High-throughput Raman platform for microplastics detection on filtration membranes.

Area of Science:

• Environmental Science
• Analytical Chemistry
• Materials Science

Background:

• Microplastic contamination in aquatic environments is a growing global concern.
• Diverse plastic types present significant analytical challenges for current detection methods.
• Existing techniques lack the high-throughput capability for comprehensive microplastic analysis.

Purpose of the Study:

• To develop an integrated line-scan Raman imaging platform for rapid, high-throughput microplastic analysis.
• To enable efficient acquisition of high-resolution spectral and spatial data for microplastics.
• To overcome challenges in analyzing microplastics on complex filter surfaces.

Main Methods:

• Combined mosaic scanning Raman spectroscopy and optical microscopy.
• Integrated deep learning algorithms for automated particle segmentation and classification.
• Developed a system for complete filter analysis within one hour.

Main Results:

• The platform successfully detects microplastics ≥10 μm on complex filter surfaces.
• Automated classification and quantification of microplastic types and size distributions achieved high statistical reliability.
• Complete filter analysis, including measurement and processing, was performed in under one hour.

Conclusions:

• The proposed scalable, AI-assisted system offers a substantial advancement for microplastic monitoring.
• Provides a robust foundation for real-time, quantitative microplastic analysis in industrial and environmental applications.
• Enables efficient, high-throughput chemical and morphological characterization of microplastics.