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Related Experiment Video

Updated: Jul 10, 2026

Microembossing: A Convenient Process for Fabricating Microchannels on Nanocellulose Paper-Based Microfluidics
03:58

Microembossing: A Convenient Process for Fabricating Microchannels on Nanocellulose Paper-Based Microfluidics

Published on: October 6, 2023

Integrated SERS and Machine Learning Workflow for Nanoplastic Detection on a Plasmonic Membrane.

Amauri Horta-Velázquez1,2, Erika Rodríguez-Sevilla1, Angelica Hernandez-Rayas3

  • 1Centro de Investigaciones en Óptica (CIO), A. C., Loma del Bosque 115, Lomas del Campestre, León 37150, Guanajuato, Mexico.

Analytical Chemistry
|July 8, 2026
PubMed
Summary

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We developed a new method using a SERS-active membrane and machine learning to automatically detect nanoplastics in water. This approach offers accurate, user-friendly monitoring for environmental health.

Area of Science:

  • Environmental Science
  • Analytical Chemistry
  • Materials Science

Background:

  • Nanoplastics present growing health concerns, demanding sensitive detection methods.
  • Surface-enhanced Raman spectroscopy (SERS) is sensitive but struggles with complex data and large analytes like nanoplastics.
  • Current methods lack automated interpretation and field deployability for nanoplastic monitoring.

Purpose of the Study:

  • To develop an automated framework for sensitive and reliable nanoplastic detection.
  • To integrate a SERS-active membrane with a machine learning pipeline for simplified analysis.
  • To enable user-friendly and interpretable semiquantitative detection of nanoplastics.

Main Methods:

  • Fabrication of a SERS-active membrane using gold nanorod-functionalized nanopaper.

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Last Updated: Jul 10, 2026

Microembossing: A Convenient Process for Fabricating Microchannels on Nanocellulose Paper-Based Microfluidics
03:58

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Published on: October 6, 2023

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09:13

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Published on: April 4, 2017

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09:10

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  • Development of a machine learning pipeline (PCA, Isolation Forests, K-means, ExtraTrees classifier) for automated data analysis.
  • Integration of an interpretability algorithm to validate machine learning predictions.

    • Main Results:

    • The SERS-active membrane efficiently collected and concentrated PMMA nanoplastics.
    • The automated machine learning pipeline achieved 95% accuracy in nanoplastic detection.
    • The workflow provided chemically validated, interpretable results with a limit of detection of 0.02 μg mL⁻¹.

    Conclusions:

    • This integrated SERS membrane and machine learning workflow streamlines nanoplastic detection and data interpretation.
    • The developed method offers a feasible pathway for user-friendly, field-deployable nanoplastic monitoring.
    • This advancement supports improved environmental surveillance and risk assessment of nanoplastics.