Surface-Enhanced Raman Spectroscopy Detection of Per- and Polyfluoroalkyl Substances in Aqueous Film-Forming Foams

TL;DR

This paper introduces a new method using gold nanoparticles and Raman spectroscopy to detect and identify specific PFAS chemicals in complex mixtures at low concentrations.

Contribution

The novel use of concave cubic gold nanoparticles for SERS enables rapid and portable detection of six specific PFAS compounds.

Findings

SERS with gold nanoparticles can detect PFAS at parts per million concentrations.

The method differentiates six regulated PFAS compounds and distinguishes them from fluorinated pharmaceuticals.

SERS results were validated with mass spectrometry in real-world aqueous film-forming foams.

Abstract

Identifying the presence
and identity of per- and polyfluoroalkyl
substances (PFAS) in complex mixtures is critical for water treatment,
hazardous waste cleanup, and the identification of workplace hazards.
The cost and scale of conventional methods for PFAS detection often
make rapid and portable detection challenging. Here, we use concave
cubic gold nanoparticles for surface-enhanced Raman spectroscopy (SERS)
to detect PFAS in parts per million concentrations, differentiating
the 6 PFASPFHpA, PFNA, PFDA, PFOA, PFHxS, and PFOSregulated
in the Commonwealth of Massachusetts by the Department of Environmental
Protection (MassDEP). Calculated Raman spectra, solid-state Raman
spectra, and 19F NMR are used to further understand the
physicochemical properties of these 6 PFAS. Quantitative analysis
of PFOA and PFOS can be achieved from 0.1 to 10 ppm, while PFAS can
be differentiated from three common fluorinated pharmaceuticals, and
perfluoroalkyl carboxylic acids (PFCA) can be differentiated from
C7 to C10 based on the length of the perfluoroalkyl backbone. Finally,
we highlight that SERS can be used to identify PFAS in real-world
aqueous film-forming foams (AFFFs), as confirmed separately by mass
spectrometry. These results advance our ability to detect and analyze
PFAS in real-world samples relevant to environmental monitoring and
analysis.

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