Super-resolution Raman imaging towards visualisation of nanoplastics

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.

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.

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.