Water pollutants – including heavy metals, inorganic contaminants, and microplastics – pose significant risks to both the environment and public health. A system for monitoring these “water baddies” is a necessary part of understanding this contamination and taking steps to correct it. Unfortunately, traditional water testing methods rely on laboratory-based analysis, which is expensive, time-intensive and often inaccessible to the general public. This project introduces a functional, small footprint/point-of care analysis system for detecting these water baddies to ensure clean water for a safe future.

The team used a colorimetric and fluorescence-based approach for rapid, on-site water monitoring in an easy-to-use desktop clinical device. The result is a novel paperfluidic device that uses commercial test strips to detect heavy metals and inorganic contaminants and fluorescent microscopy – using a digital microscope, blue LED, and a 520 nm bandpass filter – to identify microplastics. The system employs a dropper mechanism and conveyor belt to deliver samples precisely to detection zones. Results are captured via imaging, analyzed through software, and accessible via a Bluetooth-enabled mobile app. Through experimental calibration, the team determined that the device can successfully detect contaminants well below the limit of EPA National Primary Drinking Water Regulations.

The range and payload capacity of current UAVs are constrained by battery energy density limitations. This project addresses this challenge by building and testing a scalable hybrid-electric system that combines a combustion engine with electric propulsion to achieve the power-to-weight ratio required for cargo missions. The team began by simulating the powertrain in MATLAB Simulink to evaluate performance under flight conditions. Although the test system was not flyable, it was designed to scale to a UAV with a 2,200 lb. gross takeoff weight including a 1,000 lb. payload capacity with a two-hour runtime.

The team delivered two main components: a tabletop powertrain and a Simulink simulation. The powertrain is hybrid-electric with a complex data acquisition system connected to the powertrain that monitors diagnostics in real time. The simulation allows a user to customize the powertrain configurations and analyze performance over various flight profiles. Key subsystems include the power distribution system for efficient power management and the data acquisition system for user control and data collection. This proof-of-concept demonstrates the feasibility of hybrid-electric systems for heavy-lift UAVs and paves the way for future development.

The RDRE is an innovative approach to propulsion with the potential to be 25% more efficient than conventional rocket engines. This project will leverage this technology to reduce cost and improve sustainability. Conventional rocket engines use subsonic combustion – the same process as a car engine – while RDREs use supersonic combustion, also known as detonation. The key component of the RDRE is an annular combustion chamber where a detonation wave continuously propagates at supersonic speeds to efficiently burn the injected propellant.

The team designed, simulated, fabricated and tested a prototype RDRE. The focus was optimizing the detonation wave dynamics and ensuring the structural integrity of the engine under operating conditions. The team’s RDRE uses an injector plate designed to mix liquid propellants and ensure stable detonation in the combustion chamber. A deflagration-to-detonation transition tube starts a detonation wave which continuously travels around the chamber. The engine also features an aerospike nozzle to generate optimal thrust at various altitudes. The team’s tests demonstrated detonation-based propulsion’s potential to advance rocket technology, and the project has real-world applications in space exploration and defense.

ASML is a global leader in lithography for mass producing semiconductor chips. The company uses optical techniques to measure wafer topology in its commercial lithography systems. ASML’s current method for qualifying the lenses used in level sensors relies on a double-pass interferometer. While this method is effective, it also has serious limitations: It is complex, requires a large facility and is time-consuming. To address these problems, the team explored alternative single-pass metrology
techniques that can improve efficiency while maintaining accuracy and repeatability.

The team evaluated three approaches and then selected the Shack-Hartmann Wavefront Sensor for its simplicity and availability. After confirming this sensor’s potential with extensive theoretical analysis and modeling, the team designed and built a test setup around the sensor to validate the theoretical findings through experimental testing. This testing confirmed that the system successfully measures and displays wavefront aberrations introduced by the test lens. Repeated measurements demonstrate the setup’s accuracy and repeatability, confirming its potential as a viable alternative to the more complex double-pass interferometer that ASML currently uses.

The slew ring assembly is a critical component of a hydraulic mining shovel which allows the machine to rotate smoothly. A slew ring cover sits atop the slew ring to retain grease within the assembly. Traditional welding techniques for adhering components of the cover often cause distortion within the thin sheet metal, leading to grease leakage. This reduces lubrication within the assembly, raises environmental concerns and increases maintenance costs. For every hour of downtime of these machines, a customer can lose over $200,000 in potential revenue. This project explores the use of structural adhesives as an alternative to welding to prevent any distortion within the slew ring cover.

The team’s design uses structural, acrylic adhesives to bond the components of the slew ring cover to preserve the surface flatness of the sheet metal. This required an entire redesign of the cover to limit welding and incorporate these structural adhesives throughout. A series of mechanical, environmental and virtual tests validated the adhesive bonds’ strength, lifespan and resistance to mining conditions. After further development, these adhesives can enter the Caterpillar ecosystem and be used on a variety of machines for years to come.

Non-native fish threaten local aquatic ecosystems and the economies that depend on them. Biological and chemical solutions are controversial and often don’t allow for selection between native and non-native fish. Mechanical removal methods are promising and less controversial but need to be optimized to maximize control of non-native fish and minimize cost.

Electrofishing – a method of mechanical fishing that uses an electric field underwater to stun or herd fish via involuntary muscle contractions – has traditionally been used for sampling rather than population control. This project introduces a specialized electrofishing system designed specifically for efficient non-native fish removal in rivers and streams.

The team developed a system with a modular, width-spanning frame that operators drag upstream. Each section is equipped with anode and cathode droppers that generate a controlled electric field to guide fish into a bag seine for manual removal. The system is powered by a generator and managed through a control box. Users can adjust voltage, frequency and current to optimize effectiveness based on the target fish species.

By combining precision electrofishing with strategic fish herding, this solution provides a scalable, nonchemical approach to invasive fish management. This balances ecological preservation with practical implementation.

Photolithography machines use optical alignment sensors to precisely position and overlay photomask layers by detecting the reflection of polarized light from silicon wafer gratings. High alignment accuracy is crucial for wafer pattern transfer quality. This can be optimized using unpolarized light, which has the potential to maximize total signal detection. It is achieved by using coherent light sources and a depolarizer.

The team developed two theoretical models for depolarizing a broadband, visible/near-infrared input beam of any polarization state: Fiber Ring and Lyot. They chose the Lyot depolarizer and conducted experimental verification on a prototype of this passive, all-fiber model.

The Lyot depolarizer is made of two lengths of polarization-maintaining fiber spliced together, with one rotated 45 degrees relative to the other. The team selected a red fiber-coupled superluminescent diode as the coherent source for experimental setup. The light from this diode is routed through free space to control its polarization state, coupled into the depolarizer, and emitted back into free space, where the team took measurements with a Stokes polarimeter to verify the degree of polarization.

Many people around the world live in places where their dedicated health care workers do not have access to advanced medical expertise. MD-Sensei is a mobile app designed to support health care workers in remote and developing areas by providing AI-powered medical guidance, with or without internet access. It is available on iOS and Android and features an intuitive chatbot trained on decades of clinical expertise. This allows health care workers to quickly access real-time diagnostic support and treatment recommendations. When users need additional expertise, they can escalate cases to on-call medical doctors through the app. These physicians log into a secure admin website where they can review AI-generated responses, join conversations, and provide direct guidance in real time. The admin website allows clinic administrators to monitor AI-assisted interactions, track analytics, perform quality assurance checks and manage user accounts.

By seamlessly integrating AI-driven assistance with human expertise, MD-Sensei empowers frontline workers to deliver more informed, reliable care. The system bridges the gap between local clinics and experienced medical professionals so that even the most remote health care facilities have access to expert support when they need it most.

Solar power is an increasingly efficient method of electricity generation and has become an attractive prospect for many homeowners. However, customers have no way to corroborate estimates from a solar power provider and cannot know if the investment in solar panels truly makes sense for them. To address this unknown, the team designed the SPEK, a portable kit that users can rent to accurately measure the solar power potential of their home. It monitors the power output of a small, built-in solar panel throughout the day and transfers that information to a cloud-based application. It also includes a frame and electronics enclosure to ensure the SPEK will remain securely on homeowners’ roofs, insulated from rain and excessive heat.

The team developed an electronic system to accurately measure the power output of the solar panel, while also using the panel as a power source. The design includes an ESP32-C6 microcontroller, which controls the data collection and sends it to the team’s database via the homeowner’s Wi-Fi network. Users can access this data on both the website and the app, allowing convenient access to calculated usage statistics and savings estimations.

Reconnaissance is a key part of situational awareness and improves strategic decision-making. Elbit Systems of America (ESA) – a provider of tools for defense, aviation, and medical instruments – tasked the team with designing DORC. It will use visible and NIR laser technology to illuminate and capture video feeds of targets up to 500 m away. It must also be able to operate during twilight and low-light settings and resist basic outdoor operational conditions such as high temperatures and dust. The overall goal is to create a device that can serve as test equipment for ESA’s development of future electro-optic devices.

The team designed DORC with four subsystems: optical, mechanical, electrical and software. DORC can project visible or NIR light onto the target depending on the powered laser diode. A sensor then captures the target and sends the information to a Raspberry Pi for processing and display. A handheld 3D-printed shroud that can be mounted on a standard tripod houses these subsystems. This allows a user to control light output and camera field of view. The DORC can run for six hours on battery or indefinitely on wall power.