Proof of Concept Awards

Michael Baker Minimal Motion Maximum Impact

Synopsis:

A new synthetic data generation approach is being advanced to address the shortage of large, high‑quality motion datasets used in biomechanics, rehabilitation, sports analytics, and robotics. The technology expands small samples of human or robotic motion into large, annotated datasets with realistic variability using proprietary harmonic randomization, avoiding the cost and limitations of traditional motion capture or generative AI methods. Proof‑of‑concept funding supported development of a licensable software module that integrates into existing AI pipelines to accelerate machine‑learning–driven motion analysis and training applications.

Mitchell Pryor Inspection Robot for Floating Roof Storage Tanks

Synopsis:

In the oil and gas industry, manual inspection of seals in floating roof storage tanks is inaccurate, costly, and dangerous. Inspections are increasingly necessary given our aging infrastructure and desire to minimize the release of fugitive emissions harmful to inspectors and the environment. UT innovators have developed an autonomous robotic solution that performs tank inspections safer, cheaper, faster, and more accurately than traditional, manual methods.

Nancy Moran A Probiotic Mixture that Aids in Honeybee Health

Synopsis:

Prior laboratory studies show the probiotic can successfully establish in the bee gut, activate immune and nutritional pathways, and significantly improve survival following pathogen exposure compared to existing commercial products. This project develops a probiotic mixture composed of native honeybee gut bacteria to restore healthy microbiomes and improve resistance to pathogens, addressing major drivers of honeybee colony losses that threaten agricultural pollination. Proof‑of‑concept funding supports packaging validation and field‑based efficacy trials to determine whether these laboratory benefits translate to improved hive health and productivity under real‑world beekeeping conditions, enabling go/no‑go decisions for commercialization.

Nanshu Lu Wireless Chest E-Tattoo as a Wearable Cardiac Output Monitor

Synopsis:

A significant number of adults and children develop low cardiac output syndrome after surgery or disease and it can often lead to death if not detected and treated for. Engineer Dr. Nanshu Lu and her team are developing a wireless chest e-tattoo that can provide seamless, non-invasive long-term cardiac output monitoring.

Owen Beck FOOT-STIFFENING SOCKS & LOCOMOTOR PERFORMANCE

Qian Yin A Novel Adjuvant for Pan-filovirus Vaccine

Synopsis:

Researchers at UT Austin are developing a novel, solid‑form nanoparticle adjuvant designed to enable a stockpile‑ready, pan‑filovirus vaccine that can provide broad, durable protection against Ebola, Sudan, and Marburg viruses. The platform leverages a thermostable, plug‑and‑play design that broadens immune recognition of conserved viral regions, with prior validation in influenza and coronavirus models demonstrating superior breadth and durability compared to licensed vaccines. Proof of Concept funding supports solid‑form formulation, immunogenicity testing in animal and human organoid models, and translational validation to de‑risk the technology for biodefense procurement and future commercial partnerships.

Qian Zhong Bedside Breathe-In Diagnostics for Pulmonary Fibrosis

Synopsis:

A rapid, noninvasive diagnostic platform is being advanced that uses inhaled biosensors and a simple urine test to enable early, bedside detection of pulmonary fibrosis without the need for lung biopsy. The technology leverages disease specific protease activity in the lung to generate a multiplexed urinary signal that can noninvasively distinguish pulmonary fibrosis from other chronic pulmonary disorders and lung malignancy within one to two hours. Proof of concept funding supports validation in clinically confounding disease models, development of a higher plex lateral flow assay, and preliminary safety studies to accelerate translation toward clinical testing and commercialization.

Radu Marculescu CASCADE; Deep Learning for Medical Image Segmentation

Synopsis:

CASCADE is an AI‑based, plug‑and‑play software platform designed to improve colonoscopy quality by enabling real‑time, pixel‑level detection and segmentation of adenomatous polyps. By reducing variability in adenoma detection and supporting more complete polyp removal, the technology has the potential to lower colorectal cancer risk and standardize outcomes across clinicians. Proof‑of‑concept funding supported development of a deployable MVP and clinical pilot testing within UT‑affiliated GI practices to validate workflow integration and clinical value.

Ryosuke Okuno CO2 Nanobubble Generation for Enhanced Oil Recovery

Synopsis:

A novel CO₂ nanobubble technology is being advanced to dramatically improve the efficiency of enhanced oil recovery, particularly in shale and tight formations where effective EOR methods are limited. By generating stable CO₂ nanobubbles at high pressure and throughput, the approach increases oil recovery while reducing CO₂ consumption by more than threefold, making the use of captured CO₂ economically viable. Proof‑of‑concept funding supported laboratory validation, shale core experiments, and field‑scale modeling to position the technology for pilot testing and commercialization through an emerging startup.

Ryosuke Okuno Efficient Carbon Management via Novel Pathways

Synopsis:

This award enables development of a modular, electrochemically driven carbon capture approach that replaces energy‑intensive thermal amine regeneration with bipolar electrodialysis operating at ambient conditions. By capturing CO₂ as bicarbonate and simultaneously regenerating amines, the technology has the potential to cut regeneration energy use and capture costs by more than half compared to conventional systems. Proof‑of‑concept funding supports bench‑scale optimization and pilot testing relevant to steam methane reforming and blue hydrogen production, advancing a scalable pathway for lower‑cost industrial decarbonization.

Sabyasachi Tiwari AI accelerated design of quantum materials

Synopsis:

This effort advances an AI‑driven materials simulation platform that dramatically accelerates electronic and quantum materials modeling by generating ultra‑localized representations of electronic wavefunctions. By reducing computational complexity and energy demands by up to three orders of magnitude, the approach enables advanced materials simulations to run accurately on standard desktop hardware rather than leadership‑class supercomputers. Proof of concept funding supports development of a cloud‑based, user‑friendly software platform that integrates with existing materials simulation workflows and positions the technology for adoption by academic labs, national facilities, and industrial R&D teams.

Salvatore Salamone Rail Defect Detection by Noncontact Vibration Measurements

Synopsis:

Nonvisible transverse defects in railways are one of the main causes of railway track-related incidents, costing hundreds of millions of dollars in the past two decades. Current rail inspection technologies cannot be mounted on operating train cars and are only reliable at slow speeds, costing railway operators time and money. UT engineers have developed a laser doppler system that can be used on operating trains and at much higher speeds.