What question did this study set out to answer?

The research aims to employ machine learning to identify fluorophilic interactions that enhance SERS detection of PFOA.

February 22, 2026Open Access

Machine Learning-Enhanced Identification of Fluorophilic Interactions for Improved SERS Detection of PFOA

MPMonika PooniaWayne State University KTKathryn TerceiroUniversity of Rhode Island GBGeoffrey D. BothunUniversity of Rhode Island

Key Points

The research aims to employ machine learning to identify fluorophilic interactions that enhance SERS detection of PFOA.
Utilized machine learning algorithms to analyze fluorophilic interactions.
Employed surface-enhanced Raman spectroscopy for detection.
Examined the detection efficiency of PFAS compounds.
Machine learning significantly improved the identification of fluorophilic interactions.
Enhanced SERS detection capabilities for PFOA were achieved.

Abstract

Per- and polyfluoroalkyl substances (PFASs), such as the legacy C8 compound perfluorooctanoic acid (PFOA), pose significant environmental and health risks due to their persistence and widespread use. While surface-enhanced Raman...

Demander à l'IA

Bookmark

View Full Paper

Cite This Study

Poonia et al. (Thu,) studied this question.

synapsesocial.com/papers/699a9d50482488d673cd31fc https://doi.org/https://doi.org/10.1039/d5en00721f

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

Demander à l'IA

Bookmark

View Full Paper