Preventing train derailments is a critical challenge in railroad maintenance. It requires advanced monitoring systems that can detect potential issues before they escalate into catastrophic failures. The existing Sentinel Motion Internet of Things (IoT) system addresses this challenge by monitoring vibration and temperature data from railcar wheel bearings. While it is effective in identifying critical faults, its current IoT hub device is constrained by a limited transmission range. This project enhances the Sentinel Motion system by extending its data collection and transmission capabilities to improve predictive maintenance.

The team developed a vibration-powered, wireless train network (WTN) node which meets low size, weight and power constraints. The WTN node serves as an intermediate network host between Ridgetop’s Sentinel Gateway IoT hub and RotoSense sensor nodes. In this role, the WTN node facilitates seamless data collection and transmission. An integrated SAM R21 microcontroller efficiently routes data packets across multiple communication interfaces while optimizing power consumption for maximum operational longevity. A piezoelectric transducer array-based vibration energy harvesting system powers the device and incorporates safeguards against extreme vibrations and excessive voltages. This ensures complete energy self-sufficiency while facilitating communication.

CKD affects approximately 37 million adults in the United States. Many of these patients also experience abnormal electrolyte levels that can lead to further comorbidities. Hyperkalemia (high potassium) occurs in 40% to 50% of patients. This condition can result in dangerous heart rhythm problems. Additionally,
hyperphosphatemia (high phosphorus) occurs in 70% to 80% of patients and contributes to bone disease and increased cardiovascular risk. Knowing CDK patients’ potassium and phosphorus levels is therefore critical.

The team addressed this need by creating a dual-measurement, AI-based approach to determining food electrolyte content. The system combines image recognition technology for rapid, everyday assessment with physical measurements via “grind and find” for high-accuracy validation. The image recognition system leverages machine learning and web-based information to continuously improve its accuracy in classifying food items and estimating their potassium and phosphate levels. The
grind and find method represents the team’s ongoing research to establish precise measurement standards for potassium and phosphorus in food items. This in turn helps validate and improve the image recognition system’s accuracy.

Catheter-associated infections are a major health care concern. These infections often lead to prolonged hospital stays and increased patient morbidity. One solution is to coat the catheter with antimicrobials. However, traditional antimicrobial coatings degrade over time, making them ineffective in the long term. In this project, the team designed and tested a self-disinfecting urinary catheter that uses low-dose 405 nm light to continuously reduce bacterial colonization for several hours.

The team’s solution integrates fiber-coupled 405 nm LED light sources. These effectively impede bacterial growth while remaining safe for human tissue in controlled doses. A clear catheter material ensures efficient light transmission, and a low-power, embedded control unit regulates continuous light exposure at key disinfection points. The team carried out efficacy testing on the final prototype to assess its effectiveness in reducing infection risks.

This solution provides a long-term, non-chemical infection prevention method for catheterized patients. It is easy for medical staff to apply and safe for use within a hospital environment. With this design, the team has succeeded in the goal of reducing hospital-acquired infections and antibiotic reliance in human and veterinary catheter use.

Geotechnical drilling rigs like the CME 75 rely on diesel engines for high power output. However, conventional systems generate significant emissions and require frequent maintenance. To address these limitations, the team designed the Hybrid Electric Drill Retrofit (HEDR). It is a system for exploring the feasibility of using hybrid electric power as a sustainable alternative to conventional engines. By integrating multi-disciplinary engineering design, analysis and rapid prototyping, the team aimed to reduce emissions, lower maintenance costs and optimize energy use while maintaining drilling performance.

The team developed a system that integrates an electric motor, a generator and a battery stack to optimize power usage and extend operational lifespan. The electric motor directly interfaces with and drives the drill bit through a five-speed transmission. A battery stack powers the motor while the generator supplies supplemental energy to the batteries. A microcontroller controls this process. It manages power distribution, ensures optimal efficiency and collects real-time performance data.

The HEDR project offers a more sustainable and cost-effective alternative for field operations and demonstrates the feasibility of hybrid-electric solutions in geotechnical drilling.

Keeping electronics cool isn’t just about performance, it’s about survival. Excess heat can cripple efficiency, shorten lifespan or even trigger catastrophic failures. Enter the nitinol-aluminum (NiTi-Al) thermal diode, a potentially cutting-edge solution designed to preferentially control heat flow and directionally. It is analogous to the one-way electron flow of an electric diode. The NiTi-Al thermal diode could revolutionize heat management, ensuring that critical systems operate at peak performance and enhancing everything from military technology to next-generation computing and nanotechnology.

A paper by Dr. Franziskonis proposed this theory. It asked, can phase-transforming smart materials like nitinol – an alloy made of nickel and titanium – create a highly effective thermal diode? To find out, the team designed and built a specialized rig to experimentally test and analyze heat flow and thermal rectification efficiency. An insulated firebrick enclosure housed the test samples to ensure controlled thermal conditions while a patch heater simulated the thermal temperatures encountered in missile flights. Thermocouples attached to the samples sent data to a laptop through a data logger so the team could analyze the thermal diode’s performance versus control samples. Based on the test results, the team concluded that these NiTi-Al thermal diodes could be highly effective in specialized applications.

Many patients need to spend a long time in hospital beds. However, the friction and pressure of long-term bed rest can cause discomfort and further injury to severely burned or injured patients. Suspending a patient in fluid could potentially eliminate these issues and facilitate healing with the right mixture of medications. In this first-year project, the team demonstrated a half-scale model to show how some parts of the system will work in a fully realized system.

This project relied on biomedical, mechanical, electrical, materials and hydraulic engineering. All of these disciplines came together to create a completely original suspension tank. The tank includes temperature and pH sensors to monitor the state of the fluid. This ensures the comfort and safety of the patient and provides care for them in this sensitive state. The suspension tank operates on a rotating axis to allow for easy ingress and egress and to allow health care workers to monitor the patient’s recovery. It is also equipped with a waste-evacuation system that senses and removes human waste. The results of the half-scale model show promise and display the potential for future teams to build a full-scale solution.

Fuel scarcity and plastic waste pose major challenges to the environment and human health. This is especially true in disaster areas. One solution is pyrolysis oil, which converts plastic waste into fuel but requires further processing. In this project, the team developed an oil separation module to refine pyrolysis oil into five fractions to improve output oil usability.

The team’s solution uses a heating mantle to heat crude pyrolysis oil in a flask. The system first purges the flask with nitrogen to remove oxygen. It then applies a vacuum to regulate pressure. As the oil heats, it vaporizes and enters a Vigreux column where separation occurs. Lighter fractions rise, condense and collect in a receiver, while heavier fractions remain for further processing. The flask rotates for batch processing, and the cycle repeats at different temperatures to extract all fractions.

To ensure control, the system uses two thermocouples to monitor temperature and a vacuum pressure gauge to verify precise conditions. The team confirmed safety and fuel viability using gas chromatography mass spectrometry analysis to determine the chemical composition of each fraction. This portable and scalable system is an effective waste-to-energy solution that can be used in off-grid and disaster recovery applications.

Arizona law A.R.S. 28-735 mandates that vehicles maintain a minimum 3 ft passing distance when overtaking cyclists, yet no enforcement mechanism currently exists. This project seeks to develop a handlebar-mounted detection device that identifies and documents violations to provide evidence and improve cyclist safety.

This phase of development focused on high-speed detection methods to ensure accurate data capture for vehicles traveling at speeds of up to 45 mph relative to the bicycle. The goal was to deliver five functional prototypes for sponsor testing.

The system uses a time-of-flight sensor for rapid sampling. If a vehicle encroaches within the 3 ft threshold, an ESP32-S3 Sense microcontroller triggers a forward-facing camera to capture a burst of images and activates an LED alert for the cyclist. The device integrates with a companion smartphone app that logs GPS location, uploads images and auto-selects key frames from the incident.

Designed for versatility, the modular housing supports both drop-bar and flat-bar handlebars to ensure broad compatibility across different bicycle types. This innovative system provides a practical enforcement tool to enhance cyclist safety and promote compliance with driving laws.

Dehydration and other heat-related health risks are a major concern in Arizona. In this community and population project, the AZ Heat Medicine team created a surveillance system that combines a drone, a point-of-care test, and a software platform to monitor, address and potentially treat dehydration.

The system uses visual cues, regional temperature data and historical hydration results to assist a drone operator in locating individuals in the community that are at risk of heat-related injury. Once the operator has identified an at-risk individual, they can assess the individual for dehydration. This point-of-care test employs a conductivity sensor to analyze a provided urine or saliva sample for hydration levels. Depending on the assessment, the operator can also dispatch fluids for rehydration via a small deployable parachute system. A software platform allows for the communication between the drone and operator. It also tracks community hydration trends and securely stores the collected data for further analysis.

By merging these advanced subsystems, the team has created a data-driven solution that promptly assesses and responds to dehydration. This will ultimately help mitigate heat-related health risks, enhance community well-being, and potentially reduce mortality and morbidity for thousands of people in Arizona.

This project demonstrates how engineering can replicate the fine control and decision-making required to accurately and consistently putt a golf ball. It leverages concepts from various engineering disciplines from robotics to sensor integration and many more complex and fun learning opportunities. The goal of showing off these engineering skills and applications is to engage young students in STEM fields.

The Golf Putting Robot, fondly referred to as GoPheR, is a design project aimed at perfecting the complex human task of putting on a golf green. The system utilizes a lidar, or light detection and ranging, sensor to create a detailed map of the green. This allows the robot to assess slope, surface variations, and optimal paths for a successful putt. GoPheR also uses two on-board cameras for object recognition of the ball and flag and for alignment and tracking. The robot has on-board stepper motors to precisely adjust GoPheR’s position relative to the ball and the speed at which GoPheR’s putter contacts the ball. This simulates human-like putting motions. With these core components, GoPheR can ensure completing a hole in two putts or fewer from any point on a regulation putting green.