Trace detection of antibiotics in wastewater using tunable core-shell nanoparticles SERS substrate combined with...

Area of Science:

• Environmental Science
• Analytical Chemistry
• Nanotechnology

Background:

• Antibiotic contamination in aquatic ecosystems poses significant environmental and health risks, including the development of antibiotic-resistant bacteria.
• Wastewater treatment requires effective methods for detecting and quantifying residual antibiotics.

Purpose of the Study:

• To develop a novel, rapid, and highly sensitive method for the quantitative detection of antibiotics in wastewater.
• To utilize surface-enhanced Raman scattering (SERS) coupled with computational methods for antibiotic analysis.

Main Methods:

• Core-shell nanoparticles (GNRs@1,4-BDT@Ag) were synthesized for SERS applications.
• SERS spectra of ciprofloxacin and levofloxacin at various concentrations were acquired.
• Machine learning algorithms, including Principal Component Analysis (PCA), Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), and Support Vector Machine (SVM), were employed for spectral analysis and classification.

Main Results:

• The developed SERS method successfully identified and quantified antibiotic residues in wastewater solutions.
• Machine learning models demonstrated high accuracy in predicting antibiotic status, with SVM achieving up to 99% accuracy.
• PCA and OPLS-DA were effective for clustering SERS spectral datasets.

Conclusions:

• The GNRs@1,4-BDT@Ag nanoparticles combined with SERS and machine learning provide a robust tool for antibiotic detection in wastewater.
• This approach offers a simple, rapid, and accurate method for environmental monitoring.
• The technique shows potential for broader applications in analyzing contaminants in various domains.