SBIR Phase I: Development of a Portable Contaminant Field Screening Device for Rapid Measurement of PFAS in Water.

About This Grant

This Small Business Innovation Research Phase I project addresses the urgent need for affordable, real-time detection of per- and polyfluoroalkyl substances (PFAS) in environmental, industrial, and municipal water. PFAS are a class of persistent synthetic chemicals linked to adverse health effects and widespread environmental contamination, particularly near military bases, airports, and industrial facilities. Current laboratory-based detection methods are expensive, slow, and require specialized infrastructure, making them impractical for on-site monitoring. This project will develop a portable PFAS sensing device capable of detecting concentrations at or below regulatory thresholds directly in the field, enabling faster decision-making and more efficient remediation. The innovation will help environmental consultants, regulatory agencies, and municipalities accelerate site assessment, remediation, and compliance monitoring by improving access to rapid and reliable data. The estimated addressable market for PFAS field detection tools is expected to exceed $500 million over the next decade, driven by tightening regulations and increased public awareness. Broader impacts of the effort will include an enhancement of public health, a reduction in cleanup costs, and contributions to workforce development in environmental monitoring technologies. The intellectual merit of this project lies in the development of a novel sample preparation method and field-deployable sensor system that integrates surface-enhanced Raman spectroscopy (SERS) with artificial intelligence (AI)-enhanced signal processing. The core innovation is the complete field-ready analytical platform that enables rapid detection of PFAS at parts-per-trillion levels in complex water matrices without the need for laboratory analysis. Research objectives include optimizing nanostructured sensing substrates, developing a robust sample preparation technique for rapid PFAS enrichment in the field, and training AI models to correct for matrix interference and automate signal interpretation. The project will evaluate detection performance using PFAS reference standards and spiked field-relevant water samples, benchmarking the system against regulatory thresholds set by the U.S. Environmental Protection Agency. Anticipated results include the demonstration of a portable sensing device with a limit of detection below 4 parts per trillion, high reproducibility, and usability by non-specialist field personnel. The research will also advance the fields of portable spectroscopy and machine learning in environmental applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.