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Towards Raman Automation for Microplastics: Developing Strategies for Particle Adhesion and Filter Subsampling
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This paper addresses two practical barriers to automating microplastic identification by Raman spectroscopy: keeping particles stuck to filters during analysis and developing efficient subsampling strategies. The researchers found that a skin adhesive called Skin Tac can keep particles in place, enabling automated scanning of large filter areas.
Automation and subsampling have been proposed as solutions to reduce the time required to quantify and characterize microplastics in samples using spectroscopy. However, there are methodological dilemmas associated with automation that are preventing its widespread implementation including ensuring particles stay adhered to the filter during filter mapping and developing an appropriate subsampling strategy to reduce the time needed for analysis. We provide a solution to the particle adherence issue by applying Skin Tac, a non-polymeric permeable adhesive that allows microplastic particles to adhere to the filter without having their Raman signal masked by the adhesive. We also explore different subsampling strategies to help inform how to take a representative subsample. Based on the particle distributions observed on filters, we determined that assuming a homogenous particle distribution is inappropriate and can lead to over- and under-estimations of extrapolated particle counts. Instead, we provide recommendations for future studies that wish to subsample to increase the throughput of samples for spectroscopic analysis.
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