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Article ? AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button. Tier 2 ? Original research — experimental, observational, or case-control study. Direct primary evidence. Detection Methods Environmental Sources Sign in to save

Identification of common textile microplastics via autofluorescence spectroscopy coupled with k-means cluster analysis

The Analyst 2024 6 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count.
Marcus A. Johns, Hongying Zhao, Mike Gattrell, James Lockhart, Emily D. Cranston

Summary

This research demonstrated that autofluorescence spectroscopy combined with k-means cluster analysis can successfully identify common textile polymer types in greywater from laundry, using a minimal input dataset. The method accounts for the effects of photooxidation and dyes on spectral signatures, offering a practical approach for microplastic fiber identification.

This research proposes autofluorescence spectroscopy for the successful identification of common polymers present in greywater from a minimal input data set. The effects of photooxidation and dyes are also considered.

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