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

Microplastics and nanoplastics released from injection syringe, solid and liquid dimethylpolysiloxane (PDMS)

For a plastic syringe, a stopper at the end of plunger is usually made of polydimethylsiloxane (PDMS, and co-ingredients). To reduce friction and prevent leakage between the stopper and barrel, short chain polymer of liquid PDMS is also used as lubricant. Consequently, an injection process can release solid PDMS debris from the stopper and barrel, and liquid PDMS droplets from the lubricant, both of which are confirmed herein as solid and liquid micro(nano)plastics. From molecular spectrum perspective to directly visualise those micro(nano)plastics, Raman imaging was employed to analyse hundreds-to-thousands of spectra (hyper spectrum or hyperspectral matrix) and significantly enhance signal-to-noise ratio. From morphology perspective to provide high resolution of image, scanning electron microscopy (SEM) was engaged to cross-check with Raman images and increase assignment / quantification certainty. The weak Raman imaging signal of nanoplastics was extracted using image deconvolution algorithm to remove the background noise and average the signal variation. To increase the result's representativeness and avoid quantification bias, multiple syringes were tested and multiple areas were randomly scanned toward statistical results. It was estimated that thousands of microplastics and millions of nanoplastics of solid/liquid PDMS might be injected when using a plastic syringe of 1 mL. Overall, Raman imaging (along with algorithm and SEM) can be helpful for further research on micro(nano)plastics, and it should be cautious to use plastic syringe due to the increasing concern on the emerging contamination of not only solid but also liquid micro(nano)plastics.

Size-classifiable quantification of nanoplastic by rate zonal centrifugation coupled with pyrolysis-gas chromatography-mass spectrometry

Particle size is an important indicator to evaluate the environmental risk and biotoxicity of nanoplastic (NP, particle diameter <1000 nm). The methods available to determine size classes of NP in environmental samples are few and are rare to achieve efficient separation and recycling of NP with close particle sizes. Here, we show that rate-zonal centrifugation (RZC) can quickly and efficiently collect NP of different sizes based on their sedimentation coefficients. When combined with cloud-point extraction (CPE) and pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS), our method can quantify three NP particle-size classes separately (including 100 nm, 300 nm, and 600 nm) in aqueous samples with high recovery (81.4 %-89.4 %), limits of detections (LODs, 33.5-53.4 μg/L), and limits of quantifications (LOQs, 110.6-167.2 μg/L). Compared with the conventional sample pretreatment process, our method can effectively extract and determine the NP with different sizes. Our approach is highly scalable and can be effectively applied to NP in a wide range of aquatic environments. Meanwhile, our approach is highly scalable to incorporate diverse assays to study the environmental behaviours and ecological risks of NP.

An optimized multi-technique based analytical platform for identification, characterization and quantification of nanoplastics in water

Nanoplastics (NPs) have been identified as an emerging concern for the environment and our food chains in recent years. Monitoring the concentration and size of nanoplastics is essential to assess the potential risks that nanoplastic particles may pose. In this study, we presented a multi-technique based analytical platform to identify, characterize and quantify nanoplastics in water samples through a combination of sample pre-concentration, asymmetric flow field-flow fractionation coupled with multi-angle light scattering (AF4-MALS) and pyrolysis-GC/MS (Py-GC/MS). Models for predicting NPs concentration and particle number in unknown samples were established and validated using NPs standards of known size and AF4-MALS response. Py-GC/MS was applied for further identification of polymer type and quantification of mass concentration. Filtration conditions for pre-concentration were optimized to ensure a high recovery rate with minimal effect on original particle size. The addition of 0.05% SDS prior to filtration, using controlled filtration procedures, effectively improved the recovery. Furthermore, this study demonstrates the application of the analytical platform for the characterization and quantification of different nanoparticles (e.g. spiked PMMA and PS NPs) in the size range 60 nm-350 nm with detection limits down to 0.01 ppm in water samples. The established analytical platform can fill an analytical gap by offering a solution for quantifying size-resolved mass concentrations of nanoplastics and providing comprehensive data on size distribution, particle number and mass quantification with high sensitivity for detection.

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.

From celebration to contamination: Analysing microplastics released by burst balloons

Air balloons are a ubiquitous presence in our daily lives, and their rupture may release a substantial quantity of debris, as investigated herein. We employ Raman imaging to capture the fragments resulting from balloon explosions, enabling the identification and direct visualisation of minute microplastic particles / fragments with an improved signal-to-noise ratio for precise quantification. To circumvent the generation of misleading confocal Raman images, we recommend employing terrain mapping to scan the three-dimensional surface of the sample. It is important to acknowledge that the analysis of microplastics at the micro-scale inherently poses limitations in terms of throughput, as it necessitates a trade-off between low and high magnifications. We conduct explosive experiments on ten-to-hundred balloons, collecting debris from various angles and positions. Our investigation involves the random testing of multiple samples / sample positions at the micro-scale, with subsequent extrapolation to estimate the total amount of microplastics. The amalgamation of these results through statistical analysis indicates that each balloon explosion can potentially release tens-to-thousands of microplastics, highlighting a concern that has hitherto received insufficient attention. The characterisation approach, particularly the random Raman scanning method in combination with SEM and the statistical analysis on accumulated samples employed in this report, has the potential to serve as a useful tool in future research on microplastics and even nanoplastics.

Unveiling microplastics with hyperspectral Raman imaging: From macroscale observations to real-world applications

The widespread use of plastic materials, owing to their several advantageous properties, has resulted in a considerable increase in plastic consumption. Consequently, the production of primary and secondary microplastics has also increased. To identify, categorize, and quantify microplastics, several analytical methods, such as thermal analysis and spectroscopic methods, have been developed. They generally offer little insight into the size and shape of microplastics, require time-consuming sample preparation and classification, and are susceptible to background interference. Herein, we created a macroscale hyperspectral Raman method to quickly quantify and characterize large volumes of plastics. Using this approach, we successfully obtained Raman spectra of five different types of microplastics scattered over an area of 12.4 mm × 12.4 mm within just 550 s and perfectly classified these microplastics using a machine learning method. Additionally, we demonstrated that our system is effective for obtaining Raman spectra, even when the microplastics are suspended in aquatic environments or bound to metal-mesh nets. These results highlight the considerable potential of our proposed method for real-world applications.

Unveiling microplastics from zippers: Characterisation and visualisation through Raman imaging analysis

Microplastics have emerged as a global concern due to the increased plastic contamination found in a variety of sources. Herein we unveil microplastics released from plastic zippers that can generally be found in our clothes and textiles. We first employ a scanning electron microscope (SEM) to visualise the scratches developed on the zipper teeth and the derived particles. We then use Raman imaging to identify and simultaneously visualise the plastics from the chemical or molecular spectrum window. Based on hundreds to thousands of spectra, rather than a single spectrum or even a single peak that works as just a pixel in the image, imaging analysis can significantly increase the signal-to-noise ratio. Furthermore, the non-uniform distribution of components or multi-components can also be effectively imaged to avoid the possible bias from the single-spectrum analysis. The challenge to convert the hundreds to thousands of spectra of a hyperspectral matrix to an image is also discussed, and chemometrics is adopted and recommended to further improve the signal-to-noise ratio. The co-ingredient of titanium oxide in the zipper teeth/sewing lines is also effectively identified by Raman imaging. Based on the effective characterisation, we estimate that up to ~410 microplastics could be potentially released during each time of on-off zipping, although the variation can be expected and depends on several other factors. This study reminds us to be aware of the potential contamination derived from similar types of microplastic sources in our daily lives.