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

Puntos clave

• 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.