Sensing Antibiotics in Wastewater Using Surface-Enhanced Raman Scattering.

Area of Science:

• Environmental Science
• Analytical Chemistry
• Materials Science

Background:

• Antibiotic contamination in wastewater poses environmental and health risks.
• Effective removal strategies require rapid and cost-effective detection methods.
• Surface-enhanced Raman scattering (SERS) enables label-free, real-time sensing of contaminants.

Purpose of the Study:

• To evaluate gold nanostructures as SERS substrates for label-free quinoline detection in wastewater.
• To determine the limit of detection (LoD) for quinoline using different SERS substrates.
• To assess the impact of wastewater matrix effects on SERS-based antibiotic quantification.

Main Methods:

• Fabrication and testing of a self-assembled gold nanostructure SERS substrate.
• Evaluation of a commercial gold nanopillar SERS substrate (SERStrate).
• Quantification of spiked quinoline in wastewater and pure water samples using SERS.
• Application of machine learning algorithms to spectral data analysis.

Main Results:

• The self-assembled SERS substrate achieved a quinoline LoD of 5.01 ppb in wastewater.
• The SERStrate showed high sensitivity in pure water (LoD 1.15 ppb) but poor performance in wastewater (LoD 97.5 ppm) due to interferences.
• Machine learning models offered limited improvement in spectral separation, highlighting the importance of SERS selectivity.

Conclusions:

• SERS holds potential for label-free environmental sensing of antibiotics.
• Self-assembled gold nanostructures demonstrate promise for wastewater analysis.
• Further research is needed to develop practical SERS-based environmental monitoring technologies.