Identification of microplastic fibres released from COVID-19 test swabs with Raman imaging

Is paper bag plastic-free, without plastic in colourful logo area?

The concern over plastic contamination has led to bans on plastic shopping bags, often replaced by paper ones. However, logos painted or printed on paper bags may still contain plastics, as investigated herein. In some logos, for example, white pigment of titanium dioxide (TiO2) nanoparticles are bound with plastic binder onto the cellulose surface of the paper. This hybrid of plastic and nanoparticle is examined using scanning electron microscope (SEM) to characterise morphology physically, and Raman imaging to identify and visualise them chemically. Raman imaging scans the sample to separate images and identify not only plastic but also the co-formulated pigment. The scan generates a hyperspectral matrix containing hundreds to thousands of spectra, and subsequent analysis can enhance the signal-to-noise ratio. Decoding the hyperspectral matrix using chemometrics like principal component analysis (PCA) can effectively map plastic and pigment separately with increased certainty. The image can be further refined through 3-dimensional surface fitting for deconvolution, providing direct visualisation of the plastic-nanoparticle hybrid at a density of approximately 7.3 million particles per square millimetre. Overall, caution should be exercised when using paper bags, as they may not be entirely free of plastics. Raman imaging proves to be an effective method for identifying and visualising complex components, including plastics and nanoparticles. ENVIRONMENTAL IMPLICATION: The concern over plastic contamination has led to bans on plastic shopping bags, replaced by paper alternatives. However, some logos on paper bags may still contain plastics, which is investigated to confirm the presence of plastic-nanoparticle hybrid using SEM and Raman imaging. By employing decoding algorithms such as PCA to separately map plastic and pigment, and utilising 3D surface fitting to deconvolute the image, the hybrid plastic-nanoparticle is estimated at a density of approximately 7.3 million particles per square millimetre. It's important to exercise caution and not assume these items are plastic-free. This aspect of plastics may have been overlooked as another potential source of contamination.

How to Identify and Quantify Microplastics and Nanoplastics Using Raman Imaging?

While microplastics and nanoplastics are emerging as a big environmental concern, their characterization is still a challenge, particularly for identification and simultaneous quantification analysis where imaging via a hyper spectrum is generally needed. In the past few years, Raman imaging has been greatly advanced, but the analysis protocol is complicated and not yet standardized because imaging analysis is different from traditional analysis. Herein we provide a step-by-step demonstration of how to employ confocal Raman techniques to image microplastics and nanoplastics.

Characterising microplastics in indoor air: Insights from Raman imaging analysis of air filter samples

We are directly exposed to microplastic contamination via indoor air that we breathe daily, for which the characterisation of microplastics is still a challenge. Herein, two typical air filter samples were collected, one from an air-conditioner and another from a personal computer, both of which have been working for around half a year to collect and accumulate microplastics in the indoor air, like microplastic banks. After the sample preparation to remove the mineral dusts, Raman imaging was employed to directly and simultaneously identify and visualise microplastics of polyethylene terephthalate (PET) fibres, distinguish them from other fibres such as cellulose and cross-check them with a scanning electron microscope (SEM). To count the microplastics and to avoid the quantification bias, several areas were randomly scanned and imaged to statistically estimate the percentage of microplastic fibres in the analysed samples. The microplastics amount, which has been estimated at 73-88,000 fibers per filter per half a year, varies and depends on the indoor environment so that the air filter can work as a good indicator to monitor the quality of the indoor air from the microplastic perspective. Overall, human are directly exposed to this emerging contamination every day, raising environmental concerns. Raman imaging characterisation and its corresponding statistical information can help pursue further research on microplastics.

Advancements in Raman imaging for nanoplastic analysis: Challenges, algorithms and future Perspectives

BACKGROUND

While the concept of microplastic (<5 mm) is well-established, emergence of nanoplastics (<1000 nm) as a new contaminant presents a recent and evolving challenge. The field of nanoplastic research remains in its early stages, and its progress is contingent upon the development of reliable and practical analytical methods, which are currently lacking. This review aims to address the intricacies of nanoplastic analysis by providing a comprehensive overview on the application of advanced imaging techniques, with a particular focus on Raman imaging, for nanoplastic identification and simultaneous visualisation towards quantification.

RESULTS

Although Raman imaging via hyper spectrum is a potentially powerful tool to analyse nanoplastics, several challenges should be overcome. The first challenge lies in the weak Raman signal of nanoplastics. To address this, effective sample preparation and signal enhancement techniques can be implemented, such as by analysing the hyper spectrum that contains hundred-to-thousand spectra, rather than a single spectrum. Second challenge is the complexity of Raman hyperspectral matrix with dataset size at megabyte (MB) or even bigger, which can be adopted using different algorithms ranging from image merging to multivariate analysis of chemometrics. Third challenge is the laser size that hinders the visualisation of small nanoplastics due to the laser diffraction (λ/2NA, ∼300 nm), which can be solved with involving the use of super-resolution. Signal processing, such as colour off-setting, Gaussian fitting (via deconvolution), and re-focus or image re-construction, are reviewed herein, which show a great promise for breaking through the diffraction limit.

SIGNIFICANCE

Overall, current studies along with further validation are imperative to refine these approaches and enhance the reliability, not only for nanoplastics research but also for broader investigations in the realm of nanomaterials.

Microplastics in inland and offshore sediments in the Apulo-Lucanian region (Southern Italy)

Inland and offshore sediments from Southern Italy were studied in order to evaluate the occurrence and nature of microplastics (MPs). Inland sediments were collected in the Bradano and Basento rivers (Apulo-Lucanian region, Southern Italy), while offshore sediments were collected on the continental shelf near Bari (Adriatic Sea) and Metaponto (Ionian Sea). MPs were detected and characterized using optical microscopy, micro-Fourier-Transform Infrared spectroscopy (μ-FTIR) and micro-Raman analyses. The number of MPs present varied between 144 and 1246 kg-1 of dry sediment (468.8 ± 410,7 MPs kg-1) with a predominance of black fibers; no correlation emerged between MPs and sediment grain size. In river sediments, the occurrence of MPs is associated with local pollution, whereas the offshore occurrence of MPs depends on seasonal river flow and submarine canyons. Compositional analyses suggest that the main source of MPs in the studied sediments is sewage discharge from residential areas.

Super-resolution imaging of micro- and nanoplastics using confocal Raman with Gaussian surface fitting and deconvolution

Confocal Raman imaging can directly identify and visualise microplastics and even nanoplastics. However, due to diffraction, the excitation laser spot has a size, which defines the image resolution. Consequently, it is difficult to image nanoplastic that is smaller than the diffraction limit. Within the laser spot, fortunately, the excitation energy density behaves an axially transcended distribution, or a 2D Gaussian distribution. By mapping the emission intensity of Raman signal, the imaged nanoplastic pattern is axially transcended as well and can be fitted as a 2D Gaussian surface via deconvolution, to re-construct the Raman image. The image re-construction can intentionally and selectively pick up the weak signal of nanoplastics, average the background noise/the variation of the Raman intensity, smoothen the image surface and re-focus the mapped pattern towards signal enhancement. Using this approach, along with nanoplastics models with known size for validation, real samples are also tested to image microplastics and nanoplastics released from the bushfire-burned face masks and water tanks. Even the bushfire-deviated surface group can be visualised as well, to monitor the different degrees of burning by visualising micro- and nanoplastics. Overall, this approach can effectively image regular shape of micro- and nanoplastics, capture nanoplastics smaller than the diffraction limit, and realise super-resolution imaging via confocal Raman.