Thursday, November 21, 2024
11/21/2024
This NIH SBIR Phase I project addresses the critical need for rapid, in-field, and accurate monitoring of uranium in drinking water sources. Uranium contamination poses severe environmental and public health risks, particularly affecting vulnerable populations. Currently, available rapid colorimetric tests lack the sensitivity required for research and clinical purposes, demanding skilled labor and capital-intensive equipment for accurate detection. We aim to bridge this gap by developing an innovative Surface-Enhanced Raman Spectroscopy (SERS)-based platform capable of real-time, trace-level uranium quantification in water. In Phase I, we will focus on developing the essential chemistry for uranyl detection using our proprietary SERS platform. This entails creating a calibration curve with laboratory standards to achieve quantification exceeding 10% accuracy and a detection limit below 30 ppb, aligning with the EPA Maximum Contaminant Level. Subsequently, we will validate the detection strategy by evaluating its performance against known interferents in lab standards and applying it to thirty real-world water samples. Rigorous testing and comparison with established laboratory techniques will demonstrate the method's accuracy and reliability. Our research design harnesses state-of-the-art SERS technology, offering rapid, on-site uranium quantification. Successful completion of these aims will significantly contribute to enhanced water quality assessment and informed decision-making, ultimately safeguarding public health and the environment. This project serves as the cornerstone for subsequent phases, including commercialization and widespread adoption of this groundbreaking technology.
HHS’ Tracking Accountability in Government Grants System (TAGGS) website provides access to detailed descriptions of grants, loans, aggregated direct payments and other types of financial assistance awarded by the HHS Operating Divisions (OPDIVs) and the Office of the Secretary Staff Divisions (STAFFDIVs).
