In high-intensity combat zones, military medics face immense challenges in documenting and transmitting critical patient data. The Tactical Combat Casualty Care (TCCC) card, currently used by the US military and NATO, is essential for recording injuries and treatments in the field. However, its paper-based format is prone to loss, damage, and delays in data transmission, leading to inefficiencies in medical response and treatment continuity.

Through interviews with US military personnel, we learned that 8 times out of 10, the TCCC card gets lost during evacuation, resulting in critical medical data not reaching the hospital in time. Our solution digitizes this process to enhance speed, accuracy, and integration with modern medical infrastructure

Our solution is a ruggedized military iPad application that digitizes the TCCC card through AI-powered image recognition and voice-to-text processing eliminating the inefficiencies of paper-based documentation.

Inverted PbS CQD architecture can cause damage to the CQD absorbing layer due to harsh solvents used in the ETL solution. The goal is to replace solution-phase ZnO nanoparticle ETL layer with sputtered ZnO with using the AJA Magnetron Sputterer at the Materials Characterization and Processing Center (MCP). This method deposits the ETL without a solvent, protecting the absorbing layer from damage and improving overall power conversion efficiency (PCE).

We aim to implement The Zipper Algorithm, an elementary procedure to develop a conformal map between particular regions, in such a way to minimize error. The Zipper Algorithm repeatedly applies various transformations to the complex plane, which can result in loss of precision in points that are close together and greater distance between faraway points. This project will implement each step of the map into its elementary transformations as described in Marshall and Rohde’s paper Convergence of a Variant of The Zipper Algorithm for Conformal Mapping. A function is said to be poorly conditioned if a small perturbation in the inputs results in a large change in outputs. We aim to conduct the entire mapping within the unit disc in attempts to eliminate some of the poorly conditioned steps, and in doing so provide bounds on the error that may be introduced in such a mapping.

The Separated Interface Nerve Electrode (SINE) is a critical component of the Freeform Stimulator (FS) system, a novel neuromodulation technology designed to delivery ionic direct current (iDC) for vestibular. Neuromodulation technologies are integral to rehabilitative applications that require precise control over neural activity; however, current SINE designs face challenges such as mechanical kinking and gel dehydration, which affect long-term performance. This project aimed to optimize the design of the SINE to improve flexibility, stability, and consistent iDC delivery. To address these challenges, we developed a SINE incorporating a double network hydrogel composed of ionically and covalently crosslinked networks. This formulation enhances both ionic conductivity and mechanical resilience. To prevent dehydration and ensure mechanical integration, the hydrogel is chemically bound to a flexible elastomer substrate with coupling agents. This hybrid design advances the reliability and performance of the Freeform Stimulator system in vestibular rehabilitation.