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.

Dual-Principal Component Analysis of the Raman Spectrum Matrix to Automatically Identify and Visualize Microplastics and Nanoplastics.

As emerging contaminants, microplastics are challenging to characterize, particularly when their size is at the nanoscale. While imaging technology has received increasing attention recently, such as Raman imaging, decoding the scanning spectrum matrix can be difficult to achieve result digitally and automatically via software and usually requires the involvement of personal experience and expertise. Herewith, we show a dual-principal component analysis (PCA) approach, where (i) the first round of PCA analysis focuses on the raw spectrum data from the Raman scanning matrix and generates two new matrices, with one containing the spectrum profile to yield the PCA spectrum and the other containing the PCA intensity to be mapped as an image; (ii) the second round of PCA analysis merges the spectrum from the first round of PCA with the standard spectra of eight common plastics, to generate a correlation matrix. From the correlation value, we can digitally assign the principal components from the first round of PCA analysis to the plastics toward imaging, akin to dataset indexing. We also demonstrate the effect of the data pretreatment and the wavenumber variations. Overall, this dual-PCA approach paves the way for machine learning to analyze microplastics and particularly nanoplastics.

Raman imaging combined with an improved PCA/algebra-based algorithm to capture microplastics and nanoplastics.

Raman imaging has advanced recently to be able to directly visualise microplastics and even nanoplastics. However, the generated scanning spectrum matrix, akin to a hyperspectral matrix, is challenging to decode. To this end, herein, logic-based, algebra-based, principal component analysis (PCA)-based, and dual-PCA-based algorithms are compared and combined as a PCA/algebra-based algorithm. Specifically, (i) to increase the signal-noise ratio, multiple images that mapped the multiple characteristic peaks of plastics are merged to cross-check each other. The threshold-based logic algorithm is improved by differentiating the intrinsic peak intensity, using an algebra-based algorithm; (ii) PCA can decode the spectrum matrix to generate PCA spectra and PCA images. The PCA spectra can suggest how to further extract information on plastics to merge the corresponding PCA images to enable the capture of microplastics and nanoplastics, by combining and following up with the algebra-based algorithm, called a PCA/algebra-based algorithm; (iii) dual-PCA analysis is employed to guide and extract the multiple PCA spectra towards image merging, to validate the PCA/algebra-based algorithm. We also show that the score percentages of the eigenvalues of PCA can be used to estimate the size amount of the microplastics and nanoplastics in the scanning area, and how to treat the reversed peaks of the PCA spectrum. Overall, the improvement of the algorithms can lead to more effective decoding of the spectrum matrix.

Comparison between permanganate pre-oxidation and persulfate/iron(II) enhanced coagulation as pretreatment for ceramic membrane ultrafiltration of surface water contaminated with manganese and algae.

Concurrent presence of algae and manganese (Mn) in water poses a significant challenge for water treatment. This study compared the treatment efficiency of Mn-containing and algae-laden water using either permanganate pre-oxidation (KMnO4) or persulfate/iron(II) (PMS/Fe2+) enhanced coagulation as pretreatment for ceramic membrane ultrafiltration. The results showed that KMnO4 pre-oxidation achieved a slightly more effective Mn removal, and was almost unaffected by the initial dissolved organic carbon (DOC) concentrations. PMS/Fe2+ removed UV254 more efficiently (above 90% at a dose of 0.25 mmol/L), compared with KMnO4 (less than 60% UV254 removal). According to X-ray photoelectron spectroscopy (XPS) analysis of aggregates, both KMnO4 and Fe2+/PMS oxidation resulted in the formation of MnO2 precipitate. Electron paramagnetic resonance(EPR) analysis demonstrated that only the reactors dosed with PMS/Fe2+ were able to generate the highly reactive hydroxyl radical(·OH). The production of ·OH caused significant rupture of algal cells and thus higher algal removal compared to the treatment with KMnO4 (whereby insignificant cell breakage was observed). The cell rupture resulted in higher amounts of organic matter released in the systems containing PMS/Fe2+, as demonstrated by excitation-emission matrix (EEM) and protein analysis. Despite the elevated level of organic matter, adding PMS/Fe2+ was found to notably mitigate membrane fouling due to the formation of large flocs (311-533 µm) as well as the elimination of major ceramic membrane foulants, i.e. humic substances.

Toy building bricks as a potential source of microplastics and nanoplastics.

Microplastics and nanoplastics have become noteworthy contaminants, affecting not only outdoor ecosystems but also making a notable impact within indoor environments. The release of microplastics and nanoplastics from commonly used plastic items remains a concern, and the characterisation of these contaminants is still challenging. This study focused on evaluating the microplastics and nanoplastics produced from plastic building bricks. Using Raman spectroscopy and correlation analysis, the plastic material used to manufacture building blocks was determined to be either acrylonitrile butadiene styrene (correlation value of 0.77) or polycarbonate (correlation value of 0.96). A principal component analysis (PCA) algorithm was optimised for improved detection of the debris particles released. Some challenges in microplastic analysis, such as the interference from the colourants in the building block materials, was explored and discussed. Combining Raman results with scanning electron microscopy - energy-dispersive X-ray spectroscopy, we found the scratches on the building blocks to be a significant source of contamination, estimated several thousand microplastics and several hundred thousand nanoplastics were generated per mm2 following simulated play activities. The potential exposure to microplastics and nanoplastics during play poses risks associated with the ingestion and inhalation of these minute plastic particles.

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.

Ultrasonic degradation of per-and polyfluoroalkyl substances (PFAS), aqueous film-forming foam (AFFF) and foam fractionate (FF).

The ubiquitousness of per- and polyfluoroalkyl substances (PFAS) is a big concern and PFAS remediation is urgently needed such as via degradation. While previous studies have explored ultrasonic degradation of PFAS, work evaluating the operational parameters is rare, especially concerning real wastes such as aqueous film-forming foam (AFFF) and foam fractionate (FF). This study investigates the key operational parameters affecting the degradation efficiency of PFAS, encompassing ultrasonication frequency (580-1144 kHz), power intensity (125-187.5 W), initial concentration (0.08-40 ppm), treatment duration (0.5-3 h), sample volume (100-500 mL), and PFAS structure (perfluorooctanoic acid or PFOA; perfluorooctane sulfonate or PFOS; 6:2 fluorotelomer sulfonate or 6:2 FTS). The defluorination kinetics is different from the removal/degradation kinetics due to the generation of degradation intermediates, suggesting the complex degradation mechanism, which should be evaluated to close the mass balance effectively. Notably, the optimised ultrasonic system achieves ∼125%/∼115% defluorination in AFFF/FF example wastes (compared to ∼65%/∼97% removal) despite their complex composition and the involvement of total oxidizable precursor (TOP) assay. In the meantime, a few new PFAS are detected in the post-treatments, including perfluorohexane sulfonic acid (PFHxS) and 10:2 fluorotelomer sulfonate (10:2 FTS) in the AFFF, and perfluorooctane sulfonamide (FOSA) and 8:2 fluorotelomer sulfonate (8:2 FTS) in the FF, again suggesting the complex degradation mechanism. Overall, ultrasonication is effective to degrade PFAS real example wastes, advancing its potential for scale-up applications.

Accelerated transformation of plastic furniture into microplastics and nanoplastics by fire.

Numerous plastic items are known to gradually degrade and release microplastics and nanoplastics under certain conditions, which can be significantly accelerated by fire combustion. Unfortunately there is a limited knowledge about this burning process because the characterisation on microplastics and nanoplastics is still a challenge. In this study, an outdoor plastic chair is subjected to a combustion process, the change in the surface functional groups (due to different degree of burning) and the release of microplastics and nanoplastics are investigated. During the combustion process, the plastic is molten, burned and deposited on solid surfaces including concrete, stone and glass. Scanning electron microscopy (SEM) results show that the peeling off the deposited plastic generates a large number of fragments. Through Raman imaging, these fragments are characterised as polypropylene (PP) microplastics and nanoplastics due to appearance of characteristic peaks. To further increase the sensitivity, several algorithms are tested and optimised, including logic-based, non-supervised principal component analysis (PCA)-based, algebra-based and their hybrids (to intentionally correct the non-supervised PCA) to enable the effective extraction of the key information towards plastics characterisation, particularly by distinguishing the signal from the background noise towards the visualisation of the different degrees of burning. Based on the findings from Raman imaging and SEM, it is estimated that tens of microplastics and nanoplastics are created per µm2. Overall Raman imaging can be a suitable approach to characterise the microplastics and nanoplastics in a complex background, such as the fire-burned plastic items.

Raman imaging to capture microplastics and nanoplastics carried by smartphones.

The sources of microplastics and nanoplastics can be found almost everywhere, including being released from the activities of our daily lives. Unfortunately, the process for determining the sources of microplastics and nanoplastics is hampered by the limited techniques available for characterisation. Herewith, we advance Raman imaging by combining it with logic-based, algebra-based, PCA-based algorithms and their hybrid, which can significantly increase the signal-noise ratio and the imaging certainty, to enable the characterisation of microplastics. Consequently, we can capture and identify the microplastics carried by our smartphones. That is because, due to the friction and fragmentation etc., our clothes and the decoration trinkets that might be made of plastic fibres can release microplastics. The released microplastics stick on the phone surface, or are trapped in the charging port, speaker ports etc., towards accumulation. We estimate hundreds or thousands of microplastics can be captured and carried by a smartphone, depending on the clothing materials, pocketing styles, user habits etc. Due to the complexity of the samples (which shields the weak signals emitted from nanoplastics), further methodological improvements are required, such as optimisation of sample preparation (for better isolation of nano-sized plastics), refinement of data processing algorithms and combined use of Raman microscopy and scanning electron microscopy (SEM).

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.

Super-resolution Raman imaging towards visualisation of nanoplastics.

Confocal Raman imaging can potentially identify and visualise microplastics and nanoplastics, but the imaging lateral resolution is hampered by the diffraction of the laser, making it difficult to analyse nanoplastics that are smaller than the laser spot and the lateral resolution limit (λ/2NA). Fortunately, once a nanoplastic is scanned to collect the spectrum via a position/pixel array as a spectrum matrix, akin to a hyperspectral matrix, the nanoplastic can be imaged by mapping the spectrum intensity as a pattern that is transcended axially and can be fitted as a 2D Gaussian surface. The Gaussian fitting and image re-construction by deconvolution can precisely predict the nanoplastic's position and approximate size, and potentially enhance the signal intensity. Several algorithms are also employed to decode the spectrum matrix, to improve the Raman images before the subsequent image re-construction. Overall, general confocal microscopy can also break through the diffraction limit of the excitation light by using algorithms, resulting in super-resolution Raman imaging to capture nanoplastics.

Raman imaging for the analysis of silicone microplastics and nanoplastics released from a kitchen sealant.

Plastic products are used ubiquitously and can potentially release microplastics and nanoplastics into the environment, for example, products such as the silicone sealant used in kitchens. It is important to develop an effective method to monitor these emerging contaminants, as reported herein. By using advanced Raman imaging to characterize microplastics and nanoplastics from hundreds of spectra in a scanning spectrum matrix and not from a single spectrum or peak, the signal-to-noise ratio can be significantly increased, from a statistical point of view. The diffraction of the laser spot usually constrains the imaging resolution (such as at ∼300 nm), which is also pushed to the limit in this report by shrinking the scanning pixel size down to ∼50 nm to capture and image small nanoplastics effectively. To this end, image reconstruction is developed to successfully pick up the meaningful Raman signal and intentionally avoid the noise. The results indicate that the silicone sealant in a kitchen can release a significant amount of microplastics and nanoplastics. Overall, advanced Raman imaging can be employed to characterize the microplastics and even nanoplastics that are smaller than the diffraction limit of the laser via Raman imaging and image reconstruction toward deconvolution.

Detection of microplastics and nanoplastics released from a kitchen blender using Raman imaging.

Microplastics and nanoplastics have secretly entered our daily lives but the extent of the problem is still unclear, as the characterisation is still a challenge, particularly for nanoplastics. Herein we test a blender that we use in our kitchen to make juice and we find that a significant amount of microplastics and nanoplastics (∼0.36-0.78 × 109 within 30 s) are released from the plastic container. We advance the characterisation of microplastics and nanoplastics using Raman imaging to generate a scanning spectrum matrix, akin to a hyperspectral matrix, which contains 900 spectra (30 × 30). By mapping these hundreds of spectra as images, with help of algorithms, we can directly visualise the microplastics and nanoplastics with an increased sensitivity from statistical point of view. Raman imaging has a main disadvantage of the imaging resolution, originating from the diffraction of the laser spot, which is proposed to be improved by shrinking the scanning pixel size, zooming in the scanning area to capture details of nanoplastics. Using image re-construction towards deconvolution, the nanoplastics can be effective characterised and the bumpy image of microplastics stemming from the signal variation can be subsequently smoothened to further increase the signal-noise ratio. Overall, the advancements on Raman imaging can provide a suitable approach to characterise microplastics and nanoplastics released in our daily lives, for which we should be cautious.

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

Background: COVID-19 pandemic is not yet over, and it has been generating lots of plastic wastes that become a big concern. To catch the virus, for example, no matter via antigen or PCR test, swab is generally used for sampling. Unfortunately, the swab tip is commonly made of plastics, and thus it can be a potential source of microplastics. This study aims to propose and optimise several Raman imaging to identify the microplastic fibres released from different COVID-19 test swabs. Results: The results show that Raman imaging can effectively identify and visualise the microplastic fibres released from the swabs. In the meantime, on the surface of the fibres, additives such as titanium oxide particles are also captured for some brands of swabs. To increase the result certainty, scanning electron microscope (SEM) is first employed to get the morphology of the released microplastic fibres, along with Energy-dispersive X-ray spectroscopy (EDS) to confirm the presence of titanium element. Then, Raman imaging is advanced to identify and visualise the microplastics and titanium oxide particles, from different characteristic peaks in the scanning spectrum matrix. To further increase the imaging certainty, these images can be merged and cross-checked using algorithms, or the raw data from the scanning spectrum matrix can be analysed and decoded via chemometrics, such as principal component analysis (PCA). Beyond the advantages, the disadvantages of the confocal Raman imaging (affected by focal height) and algorithms (non-supervised calculation) are also discussed and intentionally corrected. In brief, the imaging analysis (particularly the combined SEM with Raman) is recommended to avoid the possible result bias that might be generated from the single spectrum analysis at a selective but random position. Conclusions: Overall, the results indicate that Raman imaging can be a useful tool to detect microplastics. The results also send us a strong warning that, if we worry about the potential microplastics contamination, we should be cautious to select the suitable COVID-19 testing kits. Supplementary Information: The online version contains supplementary material available at 10.1186/s12302-023-00737-0.

Thermal kinetics of PFAS and precursors in soil: Experiment and surface simulation in temperature-time plane.

Per- and polyfluoroalkyl substances (PFAS) are chemically and thermally stable due to the presence of carbon-fluorine (C-F) bond in their molecular structures, hence have been previously formulated as firefighting ingredients. During the firefighting process, however, owing to the high temperature, PFAS can be potentially degraded, particularly for PFAS precursors that contain non-C-F bonds, which is studied herein by exposing PFAS-contaminated soil in a muffle furnace oven. Different temperatures and time intervals are applied to the real soil sample to mimic the firing process and to evaluate the degradation and conversion of PFAS. This thermal treatment can not only degrade precursors (e.g. 6:2 fluorotelomer sulphonate), but also degrade perfluoroalkyl carboxylates (PFCA, e.g. perfluorooctanoic acid PFOA) and perfluoroalkyl sulfonates (PFSA, e.g. perfluorooctane sulfonate PFOS). The concentration dependence of the PFAS on temperature and time is fitted using a 2D Gaussian surface to simulate the complex thermal kinetic, and to compare with the traditional approach such as thermogravimetric analysis (TGA) (1D dependence on temperature only). The 2D simulation can directly visualise the thermal kinetic of individual or sum PFAS in the complex temperature-time plane, which depends on the sample background and particularly on the coexist PFAS precursors. Overall, this study provides a simple approach to monitor and optimise the thermal treatment of the PFAS-contaminated soil.

[Molecular Mechanism of Busulfan Inhibiting the Malignant Biological Characteristics of Multiple Myeloma Cells].

OBJECTIVE: To investigate the effect of Busulfan on the malignant biological characteristics of multiple myeloma cells and explore the molecular mechanism. METHODS: Multiple myeloma RPMI8226 cells were treated with Busulfan at different concentrations. Cell proliferation activity was detected by MMT assay, and cell apoptosis was detected by flow cytometry with Annexin V/PI double staining. Real-time quantitative PCR was used to detect the mRNA expression of apoptotic regulatory molecules Bax、Bcl-2 and Wnt3a/ß-catenin pathway, and Western blot was used to detect the expression changes of Bax, Bcl-2 and Wnt3a/ß-catenin pathway protein. RESULTS: Busulfan inhibited cell proliferation and induced apoptosis of myeloma RPMI8226 cells (P<0.05). After treatment with Busulfan at different concentrations for 48 h, the expression of anti-apoptotic protein Bcl-2 was decreased, the expression of pro-apoptotic protein Bax was up-regulated, and the activation of Wnt3a/ß-catenin signaling pathway was inhibited to induce programmed death of RPMI8226 cells (P<0.05). CONCLUSION: Busulfan can inhibit the malignant biological characteristics of myeloma RPMI 8226 cells, and its mechanism may be related to regulating the expression of Bcl-2 family proteins and inhibiting the activation of Wnt3a/ß-catenin signaling pathway.

Ultrasound-enhanced Magnéli phase Ti4O7 anodic oxidation of per- and polyfluoroalkyl substances (PFAS) towards remediation of aqueous film forming foams (AFFF).

Per-and polyfluoroalkyl substances (PFAS) remediation is still a challenge. In this study, we propose a hybrid system that combines electrochemical treatment with ultrasound irradiation, aiming for an enhanced degradation of PFAS. Equipped with a titanium suboxide (Ti4O7) anode, the electrochemical cell is able to remove perfluorooctanoic acid (PFOA) effectively. Under the optimal conditions (50 mA/cm2 current density, 0.15 M Na2SO4 supporting electrolyte, and stainless steel/Ti4O7/stainless steel electrode configuration with a gap of ∼10 mm), the electrochemical process achieves ∼100 % PFOA removal and 43 % defluorination after 6 h. Applying ultrasound irradiation (130 kHz) alone offers a limited PFOA removal, with 33 % PFOA removal and 5.5 % defluorination. When the electrochemical process is combined with ultrasound irradiation, we observe a significant improvement in the remediation performance, with ∼100 % PFOA removal and 63.5 % defluorination, higher than the sum of 48.5 % (43 % achieved by the electrochemical process, plus 5.5 % by the ultrasound irradiation), implying synergistic removal/oxidation effects. The hybrid system also consistently shows the synergistic defluorination during degradation of other PFAS and the PFAS constituents in aqueous film forming foam (AFFF). We attribute the synergistic effect to an activated/cleaned electrode surface, improved mass transfer, and enhanced production of radicals.

A modified TOP assay to detect per- and polyfluoroalkyl substances in aqueous film-forming foams (AFFF) and soil.

Total oxidisable precursor (TOP) assay can oxidise some per- and polyfluoroalkyl substances (PFASs) and their precursors, most of which cannot be quantitatively detected so far, and convert them to detectable PFASs, such as perfluoroalkyl acids (PFAAs). However, the conversion is constrained by the complexity of the target samples, including co-existent organics, unknown PFAS precursors, and background. In this study, the TOP assay is modified to increase the oxidation and conversion efficiency by changing the initial concentration of target sample, increasing oxidising doses, time, temperature, etc. The modified TOP assay is applied to test several aqueous film-forming foams (AFFF) and a PFAS-contaminated soil extract. The sum concentrations of the detectable PFASs are increased by up to ∼534× in the AFFF samples and ∼7× in the PFAS-contaminated soil extract. The detectable fluorotelomer sulfonate (FTS, such as 6:2/8:2 FTS) is accounted as an oxidation indicator to monitor the oxidation and conversion progress of the oxidisable PFASs precursors to the detectable PFASs. Overall, the modified TOP assay could be an appropriate method for identifying missing PFASs mass in complex matrices by detecting the PFASs precursors effectively.

Microplastics and nanoplastics released from a PPE mask under a simulated bushfire condition.

Due to COVID-19, large amounts of personal protective equipment (PPE) have been used, and many PPE units are made of plastics, such as face masks. The masks can be burned naturally in a bushfire or artificially at the incineration plants, and release microplastics and nanoplastics from the mask plastic fibres. A fire can cause the plastic, such as polypropylene (PP) fibres, to be molten and stick to the solid surface, such as glass, soil, concrete or plant, as films or islands, due to the binding property of the molten plastic material. Once the films or islands are peeled off in the processes such as weathering, ageing, or treatment and clean-up, there are residuals leftover, which are identified as nanoplastics and microplastics via Raman imaging, with the significant release amount of ~1100 nanoplastics / 10 µm2 or ~11 billion / cm2, and ~50 microplastics / 420 µm2 or ~12 million / cm2. Moreover, surface group is deviated on the plastic surface, which can also be distinguished and visualised as well via Raman imaging, down to nano size. This test validates the Raman imaging approach to capture microplastics and nanoplastics, and also provides important information about the fate and transportation of PPE mask in the environment, particularly when subjected to a fire. Overall, Raman imaging can be an effective option to characterise the microplastics and nanoplastics, along with the deviated surface group.

Characterising microplastics in shower wastewater with Raman imaging.

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.