Identification and visualisation of microplastics/ nanoplastics by Raman imaging (ii): Smaller than the diffraction limit of laser?

Cheng Fang, Zahra Sobhani, Xian Zhang, Christopher T. Gibson, Youhong Tang, Ravi Naidu

Research output: Contribution to journalArticlepeer-review

Abstract

We recently reported (Sobhani et al., 2020) that when a confocal Raman microscope imaged a nanoplastic with the diameter of 100 nm, the imaging lateral size was 300–400 nm, due to the diffraction limit of the laser spot. In this study, we examine the lateral intensity distribution of the Raman signal emitted by nanoplastics (diameters ranging ∼30–600 nm) within the excitation laser spot. We find that the Raman emission intensity, similar to the excitation power density distributed within a laser spot, also follows a lateral Gaussian distribution. To image and visualise individual nanoplastics, we (i) decrease the mapping pixel size, in a hope to generate an image with high-resolution and simultaneously to pick up items from the “blind point”. We can then either (ii) offset the colour to intentionally image only the high-intensity portion of the Raman signal (emitted from the centre of the laser spot), to localise the exact position of the nanoplastic; or (iii) categorise the imaged nanoplastics to different groups via their Raman intensity, to simultaneously and separately visualise large nanoplastics/strong Raman signals, medium nanoplastics and small nanoplastics, in an effort to avoid the shielding and overlooking of weak signals. We (iv) also cross-check multi-images simultaneously mapped at two or three characteristic peaks via either a logic-OR or a logic-AND algorithm. Thus the imaging uncertainty can be significantly reduced from a statistical point of view.

Original languageEnglish
Article number116046
Number of pages11
JournalWater Research
Volume183
DOIs
Publication statusPublished - 15 Sep 2020

Keywords

  • Car paint-polishing dust
  • Environmental contamination
  • Microplastic
  • Nanoplastic
  • Pixel size
  • Raman mapping image

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