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Machine Learning-Enhanced SERS Sensor Using Microgroove Structures for Enriching and Confining Nanoplastics in

Zilong Yan1, Maofeng Zhang1, Xue Chen1

  • 1School of Chemistry and Chemical Engineering, Hefei University of Technology, Hefei 230009, China.

Analytical Chemistry
|May 30, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel microcavity sensor that precisely detects and localizes trace nanoplastics (NPs) in real-world samples. The innovative design enhances detection limits and enables accurate classification of NPs using machine learning.

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Area of Science:

  • Materials Science and Engineering
  • Environmental Science
  • Analytical Chemistry

Background:

  • Trace detection of nanoplastics (NPs) is challenging due to random deposition and poor contact with detection hotspots.
  • Precise localization of enriched analytes is a significant hurdle for on-site environmental monitoring.

Purpose of the Study:

  • To develop a sensor for sensitive and precise detection and localization of nanoplastics.
  • To overcome limitations in trace analyte deposition and hotspot contact for enhanced sensing capabilities.

Main Methods:

  • Fabrication of a 3D Ag/In2O3/fluorine-doped tin oxide microcavity array with microgrooves.
  • Utilizing microgroove pinning effect to control analyte deposition and direct enrichment.
  • Employing surface-enhanced Raman spectroscopy (SERS) combined with machine learning (ML) for NP classification.

Main Results:

  • Achieved a limit of detection (LOD) of 2.4 × 10^-13 M for 4-MBA with excellent spatial uniformity (RSD = 7.55%).
  • Detected various nanoplastics (PS, PMMA, PET) with an LOD of 20 ng/mL for 100 nm PS.
  • Demonstrated practical applicability with LODs of 360 ng/mL in river water and 2.94 μg/g in fish matrix for PS.
  • Achieved high classification accuracy (100% in river water, 99% in fish matrix) using CNN with a small dataset.

Conclusions:

  • The microgroove-confined enrichment and 3D hotspot design enable precise localization and trace detection of nanoplastics.
  • Small-dataset machine learning significantly enhances the accuracy of nanoplastic classification in complex environmental matrices.
  • This integrated approach offers a field-ready solution for detecting trace pollutants in real-world scenarios.