to know surface winds to forecast hurricane intensity and hazards. However, in many cases the most reliable measurements come from aircraft reconnaissance (Hurricane Hunters) that cannot fly too close to the surface for safety reasons. This project works on developing an artificial intelligence (AI) model to predict surface winds from the flight level winds. The AI model uses hurricane dropsonde data that measures flight level and surface wind as a truth dataset for training.
Department:
CIRA
Faculty Mentor:
Galina Chirokova
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Developing a Dehydration Stress Regime to Assess Robustness of Engineered Cyanobacteria

Izobel Machacek
Cyanobacteria are photosynthetic bacteria similar to algae commonly found in aquatic environments that can produce their own energy and many valuable commercial products through photosynthesis. Although individual cells are microscopic, they often grow in visible colonies often through the substances they secrete into their surroundings, known as their secretome. Cell adhesion, or “stickiness,” a byproduct of the secretome affects how well cells attach to each other and surrounding surfaces and often provide protection to environmental stress. In this study, cyanobacteria are exposed to varying dehydration conditions to develop a reproducible stress assessment for secretome engineered cyanobacteria. We hypothesize that genetic changes influencing the secreted environment of the cells will increase stress tolerance.
Department:
School of Biomedical and Chemical Engineering
Faculty Mentor:
Christie Peebles
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Developing live-cell imaging methods to distinguish homoplasmic and heteroplasmic mitochondrial dysfunction using autofluorescence signals

Monique Fourie
The research conducted on mitochondrial cells is conducted due to the observation that patients with mitochondrial diseases can present very similar genetic profiles yet show markedly different clinical symptoms and treatment responses. This suggests that mitochondrial dysfunction is not always uniform within cells and that traditional methods like DNA sequencing may fail to capture functional differences such as heteroplasmy. Hence, the question we are asking is whether live-cell fluorescence imaging of mitochondrial autofluorescence (NADH and FAD), combined with data analysis, can be used to distinguish between homoplasmic (same) and heteroplasmic (mixed) mitochondrial dysfunction. The research that is being conducted includes taking mitochondrial cells and using different cell products, such as changing media, passaging, and adding different substrates, to see how the fluctuation in NADH and FAD changes. By measuring baseline redox states and tracking dynamic responses to metabolic perturbations, it quantifies how mitochondria behave within and across cells. The aim is to determine whether these functional patterns can distinguish between homoplasmic and heteroplasmic mitochondrial dysfunction. This led us to hypothesize that cells with heteroplasmic mitochondrial dysfunction will exhibit greater variability in NADH and FAD autofluorescence signals and dynamic responses to metabolic perturbations compared to cells with homoplasmic dysfunction, which will display more uniform mitochondrial behavior.
Department:
School of Biomedical and Chemical Engineering
Faculty Mentor:
Jesse Wilson
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Effects of Filtration and Water Softening on Electrochemical Nitrate Reduction

Angel Tinoco
Nitrate contamination in wastewater is an increasing environmental and public health concern, contributing to issues such as unsafe drinking water. Electrochemical reduction offers a promising approach to convert nitrates into ammonia, which is a versatile compound used in many industries such as agriculture. But its efficiency can be limited by water hardness, byproducts and suspended solids. This research explores how filtration and chemical water softening can enhance the performance of electrochemical nitrate reduction systems. Synthetic wastewater is used to evaluate solids removal efficiency and to test different chemical softening methods. The goal of this study is to investigate the role of filtration and water softening in electrochemical nitrate reduction systems and to generate data that can inform further research and system evaluation.
Department:
Department of Mechanical Engineering
Faculty Mentor:
Reza Nazemi
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Electron Uptake in Microbial Cultures Under Anaerobic Conditions

Leah Kebede
Microbial fermentation is used to convert organic substrates such as glucose into valuable products to improve efficiently in applications such as biofuel production, wastewater treatment, and sustainable chemicals. This study introduces Electro-fermentation, which is a promising approach to influence microbial metabolism by applying an external electrical potential. Under these conditions, we can shift the microbial cultures' metabolic pathways toward more desirable products by electrical input rather than using it as a direct energy source. The objective of this study is to investigate how a microbial culture responds under anaerobic conditions in a cathodic bio-electrochemical system. This study examines how nutrient-fed microbes interact with the electrode and uptake electrons, and how these interactions influence metabolic pathways to enhance the production of desired products.
Department:
School of Biomedical and Chemical Engineering
Faculty Mentor:
Kenneth Reardon
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Evaluating PFAS Redistribution in Porous Media Using Gas Sparging

Phoebe Jones
Per- and polyfluoroalkyl substances (PFAS) are persistent contaminants that can enter groundwater through sources such as aqueous film-forming foam (AFFF), posing long-term risks to environmental and human health. Because of their stability and mobility, PFAS are difficult and often expensive to remove using conventional treatment methods, making the development of more effective remediation strategies important. Gas sparging has been explored as a potential in-situ approach, as it may influence PFAS transport and behavior through interactions at air–water interfaces. This research is part of SERDP Project ER22-3221 and focuses on column experiments to better understand how gas sparging affects PFAS distribution in porous media. A component of this study investigates how different gas conditions (control, air, and nitrogen) influence the transformation of PFAS precursors into more persistent compounds such as perfluorooctanoic acid (PFOA), with the goal of evaluating how these processes may impact groundwater remediation efforts.
Department:
Department of Civil & Environmental Engineering
Faculty Mentor:
Mitch Olson
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Flame Propagation Characteristics of Decomposed Ammonia in a Rapid Compression Machine for Carbon-Free Emissions

Nicholas Juba
The negative impact of carbon-based fuels on climate change has created a need for a carbon-neutral alternative. Ammonia is a promising alternative, as it is a dense hydrogen carrier, has a high volumetric energy density, and an existing transportation sector; however, pure ammonia exhibits long ignition delay times and slow flame speeds, hindering its application in engines. Partial decomposition of ammonia into hydrogen and nitrogen significantly improves its combustion characteristics, increasing flame speeds and decreasing ignition delay times, making it a more practical fuel. Current data on ammonia flame speeds focuses on atmospheric conditions, low pressure high temperature, or high pressure low temperature conditions, however there is no data at engine-relevant conditions (700-900 K and 20-30 bar). These data sets are needed for the development of chemical kinetic mechanisms crucial for the design of next-generation engines. This research investigates the first-of-their-kind flame propagation characteristics of partially decomposed ammonia under such conditions and examines the accuracy of current ammonia chemical kinetic mechanisms for future engine design.
Department:
Department of Mechanical Engineering
Faculty Mentor:
Bret Windom
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Flash Sintering of High Entropy Ceramics

Elizabeth Smith
High entropy, non-oxide, ceramics are generally composed of metal carbides, nitrides, borides, or silicide compounds of multiple metallic elements (4-5 or more). These ceramics are hard and unmalleable. Previous research from our lab has focused on High entropy nitride ceramics synthesized by a new process known as reactive flash sintering, in which multiple materials come together to create a new solid material from a reaction caused by electrical current. It is a very fast process, taking around 4 minutes maximum. Because flash sintering is new, everything that is flash sintered is a new discovery, which is why the properties need to be studied. I will be using this process to explore the properties of high entropy carbide and boride ceramics.
Department:
Department of Mechanical Engineering
Faculty Mentor:
Zhe Cheng
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Flash Sintering to Prepare a Solid Oxide Fuel Cell Bilayers

Ilianna Shelton
Solid Oxide Fuel Cells (SOFCs) are a ceramic-based electrochemical device that converts fuel, like hydrogen or natural gas, directly into electricity at high temperatures. It works by using a solid ceramic electrolyte to transport oxygen ions from cathode to anode, where they react with the given fuel (like hydrogen) and release electrons, creating electricity. It is a low-emission, fuel-flexible, and highly efficient energy source, but uses high operating temperatures, which can be energy intensive, and is not always the most durable. The performance of solid oxide fuel cells depends strongly on bilayer structures, which help with the ion transfer and electrochemical reactions. But, traditional methods for this process use extremely high temperatures and take a very long time. This reduces the efficiency and makes it difficult to scale it into larger projects. Step-Wise Current Reactive Flash Sintering is a new technique that densifies ceramic powders in seconds, instead of hours, by applying a controlled and increasing electrical current at lower temperatures. This offers a solution for a faster, more energy-efficient process. This project focuses on applying flash sintering to prepare solid oxide fuel cell bilayers, with the goal of developing a controlled system for producing materials for electrochemical energy applications.
Department:
Department of Mechanical Engineering
Faculty Mentor:
Zhe Cheng
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Flash Sintering to Prepare a Solid Oxide Fuel Cell Bilayers

Calian Hatland
Solid Oxide Fuel Cells (SOFCs) are a ceramic-based electrochemical device that converts fuel, like hydrogen or natural gas, directly into electricity at high temperatures. It works by using a solid ceramic electrolyte to transport oxygen ions from cathode to anode, where they react with the given fuel (like hydrogen) and release electrons, creating electricity. It is a low-emission, fuel-flexible, and highly efficient energy source. However, it has high operating temperatures, which can be energy-intensive, and it is not always the most durable. The performance of solid oxide fuel cells depends strongly on bilayer structures, which help with the ion transfer and electrochemical reactions. But traditional methods for this process use extremely high temperatures and take a very long time. This reduces efficiency and makes it difficult to scale it into larger projects. Stepwise Current Reactive Flash Sintering is a new technique that densifies ceramic powders in a few minutes, instead of hours, by applying a controlled and increasing electrical current at lower temperatures. This offers a solution for a faster, more energy-efficient process. This project focuses on applying flash sintering to prepare solid oxide fuel cell bilayers to develop a controlled system for producing materials for electrochemical energy applications.
Department:
Department of Mechanical Engineering
Faculty Mentor:
Zhe Cheng
SURE presentor photo

Gathering Driver's Visual Data Using Eye-Tracking Technology

Norberto Hinojosa
Eye-gaze behavior can be very important for predicting human actions and for helping identify safety-critical outcomes. Driving is one activity that stands out from other human behaviors because you're not just in charge of your own safety, but also the safety of others. Therefore, having a reliable and measurable way to detect where drivers are looking is important in researching possible solutions to improve road safety. The objective of this research is to document how to effectively use the Dikabliss Eye Trackers to monitor driver eye behavior.
Department:
Department of Systems Engineering
Faculty Mentor:
Erika Gallegos
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Gathering HRIT Data from a Custom-Made Satellite For An Analysis of Earth's Atmosphere

Jairo Moreno
This project involved building a new satellite data receiver for gathering HRIT data from NOAA's GOES satellites. NOAA (National Oceanic and Atmospheric Administration) is a scientific agency that predicts changes in the weather, climate, oceans, etc., while managing marine ecosystems and resources. The GOES (Geostationary Operational Environmental Satellite) satellites are operated by NOAA but managed by NASA, which tracks imagery and data of the Earth's atmospheric conditions. HRIT (High-Rate Information Transmission) is data that is translated from the GOES satellites to display Earth imagery and environmental data. This data will be used to replicate the data acquisition and processing conditions for remote locations where high-bandwidth data acquisition is not possible. The system will translate the HRIT data into scientific imagery of meteorological significance. We will receive HRIT by a custom-made satellite data receiver with an SDR, a satellite dish, a Band Pass Filter, and its custom mount. An SDR (Software-Defined Radio) is a communication system that satellites utilize to manage radio frequencies and functions to provide efficiency with the objective of the satellite. A Band Pass Filter (BPF) is a device that regulates which radio frequencies can be allowed while rejecting frequencies outside of the desired range. I have researched appropriate hardware and produced a spreadsheet with recommendations and prices for use in decision-making. I also have taken measurements at the installation location, then used those measurements to produce a custom, 3D-printed mount for the satellite dish. I will produce and design my custom mount in Autodesk Fusion to build a stable mount that will hold the weight of the satellite receiver while simultaneously stabilizing it. The custom-made satellite data receiver can be utilized by fellow researchers in the atmospheric department for future projects, and provide students in the future with a glimpse into HRIT data.
Department:
Department of Electrical and Computer Engineering
Faculty Mentor:
Jeremy Solbrig
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Global Infrastructure and Road map Analysis with Python and Satellite Imagery

Silver Velasquez Delao
Road analysis conducted manually is a time-consuming task that can be misleading and delay road repairs on a global scale. When analyzing road conditions, inspectors must follow a standardized scale that determines the quality and durability of the road being evaluated. This scale is called the Pavement Surface Condition Index (PSCI), which uses a 10-point system. A rating of 10 indicates that a road is in perfect condition with no visible defects, while a rating of 1 represents extensive structural distress, including deep potholes and severe deterioration. However, inspections performed by individuals can be misleading due to the subjective nature of human judgment. Our research with Dr. Jana focuses on developing a model to improve this process using AI and Python. By collecting data from Google Street View, we can analyze road conditions more efficiently. Using code to gather images from streets—such as College Avenue in our college town—we can compile large datasets for evaluation. These images can then be assessed by AI systems like ChatGPT or Gemini. The goal of this research is to create a scalable and efficient method for global road analysis. This approach could significantly reduce delays in identifying and addressing critical repairs. Additionally, it would improve infrastructure management by allowing cities to prioritize repairs more effectively and reduce the time required for road condition assessments from months to a fraction of that time.
Department:
Department of Electrical and Computer Engineering
Faculty Mentor:
Debasish Jana
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How do Beaver Dam Analogs Affect the Groundwater Hydrology of Post-Fire Ecosystems

Sarun Hoefer
Beavers are a keystone species due to their ability to build dams that mitigate water flow in rivers and increase the biodiversity and hydrology in ecosystems. Their natural dam-building elevates the surrounding water table and expands habitat complexity. BDAs and PALS are human-made structures that replicate the function of natural beaver dams by modulating stream flow and inducing floodplain saturation. They are preferred over cement dams to keep restored ecosystems closer to a natural state and minimize ongoing human intrusion. Our study area is on the South Fork of the Cache La Poudre River, an area severely impacted by the 2020 Cameron Peak Fire. Because the fire stripped away critical vegetation, restoration teams have installed Beaver Dam Analogs (BDAs) and post-assisted log structures(PALS) at the site of the fall of 2024. We are investigating whether the site demonstrates improved subsurface hydraulic storage, which would support re-saturating the floodplains and accelerate the growth of ecological recovery. The goal of our research is to analyze multiple geological datasets(groundwater level, streamflow, stream water stage, precipitation, etc) from Site South Fork to evaluate how BDAs and PALS are affecting local groundwater hydrology. By comparing these various data sets, we aim to analyze how BDAs affect groundwater storage. In demonstrating that BDAs effectively provide ecosystem services as a vital strategy for post-fire ecosystems, not just for South Fork, but for locations beyond ours.
Department:
Department of Civil & Environmental Engineering
Faculty Mentor:
Antonio Alves Meira Neto
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How Fluid Interfaces Affect Sperm Motion

Antonia Perez Martinez
Understanding sperm activity in real-world environments is important to inspire innovation in diagnosis and fertility technologies. We aim to replicate changes in the female reproductive tract to understand the fluid interfaces in the transition between the nonviscous medium of the vaginal track to the viscous medium in the cervix. This research helps us to understand how sperm successfully navigate the reproductive system and fertilization process. One of the main goals of the project is to use a model to track sperm as they cross these boundaries and to measure the contrast in sperm activity between the two areas. Furthermore, we want to compare these results across species to understand if our findings are consistent.
Department:
Department of Electrical and Computer Engineering
Faculty Mentor:
Diego Krapf
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How Fluid Interfaces Affect Sperm Motion

Hiba Aryan
Understanding sperm activity in real-world environments is important to inspire innovation in diagnosis and fertility technologies. We aim to replicate changes in the female reproductive tract to understand the fluid interfaces in the transition between the nonviscous medium of the vaginal track to the viscous medium in the cervix. This research helps us to understand how sperm successfully navigate the reproductive system and the fertilization process. One of the main goals of the project is to use a model to track sperm as they cross these boundaries and to measure the contrast in sperm activity between the two areas. Furthermore, we want to compare these results across species to understand if our findings are consistent.
Department:
Department of Electrical and Computer Engineering
Faculty Mentor:
Diego Krapf
SURE presentor photo

How Fluid Interfaces Affect Sperm Motion

Oliver Holmes
Understanding sperm activity in real-world environments is important to inspire innovation in diagnosis and fertility technologies. We aim to replicate changes in the female reproductive tract to understand the fluid interfaces in the transition between the nonviscous medium of the vaginal track to the viscous medium in the cervix. This research helps us to understand how sperm successfully navigate the reproductive system and fertilization process. One of the main goals of the project is to use a model to track sperm as they cross these boundaries and to measure the contrast in sperm activity between the two areas. Furthermore, we want to compare these results across species to understand if our findings are consistent.
Department:
Department of Electrical and Computer Engineering
Faculty Mentor:
Diego Krapf
SURE presentor photo

How Microbial Communities from Anaerobic Digesters Make Short-Chain Fatty Acids

Kylin Boland
Anaerobic digestion is a complex, crucial process driven by microorganisms that break down organic materials in the absence of oxygen. Understanding how these microbial communities operate and interact is vital to their use in waste treatment, human gut health, and other industrial processes. This project aims to isolate and study these microbial communities from various anaerobic digesters. Notably, it focuses on which microbial community produces short-fatty acid chains. Ultimately, this project seeks to improve the efficiency and value of anaerobic microbial conversion.
Department:
School of Biomedical and Chemical Engineering
Faculty Mentor:
Joshua Chan
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Hydrogen Fuel Cell Compressor Control Cabinet

Alexander Raimond
This project focuses on the possibility of using hydrogen fuel cells to store excess electricity created by renewable energy sources. During peak sunlight solar panels tend to produce more electricity than the grid needs from these electricity sources. this electricity is unused and wasted during these times. Using hydrogen fuel cells, we can store large quantities of electricity during peak production hours, and can then be used when these green energy sources are not producing power. The equipment we have for our research was donated, and is missing documentation. I have been tasked with documenting the S700 power control panel, that controls the condenser, and links it to the rest of the system.
Department:
Department of Electrical and Computer Engineering
Faculty Mentor:
Bret Windom
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Imaging System for Defects in Optical Lenses Used for Laser Fusion

Brianne Foley
Optical lenses can contain hundreds to thousands of defects trapped in their many layers. These particles can be as simple as dust falling onto the lenses as they are being processed, or air bubbles being trapped under the layers. When lasers pass through the lenses, these defects can absorb the heat from the laser, which damages the lenses and defeats the purpose of the lens, which is to manipulate the laser. My task has been to create a prototype of a system that we can use to detect defects in optical lenses. This will assist us in minimizing the number of particles in an optical lens, and increase the longevity of these optics. To complete this, I have been using a high-resolution camera, a laser, mirrors, computer programs, and samples that contain defects. To aid in my goal, I have done research on lasers, optical components, cameras, and sheets of light. My finished prototype will allow us to be able to take and analyze images of defects of very small sizes, allowing us to get an accurate count and location of all particulates in the optical lens.
Department:
Department of Electrical and Computer Engineering
Faculty Mentor:
Carmen Menoni
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Impacts of Snow Plowing on Snow Pack Depth, Density, and Water Content

Yanisa Wongveerakul
Whenever it snows in Colorado or any place there is snow, this snow gets plowed to clear streets and sidewalks and increase traffic flow. However, the plowed snow takes longer to melt and has different properties than the surrounding snow. We are using a cosmic ray neutron (CRN) rover to measure the snowpack. Since this sensor is driven along highways, to calibrate it, we need to consider the characteristics of the plowed snow on the side of the road. We measured how the plowed snow is affecting the snow pack near the road. We have been focusing on the area near Cameron pass in northern Colorado. Since there is so little snow this winter season, we are comparing this data to data collected last winter, and data from the Joe Wright Snowpack Telemetry (SNOTEL) station. Plowing has led to higher densities and more water content along the roads. This needs to be considered when evaluating snowpack estimates from the CRN rover. These adjustments should help make the CRN rover a valuable tool to measure the snowpack more often. The information gathered in this project will help us better measure the snowpack. With a changing climate, more extreme weather, and a more uncertain snowpack, new tools will help measure a diminishing snowpack. This is important in dry environments like Colorado, with vast areas prone to wildfires.
Department:
Department of Civil & Environmental Engineering
Faculty Mentor:
Jeffrey Niemann
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Increasing Resilience and Reliability of the Modern Power Grid

Sebastian Fiedor
As the power demand increases, there is more stress put onto the power grid. With the increased stress, the power grid has to be running at full strength at all times, so high impact low probability events and cyber attacks that can cause blackouts have to be minimized. Because of this dependency, engineers and linemen are modernizing the current power grid.
Department:
Department of Electrical and Computer Engineering
Faculty Mentor:
Zongjie Wang
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Large Language Model-Powered Decision Support for Water and Energy Operations

Maddox Mulholland
In today’s utility operations, it is not uncommon for operators to face a sudden rush of alerts, sometimes ten or more within just a few minutes. This overwhelming amount of information can make it difficult to respond quickly and accurately. Through this project, we are working to create an AI assistant that can take all of this data and turn it into clear, prioritized instructions that help operators stay focused and make the right decisions. My main responsibility has been to design different ways for the AI to explain its recommendations and to build a web-based environment where we can test how well these explanations work. By tracking how quickly operators respond and how much they trust the AI, I am helping to find out if these new summaries can give operators better guidance than traditional manuals. My goal is to make sure that, even in a crisis, operators have the support they need to keep the public safe.
Department:
Department of Electrical and Computer Engineering
Faculty Mentor:
Steve Conrad
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Machine Vision Integration for AIRLIFT System

Isabella Vigil
The goal of this research is to develop a camera system that can read QR codes and capture images of air filters within the AIRLIFT system. This enhancement will allow filters to be automatically identified and categorized without manual handling. By integrating camera based QR code recognition, the AIRLIFT system can maintain its labor-saving benefits while enabling efficient and accurate filter classification.
Department:
Department of Mechanical Engineering
Faculty Mentor:
Christian L'Orange
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Measuring cardiovascular pulse waveforms using minimally invasive sensors

Aneesa Khan
The most common blood pressure monitoring methods are via an arm cuff or a catheter inserted in the wrist artery. While these methods are highly effective for reading blood pressure, they are limited in several ways. Machines for personal health monitoring can be costly, are often limited to at-home use, and use invasive techniques to gather cardiovascular information. We are researching into developing a wearable device that passively tracks blood pressure without having to squeeze the arm or wrist. Tracking blood pressure can be important for individuals with hypertension (high blood pressure), as this can cause serious issues like strokes or heart attacks. Various methods for measuring blood pressure include combining a piezoresistive pressure sensor with a PPG sensor. This would allow for the pressure sensor to detect minuscule changes in volume around an artery, and the PPG sensor to use light reflections to validate this data. Another form is pulse-transit time, which involves two piezoresistive pressure sensors spaced along the wrist artery. This method tracks the time it takes for a pulse to travel through the artery, giving an estimate of blood pressure. Our research will be centered around these ideas, and exploring how combining different sensors can give an accurate reading of blood pressure, and how it can be incorporated into a medical wearable device.
Department:
Department of Mechanical Engineering
Faculty Mentor:
Zhe Cheng
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Measuring cardiovascular pulse waveforms using minimally invasive sensors

Francisco Morales
This project will allow for a low-cost alternative to finding a person's blood pressure. The device operates using noninvasive sensors that are designed to get systolic and diastolic data. With it being a wearable sensor, it would allow a person to get their blood pressure whenever they want. This does come with its own set or rule which include being in a seated position and making sure you have not done any sort of exercise withing 5 minutes of trying to get your blood pressure since it will cause inaccurate readings.
Department:
Department of Electrical and Computer Engineering,
Department of Mechanical Engineering
Faculty Mentor:
Zhe Cheng
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Microbial Communities and the formation of Short-Chain Fatty Acids

Arely Mendiola Morales
Anaerobic digestion is a process that involves microorganisms breaking down organic materials to produce short-chain fatty acids and useful products such as butyrate. The study of microbial communities in the production of short-chain fatty acids is vital to their use in waste treatment and management. This project aims to use anaerobic digesters to successfully and efficiently separate isolate fatty-chain acids.
Department:
School of Biomedical and Chemical Engineering
Faculty Mentor:
Joshua Chan
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Name That Alert: Alert Display Systems(HMI)

Carlos Rivera Marquez
As engineering systems become more complex, improving how humans interact with AI is increasingly important for efficiency, safety, and decision-making. We focused on the idea of humans working together with AI in responding to different alerts in a water treatment plant (WTP) context. WTP alerts are displayed in a Human-Machine Interface (HMI). Alerts displayed on HMIs facilitate personnel responses to potential actions. However, alerts can be unclear or inefficient, causing slower response times or misinterpretation. To improve alert systems, we tasked AI with generating explanations for different alerts to reduce misfire rates, improve time to action, and enhance understanding of these alarms. We explored different options that improve how the importance of alarms is presented, assisting with reaction time and implementation of the AI explanation system. Our goal is to evaluate how AI-assisted explanations and improved HMI design can enhance user understanding, trust, and response efficiency in a WTP environment.
Department:
Department of Electrical and Computer Engineering,
Department of Mechanical Engineering,
Department of Systems Engineering
Faculty Mentor:
Steve Conrad
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Name That Alert: How can AI explain alarms in a better way

Ernesto Maldonado
We are working on implementing artificial intelligence (AI) in alarms in a water treatment system for better explanations. Alarm systems have been around for many years allowing workers to know what is wrong and where, but, there's a lot of room where the alarm outputs are wrong. Whether that's from a malfunction with the sensor, communication error, or a misreading. With alarms being one of the most important elements of any place of work dealing with machinery and things being worked on, it is important for these alarms to be of high accuracy and easily understandable, this is the reason why this research is important. One of the ways we can implement AI into alarm systems is by working on ways to get inputs and figuring the best output to give. In particular, I worked on a decision tree explaining problems when the pH water was low and its effects.
Department:
Department of Electrical and Computer Engineering,
Department of Mechanical Engineering,
Department of Systems Engineering
Faculty Mentor:
Steve Conrad
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Novel Fabrication Methods of Homogeneous PVA Hydrogels

Ahmad Jalal
The TMJ (Temporomandibular) joint, located in the jaw, wears down for millions of people in the US. This leads to long-term consequences on jaw health, including Arthritis and other TMJ disorders. A treatment that can be done to combat this is prosthetic replacements. Hydrogels have shown promise in cartilage repair/replacement procedures, with previously established works demonstrating that polyvinyl alcohol (PVA) can be used to manufacture such prosthetics. The process of developing PVA Hydrogels can be difficult and inconsistent, requiring extensive testing and iteration. Creating a unique system to produce a consistent hydrogel result for the initial processing of the implant is crucial to effectively construct a successful implant. The difficulty of manufacturing the polymers is achieving one of the most important parameters of surface smoothness. This parameter is crucial to test the hydrogels and tune their coefficient of friction, and to produce flat sheets for our novel wear tester. This then leads to further obstacles in the production of planar curling, inconsistent drying, and waste material at the edges, providing extra stiffness. This project aimed to address the fabrication issues through specialized mold preparation, desiccation, and varying drying and annealing conditions to improve hydrogel homogeneity. Once the production of PVA hydrogel polymers can be consistent and tuned for appropriate wear, it can be used to reduce the friction between the cartilage and the hydrogel in the jaw and later iterated further upon for implant design.
Department:
Department of Mechanical Engineering,
School of Biomedical and Chemical Engineering
Faculty Mentor:
Kevin Labus
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Optimizing Hydrogen Peroxide Generation in a Flow-Through Electrochemical Reactor

Angel Acevedo
Hydrogen Peroxide (H₂O₂) is widely used in water treatment because it can break down harmful organic contaminants. However, traditional production methods are energy-intensive and rely on centralized facilities, making them less sustainable. Electrochemical systems offer a more sustainable approach by producing hydrogen peroxide directly within the treatment. This reduced the need for transportation and allows for more efficient, on-demand use. Flow through reactors further improves this process by enabling continuous operation and better mixing. This research focuses on improving hydrogen peroxide production in a flow-through electrochemical reactor. Key factors such as voltage, signal amplitude, flow configuration, and reactor design are studied to understand how they affect efficiency. The goal is to develop a more effective and sustainable method for water treatment.
Department:
Department of Civil & Environmental Engineering
Faculty Mentor:
Yanghua Duan
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Optimizing Hydrogen Peroxide Generation in a Flow-Through Electrochemical Reactor

Benjamin Gonzales
Hydrogen Peroxide (H₂O₂) is widely used in water treatment because it can break down harmful organic contaminants. However, traditional production methods are energy-intensive and rely on centralized facilities, making them less sustainable. Electrochemical systems offer a more sustainable approach by producing hydrogen peroxide directly within the treatment. This reduced the need for transportation and allows for more efficient, on-demand use. Flow through reactors further improves this process by enabling continuous operation and better mixing. This research focuses on improving hydrogen peroxide production in a flow-through electrochemical reactor. Key factors such as voltage, signal amplitude, flow configuration, and reactor design are studied to understand how they affect efficiency. The goal is to develop a more effective and sustainable method for water treatment.
Department:
Department of Mechanical Engineering
Faculty Mentor:
Yanghua Duan
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Optimizing Hydrogen Peroxide Generation in a Flow-Through Electrochemical Reactor

Jamoni Lawler
Hydrogen Peroxide (H₂O₂) is widely used in water treatment because it can break down harmful organic contaminants. However, traditional production methods are energy-intensive and rely on centralized facilities, making them less sustainable. Electrochemical systems offer a more sustainable approach by producing hydrogen peroxide directly within the treatment. This reduced the need for transportation and allows for more efficient, on-demand use. Flow through reactors further improves this process by enabling continuous operation and better mixing. This research focuses on improving hydrogen peroxide production in a flow-through electrochemical reactor. Key factors such as voltage, signal amplitude, flow configuration, and reactor design are studied to understand how they affect efficiency. The goal is to develop a more effective and sustainable method for water treatment.
Department:
Department of Mechanical Engineering,
School of Biomedical and Chemical Engineering
Faculty Mentor:
Yanghua Duan
SURE presentor photo

Optimizing Hydrogen Peroxide Generation in a Flow-Through Electrochemical Reactor

Joseph Rillos
Hydrogen Peroxide (H₂O₂) is widely used in water treatment because it can break down harmful organic contaminants. However, traditional production methods are energy-intensive and rely on centralized facilities, making them less sustainable. Electrochemical systems offer a more sustainable approach by producing hydrogen peroxide directly within the treatment plant. This reduces the need for transportation and allows for more efficient, on-demand use. Flow through reactors further improve this process by enabling continuous operation and better mixing. This research focuses on improving hydrogen peroxide production in a flow-through electrochemical reactor. Key factors such as voltage, signal amplitude, flow configuration, and reactor design are studied to understand how they affect efficiency. The goal is to develop a more effective and sustainable method for water treatment.
Department:
Department of Civil & Environmental Engineering
Faculty Mentor:
Yanghua Duan
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Photodiode-Based Measurement of Wildfire Fire Radiative Power

Jack Friesen
Colorado experiences numerous wildfires each summer that release large quantities of pollutants into the atmosphere. These emissions contribute to climate change and negatively impact human health and air quality. Fire Radiative Power (FRP) represents the radiative power output of a fire and is directly correlated with the rate of pollutant emissions. Accurately measuring FRP therefore provides valuable insight into wildfire intensity and environmental impact. Current satellite-based systems, such as the MODIS aboard the Terra and Aqua satellites, are capable of estimating FRP for large wildfires. However, these systems have limited spatial resolution and often fail to accurately detect smaller fires. This project aims to develop a compact, low-cost dual-color pyrometer capable of estimating FRP from smaller fires using infrared photodiode measurements.
Department:
Department of Mechanical Engineering
Faculty Mentor:
Shantanu Jathar
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Power Systems Against Wildfires

Kacey Hoang
Every day, the world is facing more and more issues around our planet and finding more efficient ways to sustain what we still have. In order to optimize sustainability and reduce waste, we must find ways to make the most of our resources. Colorado, specifically, is all around has extreme weather conditions and is a dry place, making it prone to wildfires. The goal of this research is to shed some light on the issue of power systems against extreme weather conditions, and specifically wildfires, by finding ways to prevent equipment from failing in high-risk conditions. Not necessarily solving it, but finding a way to mitigate wildfires and their effect on power systems. Through this, we can find more ways to optimize power systems and functionality through methods such as the prediction of wildfire forecasts in order to aid in the mitigation and prevention of damage from extreme weather conditions.
Department:
Department of Electrical and Computer Engineering
Faculty Mentor:
Zongjie Wang
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Predicting Vehicle Purchase Decisions with Large Language Models: Incorporating Social Influence from Customer Networks

Dj Cruz
Accurately predicting vehicle purchase decisions remains a longstanding challenge in the automotive industry, where reliable forecasts can support targeted marketing, customer engagement, and product positioning. Conventional prediction approaches typically emphasize individual-level characteristics, but they often give limited attention to the role of social influence in shaping consumer behavior. This study examines whether large language models (LLMs) can improve vehicle purchase prediction by incorporating both customer-specific attributes and information about each customer’s social circle. To investigate this question, we integrated new-car buyer survey data and customer-reported social-network information into structured prompts for an LLM. These prompts included demographic and behavioral variables such as household income, driving habits, and vehicle preferences, along with social-context features such as the vehicle types driven by peers and the frequency of interaction with them. For each respondent, the LLM was tasked with predicting a vehicle choice from a predefined set of candidate vehicles. The predicted choices were then compared with respondents’ actual purchases to evaluate predictive accuracy and to identify which categories of input information contributed most to model performance. The results show that prediction accuracy is highest when both individual-level and social-circle information are included in the prompt. Although overall predictive performance remains modest, the findings demonstrate the potential of LLMs as flexible tools for modeling complex consumer decision-making processes that extend beyond isolated individual characteristics. More broadly, this work highlights the value of incorporating social influence into vehicle purchase prediction and suggests a promising direction for future research on LLM-based decision modeling in automotive marketing and related application domains.
Department:
Department of Systems Engineering
Faculty Mentor:
Yinshuang Xiao
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Processing-Dependent Mechanical Behavior of Polymer Specimens

Kameron Methvin
The Bailey Research Group at CSU routinely synthesizes and characterizes novel polymers. One key method of characterization is uniaxial tensile testing, which provides important insight into the mechanical properties of these materials. Traditionally, polymer samples are hot pressed into sheets and then cut into dog bone shaped specimens using a die. To streamline this process, the group is exploring the use of direct injection molding to produce dog bone specimens. The goal of this project is to evaluate the feasibility of this transition by comparing the mechanical properties of samples produced using both methods and determining whether any significant differences arise.
Department:
Department of Mechanical Engineering
Faculty Mentor:
Travis Bailey
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Reconstruction of mechanical systems, allowing films of glass to be created for ionic beam lasers.

Mia Mendolia
Through the use of creating ultra thin layers of glass, we are able to enhance and grow our understanding of Ionic Beam Lasers. These layers are built on top of themselves, and can be used to study interactions leading to ultra-high energy density plasmas. The topic that was researched upon was the idea of pushing the boundaries of what we know regarding lasers, and where we can apply them once perfected. One of these placements includes the control of elastic energy dissipation that are used in ultra-high sensitivity gravitational wave detectors. By creating these lenses, we are able to limit the amount of power that is outputted, creating a controlled environment. The goal that is being reached by my team is the alteration of the laser mechanics, so that these beams can reach the desired spots accurately to produce the best possible outcome.
Department:
Department of Electrical and Computer Engineering
Faculty Mentor:
Carmen Menoni
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Reducing Canal Water Loss Using Advanced Textiles and Optical Fiber Sensors

Waverly Puckett
Decreasing snowpack in Colorado is reducing available water supplies, making it critical to maximize the efficiency of snowmelt as it travels through canal systems. Significant water loss occurs during transport due to evaporation and seepage, lowering overall delivery efficiency. Geomembranes, which are synthetic sheets or liners with very low-permeability, act as a barrier to fluids or gases, preventing them from seeping into the soil or surrounding environment. They offer a potential solution by reducing leakage in canal linings. When combined with embedded optical fiber sensors, these materials can enable real-time monitoring of water loss and structural integrity. This research explores the use of sensor-integrated geotextiles to improve water conservation and sustainability in canal systems.
Department:
Department of Civil & Environmental Engineering
Faculty Mentor:
Ellison Carter
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Reworking of Existing Driving Simulator for External Software Connectivity

Joel Villalobos
Driving simulators provide safe and controlled environments for research and training. Still, many, including ours at CSU, are limited by design and only compatible with one program, preventing users from using other simulation programs along with it. This lack of compatibility reduces their effectiveness in various applications. This project focuses on redesigning an existing driving simulator by rewiring and reprogramming existing hardware and software to allow communication and compatibility with various driving simulation programs. The objective is to create a more flexible system that can support various types of programs for different types of research.
Department:
Department of Systems Engineering
Faculty Mentor:
Erika Gallegos
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Small Cell, Big Data: A Miniaturized Dual‑Chamber H‑Cell Reactor for Accelerated Bioelectrochemical Analysis

Isabella Quimby
Dual‑chamber H‑cell reactors are essential tools for studying how microorganisms move electrons, but traditional versions are large, slow to operate, and require significant amounts of microbial media. These limitations make it difficult for researchers to run rapid, iterative experiments or explore new bioelectrochemical ideas efficiently. As the field pushes toward deeper understanding of microbial electron transfer and its applications in energy, sustainability, and biotechnology, smaller and more adaptable research tools are becoming increasingly important. This project focuses on creating a miniaturized dual‑chamber H‑cell reactor designed to deliver full electrochemical functionality in a compact, well‑plate‑sized format. By preserving key features, such as membrane separation and compatibility with three‑electrode potentiostat systems, this design reduces material use, shortens startup times, and supports higher‑throughput experimentation without sacrificing scientific rigor. The goal of this work is to provide an accessible, efficient platform that accelerates bioelectrochemical research and opens the door to more sustainable, scalable testing methods. Through iterative design, CAD modeling, and prototyping, this project lays the foundation for a new generation of small‑scale reactors that support both fundamental discovery and future applied innovations.
Department:
School of Biomedical and Chemical Engineering
Faculty Mentor:
Kenneth Reardon
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System-Level Redesign of Driving Simulator for External Software Connectivity

Leandro Duran
This research is to get a driving simulator to be able to use various software's beside the original software. We are starting this from scratch and have to figure our way through the complex software and hardware.
Department:
Department of Systems Engineering
Faculty Mentor:
Erika Gallegos
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The Process of Becoming a Commercial Airline Pilot

Austin Monroe
This system models the structured pathway required to become a commercial airline pilot. The goal of this work is to evaluate the efficiency and effectiveness of the pilot‑training pipeline, including an assessment of how traditional and accelerated training pathways influence readiness, safety outcomes, and overall time to qualification. It incorporates key components such as ground school coursework, flight training programs, FAA written and practical examinations, medical certification, and the gathering of required flight hours, all of which mold into a modern airline pilot. These elements interact through framework that ensures pilots-to-be meet regulatory, safety, and competency requirements. By modeling this system, the research presents analyzing on how training institutions, certification processes, and airline qualification programs integrate to produce operationally ready pilots.
Department:
Department of Systems Engineering
Faculty Mentor:
Daniel Herber
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Virtual Reality Cat Cafe for Mental Health Research

Mia Allen
Virtual reality is a three-dimensional experience that immerses users in a simulated environment by changing their surroundings and allowing them to interact with objects within the space. Studies have shown that participating in relaxing experiences, such as ones experienced using virtual reality, helps to improve mental health. The purpose of our project, a vr cat cafe, is to create another relaxing space that can be used to further study this topic.
Department:
Department of Systems Engineering
Faculty Mentor:
Marie Vans
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Virtual Reality Cat Cafe for Mental Health Research

Priscilla Shortridge
Mental Health issues, such as stress and anxiety, are serious issues that affect many individuals in society. While there are already multiple forms of mental health resources available, not everyone has access to them. In this project, our team is focusing on animal-assisted therapy, which has been proven to improve a person's mood, reduce stress, and provide comfort. While animal-assisted therapy has proven to be helpful, there are still barriers that prevent people from having access to this care, such as cost, living situations, or allergies. To address this issue, our team has helped build a Virtual Reality Cat Cafe where players can interact with the cats and other in-game objects. Our goal is to simulate the comfort and stress relief that comes from interacting with real animals through virtual reality. By using virtual reality, we have made mental health more accessible, allowing users to experience similar emotional benefits without the limitations of real life.
Department:
Department of Systems Engineering
Faculty Mentor:
Marie Vans
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Virtual Reality Cat Cafe for Mental Health Research

Abdoulaye Ballo
The goal of the project to create a virtual reality game that can be used for mental health studies. The scenario includes a cafe where patrons can order a drink and take it back to their table and have cats visit and interact with them. Over the past few semesters, this goal has been achieved by past scholars. This semester, I am aiming to improve the simulation by adding NPC's that utilize AI to make the experience more realistic. This work is significant because it bridges the gap between mental health research and game design in a way that can be fun and relaxing for the users. By adding these AI bots, I hope to enhance the project to allow for more discoveries to be made for the sake of mental health.
Department:
Department of Systems Engineering
Faculty Mentor:
Marie Vans