Knowing platelet-derived microparticle (PDMP) levels is important for proper treatment of patients with cardiac complications. Currently, determining a patient’s PDMP levels is a lengthy process that requires expensive lab equipment. The team designed and developed a dynamic light scattering system as a cost effective, point-of-care alternative for sizing and quantifying PDMPs in a blood sample.

The design uses a 520 nm laser that shines through the buffy coat of a patient’s blood sample. The PMDPs within the buffy coat interact with the beam to create light scatter that is captured by an avalanche photodiode. The intensity signal is read by an oscilloscope and processed by a Raspberry Pi to create a calibration curve using an autocorrelation function. These calibration curves are used to determine the size and quantify the PDMPs within the blood sample. A graphical user interface displays particle size and concentration. The Raspberry Pi saves and stores the optical analysis data for each run, and this data can be transferred to a separate device if desired. The entire system is housed in a dark, closed container that allows for sample access while ensuring a reliable environment for optical analysis.

Kidneys are vital organs involved in the balance of fluids and electrolytes in the body. Intaking too little or too much potassium can have serious consequences on a person’s health. For patients with chronic kidney disease, it is important to precisely moderate potassium intake. This project presents a portable potassium diagnostic system which can directly measure potassium content in food and fluid, as well as estimate potassium content through image recognition.

To directly measure potassium content, the system uses a probe which is placed into a food sample prepared using the system’s homogenizer. A camera system then takes photos to identify and estimate the volume of the food or fluid. A Raspberry Pi computer contains both a software package with an evolving database listing potassium content for specific substances and a graphical user interface which allows users to easily track and record daily potassium intake. The recorded measurements are available for use by health care providers.

Blood clotting and related diseases account for the majority of hospitalizations and disease-related deaths in the United States, affecting more than 35 million people each year. By measuring the stiffness of platelets, medical researchers can create and refine implantable medical devices to reduce a patient’s risk of developing these life-threatening conditions.

The CytoMech is a compact and cost-effective system to measure platelet stiffness. Platelets are suspended inside a microfluidic chip, imaged by a fluorescence-based microscope camera, and analyzed by the team’s software system. The CytoMech uses a method known as dielectrophoresis to stretch platelets by subjecting them to a nonuniform electric field while the image analysis system measures the change in platelet size. Researchers can use a user-friendly graphical user interface to control the system, view the platelet while specifying the electrical force applied to it, and display the final result to the user. The system calculates the stiffness of the platelet based on the applied force and the deformation. By averaging the results of tests with multiple platelet samples, clinicians will be better able to evaluate the safety of implantable medical devices and diagnose a patient’s risk for thrombosis.

Nearly 37 million people in the United States suffer from respiratory diseases such as asthma, chronic bronchitis and lung cancer. Physicians can’t capture all sound that occurs during doctors’ visits, so much of the nonverbal information from patient encounters is not extracted for further analysis.

The sound analysis system consists of three subsystems. A handheld device containing a digital stethoscope captures patient sounds using a Raspberry Pi Zero W, and a room audio capture system uses high-quality microphones to record the doctor-patient interaction. These two systems then transmit data to the third component: a server which analyzes and processes the recorded sound files using a Raspberry Pi4 and stores them securely within the clinic database. The system is equipped with various open-source codes using Python and MATLAB to extract and analyze sound components.

Condition-based maintenance (CBM) systems that use wireless sensors are becoming a popular and useful tool for identifying maintenance needs and preventing system failure. This project presents a vibration-based energy harvesting method to extend the IoT sensor battery life to around three to five years in a railroad operating environment.

The team developed an energy harvesting solution that captures vibrational and solar energy from the operating environment to recharge the IoT sensors in the CBM system. Piezoelectric generators capture the vibrational energy when vibrated at certain frequencies, and solar panels on the exterior of the system capture the solar energy. An onboard microcontroller monitors the incoming harvested energy and connects the source to the rest of the circuit when a certain voltage threshold is met. The battery charging circuit is equipped with a load sharing circuit, which allows power to be shared between the incoming power and battery to power the system.

In a gas turbine engine, the pressure ratio throughout the compressor stage can increase to a critical state and cause the inlet flow of air to reverse its direction. This event is known as “surge” and can lead to catastrophic damage to aircraft engines. The team designed a simplified section of a gas turbine engine and tested a potential solution: stabilizing the pressure ratio by introducing a secondary inlet flow that decreases downstream pressure.

The test piece includes a 3D-printed bottom hub and top plate, stator airfoils, and a tube that introduces secondary flow using a standard air compressor. The team selected tubing components that would cause little pressure drop from the air compressor to the main inlet flow to ensure accurate data. The mass flow rate of air introduced into the main flow is controlled by varying inlet pressure from the compressor. The velocity of the secondary flow is manipulated to decrease downstream pressure.

The team conducted testing in a wind tunnel running at 100 mph. They recorded pressure using a pitot-static tube rake, which can change height based on the pressure change during computational fluid dynamics testing. This data will inform gas turbine engine design by determining exactly the velocity needed for the secondary inlet flow to help prevent a surge event.

Hyperspectral cameras, which can view the unique spectral fingerprints of an object, have consumer applications including detecting the freshness of produce and validating the authenticity of currency. However, the cost and size of these devices makes it challenging to breach the consumer market with these applications. The team’s nontraditional technique proved that a cost efficient design can be achieved by using a diffractive lens to create chromatic aberration.

The team researched and developed a specialized diffractive lens system with a motorized detector to create a hyperspectral imager functionally similar to commercially available ones. The lens system separates light out by color to create spectral-dependent focal planes. These focal planes are captured by a motorized detector, which is user-controlled via a Raspberry Pi. These images are sent to a separate computer, which performs image processing to synchronize the captured images with the location of the detector and identify the spectrum of an object.

The world’s population is projected to increase to 10 billion by the year 2050. Traditional agriculture is costly and inefficient compared to Controlled Environment Agriculture (CEA). New technologies have reduced the operational costs of CEA, but humans still perform the harvesting operations, which is costly. The team created a machine that reduces the need for manual labor when harvesting produce from greenhouses or vertical farms.

The machine can cut the leafy greens or microgreens, cut the roots, remove the growing media from the foam growing board and direct these items to the next step in the process. The team focused on using readily available commercial components to minimize cost and improve availability of replacement parts. The design includes hedge trimmer cutting blades, stepper motors and conveyor belts, as well as an aluminum frame with a platform that accepts foam growing rafts. The operator uses a touchscreen linked to a Raspberry Pi single-board computer and an Arduino microcontroller to indicate which product is being harvested.

This product met all of its system requirements and greatly reduced the time and labor costs associated with the harvesting operation in CEAs.

The Smart Lysimeter automates the process of collecting data on the nutrient solution, pH, electrical conductivity (EC), and drainage rate in a greenhouse, eliminating the need for manual labor. These metrics are crucial for maintaining the proper environment for crop growth and resource allocation. This lysimeter fills a market niche by automatically collecting all the necessary data to make real-time greenhouse operation decisions, without requiring high-tech, expensive equipment. It can also be transferred between greenhouses and crops with just a few simple steps.

The design consists of a divided tank, each side equipped with a pH probe, EC probe, fluid level sensor and pump. The two-section tank allows for monitoring of both the solution that drains out of the plants, and the solution being given to the plants. After data is collected, the solutions are pumped out of the system and into a drainage channel so they can be recycled. The team developed software to autonomously collect data from the sensors at appropriate time intervals, process the data and display it in a user-friendly manner. A Raspberry Pi serves as the microcontroller and data storage unit, allowing for a historical log of data.

Optical fibers are used to perform highly accurate laser measurements in photolithography machines. External factors can affect the beam propagation in these fibers, so modifying the laser settings to accommodate the variations leads to higher yield on silicon wafers (semiconductors). This project predicts output field distribution, intensity and power loss from external effects as the light propagates through the fiber.

The team modeled the structure for simulation using MATLAB, which allows integration into the model the sponsor currently supports. The code is modular and derived from preexisting mathematical models. This allows for separate functions to simulate each of the effects independently. The team’s design for the experimental validation component of the project features a supercontinuum laser, and narrow band pass filters to test individual wavelengths. The laser is then coupled into an optical fiber with a bi-convex focusing lens where the fiber is exposed to bending, twisting and thermal effects. A Newport Si power detector records the power before and after coupling.