This project aims to improve the performance of solid-state batteries by redesigning the cathode structure using 3D printing. Traditional cathodes limit ion and electron transport due to disorganized internal pathways. To address this, we use 3D-printed polymer lattice molds made from polycarbonate-carbon fiber (PC-CF) to guide the structure of lithium iron phosphate (LFP) cathodes. The PC-CF decomposes during sintering, leaving behind organized channels that enhance conductivity. We optimized printing parameters using the Prusa i3 Mk3S+ and tested materials using optical microscopy, DSC/TGA, and Raman spectroscopy. This method allows precise control over porosity and tortuosity to improve battery efficiency, durability, and safety.

Our team has partnered with GKII to enhance Latent Tuberculosis Infection (LTBI) screening protocols in India. Over the summer, we conducted ethnographic research in India to gain insights into the tuberculosis landscape. Current protocols require patients to return to the clinic 2-3 days after their initial visit, where a clinician measures the induration of a skin test (Cy-Tb) using a ruler. This process is time-consuming and limits screening capacity. Based on our findings, we identified a critical need for an accurate, home-based method to measure Cy-Tb test results, eliminating the need for repeat visits and streamlining the screening process. To address this, our team has spent the past year developing a scalable, low-cost medical device aimed at improving LTBI screening efficiency and accessibility in resource-limited settings.

This project showcases the functional verification and live demonstration of a custom silicon chip implementing a Recurrent Spiking Neural Network (RSNN)—a bio-inspired architecture designed entirely through natural language prompts to a Large Language Model (LLM), specifically ChatGPT-4.
The RSNN was synthesized from Verilog code generated by ChatGPT, validated on tasks like XOR, IRIS classification, and MNIST, and ultimately fabricated through the open-source TinyTapeout ASIC shuttle using SkyWater’s 130nm process.
To verify the fabricated chip, our team developed a complete hardware-in-the-loop test framework using microcotb, a Python-based cocotb-like environment adapted for embedded systems. We also created custom MicroPython firmware for the Raspberry Pi Pico to enable direct communication with the chip for streaming neuron parameters, injecting spike inputs, load test scripts and reading output spike trains in real time.

Our project aims to improve the diagnosis of Acute Compartment Syndrome (ACS), a condition caused by increased pressure in muscle compartments, which can lead to muscle necrosis, nerve damage, loss of function, and in severe cases, even amputation or death, if not treated promptly. The current standard of care—using the Stryker needle—is invasive, painful, and unreliable. We propose a non-invasive alternative using Electrical Impedance Tomography (EIT), which measures electrical signals on the skin’s surface to detect changes in tissue composition. By tracking impedance patterns, our system can monitor fluid buildup and ischemia progression in real time, providing clinicians with a visual tool to identify the critical window before tissue necrosis.