While conventional per- and polyfluoroalkyl substances (PFAS) detection methods, such as liquid chromatography/tandem mass spectrometry (LC-MS/MS), are highly sensitive, their costs, extensive sample preparation, and long turnaround times limit practicality for real-time monitoring. Raman spectroscopy, particularly surface-enhanced Raman spectroscopy (SERS), offers a promising alternative that can provide rapid, in-field detection of total PFAS at parts-per-trillion (ppt) levels with rapid field-ready sample preparation; however, PFAS exhibit weak Raman cross sections and poor native affinity to SERS substrates, and environmental matrices introduce significant interferences. This project will develop and validate a field-ready Raman screening tool for rapid quantitation of “total PFAS” in water, anchored to confirmation methods. The specific project objectives are as follows:

1. Design and engineer a portable Raman field screening platform that integrates sample extraction/preconcentration techniques to achieve robust PFAS quantification at ppt levels in the presence of environmental interferences.
2. Advance detection sensitivity and precision using artificial intelligence (AI)-enhanced signal processing to mitigate environmental interferences and enable automated, high-confidence PFAS measurements in field conditions.
3. Validate sensor performance using split-sample comparisons to Environmental Laboratory Accreditation Program (ELAP)-accredited laboratory results and assess impacts on sensitivity and specificity.
4. Conduct field testing of groundwater monitoring wells under various conditions for sensor development and on-site evaluation of sensor performance.
5. Facilitate technology transfer by developing a field manual, training and technology overview videos, and a user-friendly interface, while ensuring industry validation to support commercialization.

The project will develop a Raman Field Sensing Device (RFSD) that couples SERS with a rapid liquid-liquid extraction workflow optimized for field use. Because PFAS signals are intrinsically weak in Raman/SERS, the method uses a cationic Raman-active ion-pairing probe that associates with extracted anionic PFAS to generate strong, quantifiable spectral features, along with a separate Raman-active internal standard to normalize spectral variability and provide measurement-level quality assurance and quality control (QA/QC). The project is structured in the following tasks:

• Task 1 includes a structured interference assessment (e.g., dissolved organic matter, salinity/ionic strength, pH, co-occurring chemicals, and non-target PFAS mixtures) and a go/no-go decision to ensure robustness before field testing.
• In Task 2, AI-based analysis will be trained and evaluated using matrix-stratified holdouts and multiple performance metrics beyond correlation (R²) (e.g., root mean square error/mean absolute error, bias, and uncertainty bounds; and false positive/negative behavior where applicable).
• Tasks 1–3 comprise an integrated laboratory development and benchmarking effort, with Tasks 2–3 initiated in parallel during Task 1 to accelerate dataset generation and model development; however, Task 4 field testing will not be initiated until the go/no-go decision at the end of Task 1 confirms minimum field-readiness under representative matrix interferences.
• Field deployment (Task 4) will evaluate performance on groundwater samples across diverse site conditions with split-sample benchmarking to ELAP-accredited laboratory results.

PFAS-impacted groundwater and drinking water, particularly from aqueous film-forming foam use, necessitates rapid, cost-effective field screening to guide sampling density, plume delineation, and treatment process-control decisions. LC-MS/MS testing is costly and slow, delaying decisions and increasing field mobilization and project timelines. The RFSD will provide rapid, field-deployable quantitation of “total PFAS” in water with measurement-level QA/QC and confirmation-anchored validation, reducing reliance on laboratory turnaround for iterative decisions. This capability supports the Triad Approach by enabling adaptive sampling and near real-time decision-making, improving the efficiency of site assessments and long-term monitoring while lowering overall costs. Successful completion of this research will ultimately improve the cost effectiveness of PFAS management, directly benefiting the warfighter and installation communities. (Anticipated Project Completion - 2029)