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The characterisation of microplastics is still difficult, and the challenge is even greater for nanoplastics. A possible source of these particles is the scratched surface of a non-stick cooking pot that is mainly coated with Teflon. Herein we employ Raman imaging to scan the surfaces of different non-stick pots and collect spectra as spectrum matrices, akin to a hyperspectral imaging process. We adjust and optimise different algorithms and create a new hybrid algorithm to extract the extremely weak signal of Teflon microplastics and particularly nanoplastics. We use multiple characteristic peaks of Teflon to create several images, and merge them to one, using a logic-based algorithm (i), in order to cross-check them and to increase the signal-noise ratio. To differentiate the varied peak heights towards image merging, an algebra-based algorithm (ii) is developed to process different images with weighting factors. To map the images via the whole set of the spectrum (not just from the individual characteristic peaks), a principal component analysis (PCA)-based algorithm (iii) is employed to orthogonally decode the spectrum matrix to the PCA spectrum and PCA intensity image. To effectively extract the Teflon spectrum information, a new hybrid algorithm is developed to justify the PCA spectra and merge the PCA intensity images with the algebra-based algorithm (PCA/algebra-based algorithm) (iv). Based on these developments and with the help of SEM, we estimate that thousands to millions of Teflon microplastics and nanoplastics might be released during a mimic cooking process. Overall, it is recommended that Raman imaging, along with the signal recognition algorithms, be combined with SEM to characterise and quantify microplastics and nanoplastics., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

As contaminants of emerging concern, microplastics and nanoplastics are ubiquitous in not only aquatic and terrestrial environments but also household settings. While the characterisation of microplastics is still a challenge, the analysis of nanoplastics is even more difficult. In this study, we aim to examine several novel algorithmic methods intended for analysing complex Raman spectrum matrices towards visualisation of plastic particles released from a chopping board. Specifically, we compare and advance three decoding algorithms, including (i) a logic-based algorithm to merge and cross-check multiple Raman images that map the intensities of several characteristic peaks; (ii) a principal component analysis-based algorithm to generate intensity images from whole sets of spectra, not just from individual characteristic peaks; (iii) an algebra-based algorithm to merge and cross-check the loading matrix to enhance characterisation efficiency. Assisted with a scanning electron microscope, we estimate that 100-300 microplastics / nanoplastics per mm per cut along the groove formed on the chopping board, and ~3000 per mm 2 per cut in the scratched area, may be released from a chopping board during food preparation and may be subsequently ingested by human. Overall, the Raman imaging combined with algorithms can provide effective characterisation of microplastics and nanoplastics., (Copyright © 2022 Elsevier B.V. All rights reserved.)

The characterisation of nanoplastics is much more difficult than that of microplastics. Herewith we employ Raman imaging to capture and visualise nanoplastics and microplastics, due to the increased signal-noise ratio from Raman spectrum matrix when compared with that from a single spectrum. The images mapping multiple characteristic peaks can be merged into one using logic-based algorithm, in order to cross-check these images and to further increase the signal-noise ratio. We demonstrate how to capture and identify microplastics, and then zoom down gradually to visualise nanoplastics, in order to avoid the shielding effect of the microplastics to shadow and obscure the nanoplastics. We also carefully compare the advantages and disadvantages of Raman imaging, while giving recommendations for improvement. We validate our approach to capture the microplastics and nanoplastics as particles released when we cut and assemble PVC pipes in our garden. We estimate that, during a cutting process of the PVC pipe, thousands of microplastics in the range of 0.1-5 mm can be released, along with millions of small microplastics in the range of 1-100 μm, and billions of nanoplastics in the range of <1 μm. Overall, Raman imaging can effectively capture microplastics and nanoplastics., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

The characterisation of microplastics is still a challenge, and the challenge is even greater for nanoplastics, of which we only have a limited knowledge so far. Herewith we employ Raman imaging to directly visualise microplastics and nanoplastics which are released from the trimmer lines during lawn mowing. The signal-noise ratio of Raman imaging is significantly increased by generating an image from hundreds or thousands of Raman spectra, rather than from a single spectrum, and is further increased by combining with the logic-based and PCA-based algorithms. The increased signal-noise ratio enables us to capture and identify microplastics and particularly nanoplastics, including plastic fragments or shreds (with diameters / widths of 80 nm - 3 µm) and nanoparticles (with diameters of < 1000 nm) that are released during the mimicked mowing process. Using Raman imaging, we estimate that thousands of microplastics (0.1-5 mm), and billions of nanoplastics (< 1000 nm), are released per minute when a line trimmer is used to mow lawn. Overall, Raman imaging provides effective characterisation of the microplastics and is particularly suitable for nanoplastics., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Microplastics can potentially be released in our daily activities, such as via our showers, as our clothes are made of plastic fibres, and/or cotton fibres. The challenge is how to characterise these microplastics in shower debris. Herewith we employ Raman imaging to directly visualise the microplastics collected from shower wastewater. Raman can map an image from the scanning array that contains a matrix of thousands of spectra, featuring a considerably higher signal-noise ratio than that from a single spectrum. The increased signal-noise ratio reduces the complexity of sample preparation. Consequently, after the shower debris was sampled and washed, Raman imaging allowed us to distinguish the microplastic fibres from the background including cotton fibres and dirt aggregates. Interestingly, by adjusting the laser power intensity, the scanning process enabled simultaneous in-situ bleaching of the colorants formulated in the textile fibres and collection of signals. The disadvantage of Raman imaging such as the short focusing/working distance is also presented and discussed. Overall, the Raman imaging can extract meaningful information from the complex shower debris samples to enable analysis of microplastics., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Microplastics are among the ubiquitous contaminants in our environment. As emerging contaminants, microplastics are still facing with lots of challenges on the characterisation, including their capture, identification and visualisation, particularly from a complex background. For example, when we print documents using a laser printer, we are printing microplastics onto paper, because the plastics are the main ingredient of the toner powder mixture. Characterisation of these microplastic mixture meets an even more complicated challenge, because plastic's signals might be shielded by other toner powder ingredients such as the pigments, the dyes, the black carbon, and the paper fabrics as well. To solve this challenge, we employ various techniques, including SEM, TEM, XPS, FT-IR, TGA and Raman, to characterise the microplastics printed via the toner powders. Interestingly, we show that Raman can distinguish and visualise the distribution of the microplastics from the complex background of the mixture. We estimate the millions of toner powders, each of which is ~4-6 μm in size, are printed out per A4 sheet as microplastics. The findings send a strong warning that millions of microplastics might be generated from the printing activities in our daily lives., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Millions of tonnes of plastics have been released into the environment. Although the risk of plastics to humans is not yet resolved, microplastics, in the range of 1 μm - 5 mm, have entered our bodies, originating either from ingestion via the food chain or from inhalation of air. Generally there are two sources of microplastics, either directly from industry, such as cosmetic exfoliants, or indirectly from physical, chemical and biological fragmentation of large (>5 mm) plastic residues. We have found that microplastics can be generated by simple tasks in our daily lives such as by scissoring with scissors, tearing with hands, cutting with knives or twisting manually, to open plastics containers/bags/tapes/caps. These processes can generate about 0.46–250 microplastic/cm. This amount is dependent on the conditions such as stiffness, thickness, anisotropy, the density of plastic materials and the size of microplastics.This finding sends an important warning, that we must be careful when opening plastic packaging, if we are concerned about microplastics and care about reducing microplastics contamination. [ABSTRACT FROM AUTHOR]

Raman imaging can effectively characterise microplastics and nanoplastics, which is validated here to capture the items released from the plastic gloves when subjected to a mimicked fire. During the COVID-19 pandemic, large quantities of personal protective equipment (PPE) units have been used, such as the disposable gloves. If discarded and poorly managed, plastics gloves might break down to release secondary contaminants. The breakdown process can be accelerated by burning in a bushfire or at the incineration plants. During the burning process, the functional groups on the surface can be burned differently due to their different thermal stabilities. The different degrees of burning can be distinguished and visualised via Raman imaging. In the meantime, at the bottom of the burned plastics, microplastics and nanoplastics can be generated at a significant amount. The possible false Raman imaging on microplastics and nanoplastics is also discussed, by effectively extracting and distinguishing the weak signal from the background or noise. Overall, these findings confirm the importance of effectively working waste incineration plants and litter prevention, and suggest that Raman imaging is a suitable approach to characterise microplastics and nanoplastics. [Display omitted] • Burning disposable gloves generates molten plastic films that are easily breakable. • Molten plastics might be peeled off and released as microplastics and nanoplastics. • The burning process alters surface functional groups on the plastic surface. • Varied degrees of burning might occur at different surface locations. • It is estimated 0.54 billion/cm2 nanoplastics can be released from a burned glove. [ABSTRACT FROM AUTHOR]