- Title
- Microplastics and nanoplastics analysis: Options, imaging, advancements and challenges
- Creator
- Fang, Cheng; Luo, Yunlong; Naidu, Ravi
- Relation
- TrAC Trends in Analytical Chemistry Vol. 166, Issue September 2023, no. 117158
- Publisher Link
- http://dx.doi.org/10.1016/j.trac.2023.117158
- Publisher
- Elsevier
- Resource Type
- journal article
- Date
- 2023
- Description
- As emerging contaminants, microplastics and nanoplastics pose analytical challenges to the scientific community due to the small size, diverse composition and complex environmental background. The research on nanoplastics is far behind that on microplastics because the shrink size and the weak signal lead to more challenging analysis. Herein we review the recent advancements on their analysis including sampling, sample preparation, test and aftermath data analysis. We begin by clarifying the unique properties of microplastics and nanoplastics that complicate the sampling and the sample preparation processes, which have been rarely reported but should be emphasised for the subsequent analysis. For the analysis, there are various techniques for morphological and chemical characterisations, including microscope, element analysis, mass spectroscopy and molecular spectroscopy. They are compared herein to highlight their advantages and disadvantages. Because the microplastics and nanoplastics can have their own sub-structures, there might be bias if the non-imaging analysis is conducted, such as using a single spectrum analysis (point analysis) conducted at a selective position that is only a partial surface area of the whole structure (surface and bulk). Imaging analysis via micro-IR and micro-Raman spectroscopy particularly Raman imaging shows some advantages to overcome this bias. However, Raman imaging is a time-consuming process with a diffraction-limited resolution problem that is also discussed herein. Moreover, it is difficult to convert the scanning hyperspectral matrix to image. To address this, algorithms of chemometrics and artificial intelligent (AI) can be utilised to decode the hyperspectral matrix that acts as a big data, and re-construct the image towards deconvolution. The current analysis techniques should be either improved or combined for the emerging contaminants’ analysis. Overall, this review summarises the analysis challenges and advances, and also suggests the future research directions.
- Subject
- microplastics; nanoplastics; sample preparation; imaging analysis; algorithm; hyperspectral matrix
- Identifier
- http://hdl.handle.net/1959.13/1497938
- Identifier
- uon:54439
- Identifier
- ISSN:0165-9936
- Language
- eng
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