Investigating kitchen sponge-derived microplastics and nanoplastics with Raman imaging and multivariate analysis

Yunlong Luo, Fangjie Qi, Christopher T. Gibson, Yongjia Lei, Cheng Fang

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


Microplastics can be found almost everywhere, including in our kitchens. The challenge is how to characterise them, particularly for the small ones (<1 μm), referred to as nanoplastics, when they are mixed with larger particles and other components. Herewith we advance Raman imaging to characterise microplastics and nanoplastics released from a dish sponge that we use every day to clean our cookware and eating utensils. The scanning electron microscopy result shows significantly different structures of the soft and hard layers of the sponge, with the hard layer being more likely to shed particles. By scanning the sample surface to generate a spectrum matrix, Raman imaging can significantly improve signal-noise-ratio, compared with individual Raman spectra. Through mapping the characteristic peaks from the matrix that contains hundreds, even thousands of Raman spectra, it is confirmed that the particles released from the soft and hard layers of the sponge are mainly Nylon PA6 and polyethylene terephthalate, respectively. Using principal component analysis (PCA) to decode the spectrum matrix further enhances the signal-noise ratio, which enables mapping the whole set of the spectrum, rather than the selected peaks. By optimising the Raman scanning parameters, the PCA-Raman imaging is able to reliably capture and visualise microplastics and nanoplastics released from both sides of the dish sponge, including a plastic-surrounding-sand composite structure. Overall, PCA-Raman imaging is a holistic and effective approach to characterising miniature plastic particles.

Original languageEnglish
Article number153963
Number of pages9
JournalScience of The Total Environment
Early online date18 Feb 2022
Publication statusPublished - 10 Jun 2022


  • Additive
  • PCA
  • Pixel
  • Raman spectroscopy
  • Signal-to-noise ratio


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