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Machine learning-driven optical microfiltration device for improved nanoplastic sampling and detection in water

Liyuan Gong1, Bryan Varela1, Erfan Eskandari1

  • 1Department of Mechanical, Industrial and Systems Engineering, College of Engineering, University of Rhode Island, Kingston, RI 02881, United States.

Journal of Hazardous Materials
|May 4, 2025
PubMed
Summary
This summary is machine-generated.

A new agarose microfiltration device effectively captures and identifies nanoplastics in water using machine learning-assisted Raman spectroscopy. This innovative method enhances detection sensitivity and reduces analysis time for environmental and health monitoring.

Keywords:
Agarose microfiltrationConvolutional neural network (CNN)Environmental detectionNanoplasticsRaman mapping analysis

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Area of Science:

  • Environmental Science
  • Analytical Chemistry
  • Materials Science

Background:

  • Nanoplastics in aquatic environments pose significant ecological and health risks.
  • Current nanoplastic detection methods are often slow, labor-intensive, and lack sensitivity.
  • Challenges include nanoplastic size, complex chemistry, and environmental interference.

Purpose of the Study:

  • To develop a novel, efficient, and sensitive method for nanoplastic capture and identification in water.
  • To integrate microfluidics with machine learning-assisted Raman spectroscopy for improved nanoplastic analysis.
  • To address limitations of traditional nanoplastic detection techniques.

Main Methods:

  • An agarose-based microfluidic device with micropost arrays for dual filtration and preconcentration of nanoplastics.
  • Dehydration of the agarose matrix to form a transparent film for enhanced Raman spectroscopy compatibility.
  • Application of a convolutional neural network (CNN) for accelerated spectral analysis and identification.
  • Testing with 100-nm polystyrene nanoparticles (PSNPs) in distilled water and seawater across various concentrations and flow rates.

Main Results:

  • The device achieved high nanoplastic capture efficiencies: up to 80% in seawater and 66% in distilled water at a flow rate of 2.5 µL/min.
  • The integrated CNN reduced spectral mapping time by 50% and enabled PSNP detection at low concentrations (6.25 µg/mL) in seawater.
  • The dehydrated agarose film minimized background interference, enhancing Raman signal clarity and sensitivity.

Conclusions:

  • The agarose-based microfiltration system offers a scalable, cost-effective solution for nanoplastic sampling and analysis.
  • Combining microfluidics with machine learning-assisted Raman spectroscopy significantly improves nanoplastic detection capabilities.
  • This approach holds promise for addressing critical environmental monitoring and public health challenges related to nanoplastic pollution.