Towards Raman Automation for Microplastics: Developing Strategies for Particle Adhesion and Filter Subsampling

Area of Science:

• Environmental Science
• Analytical Chemistry
• Materials Science

Background:

• Spectroscopic analysis of microplastics is time-consuming.
• Automation faces challenges with particle adherence and subsampling strategies.
• Accurate microplastic quantification is crucial for environmental monitoring.

Purpose of the Study:

• To address methodological challenges in automated microplastic analysis.
• To improve particle adhesion to filters during spectroscopic analysis.
• To develop effective subsampling strategies for microplastic quantification.

Main Methods:

• Application of Skin Tac, a permeable adhesive, to enhance particle adherence.
• Exploration of various subsampling strategies for filter mapping.
• Analysis of particle distribution on filters to assess homogeneity.

Main Results:

• Skin Tac effectively adheres microplastic particles without masking their Raman signal.
• Assuming homogenous particle distribution leads to inaccurate extrapolated counts.
• Non-uniform particle distribution was observed on filters.

Conclusions:

• Skin Tac offers a viable solution for particle adherence in microplastic analysis.
• Subsampling strategies must account for non-uniform particle distribution.
• Recommendations are provided for optimizing subsampling in future studies to increase throughput.