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A Filter-based Surface Enhanced Raman Spectroscopic Assay for Rapid Detection of Chemical Contaminants

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Published on: February 19, 2016

Machine Learning-Assisted 3D Envelope-Enhanced SERS Platform for Detection and Classification of Multiple Coexisting

Sisi Tang¹, Hongbo Yu¹, Shuting Huang¹

¹College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, China.

Analytical Chemistry

|June 25, 2026

Summary

This summary is machine-generated.

This study introduces a novel 3D envelope-enhanced SERS platform for detecting coexisting antibiotic pollutants on plastics. The method combines surface-enhanced Raman spectroscopy (SERS) with machine learning (ML) for accurate identification and quantification.

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Area of Science:

Environmental Science
Analytical Chemistry
Materials Science

Background:

Antibiotics adsorb onto plastic surfaces, forming coexisting pollutants with amplified environmental risks.
Existing detection methods struggle with complex mixtures and low concentrations of these pollutants.

Purpose of the Study:

To develop a sensitive and accurate platform for detecting and quantifying coexisting antibiotic pollutants on plastic surfaces.
To integrate surface-enhanced Raman spectroscopy (SERS) with machine learning (ML) for enhanced pollutant analysis.

Main Methods:

A three-dimensional envelope-enhanced SERS (3D EES) platform was created using three-phase interface self-assembly.
Au@Ag plasmonic nanoparticles were organized into a hydrophobic array substrate to generate plasmonic hot spots.
Machine learning models (SNE-SVM and RF-SVM) were trained on a comprehensive SERS dataset.

Main Results:

The 3D EES strategy achieved sensitive detection of antibiotics adsorbed on polystyrene (PS) down to 250 ng/L.
Machine learning models demonstrated high accuracy (98.07%) and Jaccard similarity (96.44%) in classifying antibiotic species and predicting adsorption levels.
The method effectively amplified SERS signals through coconcentration and additional hot spot generation.

Conclusions:

The developed 3D EES platform offers a robust tool for identifying coexisting pollutants in environmental samples.
This approach provides new avenues for tracing pollutant sources and understanding their environmental fate.
The integration of SERS and ML significantly enhances the analysis of complex environmental pollutant mixtures.