Identification and visualisation of microplastics/ nanoplastics by Raman imaging (ii): Smaller than the diffraction limit of laser?

Silver nanostars arrayed on GO/MWCNT composite membranes for enrichment and SERS detection of polystyrene nanoplastics in water

Nanoplastic water contamination has become a critical environmental issue, highlighting the need for rapid and sensitive detection of nanoplastics. In this study, we aimed to prepare a graphene oxide (GO)/multiwalled carbon nanotube (MWCNT)-silver nanostar (AgNS) multifunctional membrane using a simple vacuum filtration method for the enrichment and surface-enhanced Raman spectroscopy (SERS) detection of polystyrene (PS) nanoplastics in water. AgNSs, selected for the size and shape of nanoplastics, have numerous exposed Raman hotspots on their surface, which exert a strong electromagnetic enhancement effect. AgNSs were filter-arrayed on GO/MWCNT composite membranes with excellent enrichment ability and chemical enhancement effects, resulting in the high sensitivity of GO/MWCNT-AgNS membranes. When the water samples flowed through the portable filtration device with GO/MWCNT-AgNS membranes, PS nanoplastics could be effectively enriched, and the retention rate for 50 nm PS nanoplastics was 97.1 %. Utilizing the strong SERS effect of the GO/MWCNT-AgNS membrane, we successfully detected PS nanoparticles with particle size in the range of 50-1000 nm and a minimum detection concentration of 5 × 10-5 mg/mL. In addition, we detected 50, 100, and 200 nm PS nanoplastics at concentrations as low as 5 × 10-5 mg/mL in real water samples using spiking experiments. These results indicate that the GO/MWCNT-AgNS membranes paired with a portable filtration device and Raman spectrometer can effectively enrich and rapidly detect PS nanoplastics in water, which has great potential for on-site sensitive water quality safety evaluation.

Raman Characterization of Plastics: A DFT Study of Polystyrene

Plastic materials are ubiquitous and raise concerns about their impact on health and the environment. To address these concerns, it is crucial to characterize the structural, size, and textural properties of plastics throughout their lifecycle from production to degradation. Raman spectroscopy appears as a valuable tool for this purpose, offering speed, robustness, and sensitivity to nanoscale and amorphous particles. In order to be properly used for plastics, the Raman response of reference materials needs to be carefully assessed, with the literature on such assessments being scarce. This study addresses this gap by using theoretical calculations to generate ab initio spectra for polystyrene, a reference material. The aim is to explain the origins of the spectral peaks and their consistency across various compositions and structures using linear ordered polymeric and finite amorphous models. The CRYSTAL package is employed to obtain full Raman spectra based on a careful benchmark of computational settings. While some peaks are present across all spectra and can serve for calibration, others exhibit structure-dependent behavior, enabling polymer identification. We conclude that Raman spectroscopy is a well-suited technique for plastics characterization provided that a careful analysis of signal origin is conducted to fully interpret the spectra and deploy applications.

Convenient Size Analysis of Nanoplastics on a Microelectrode

Chemical recycling is a promising approach to reduce plastic pollution. Timely and accurate size analysis of produced nanoplastics is necessary to monitor the process and assess the quality of chemical recycling. In this work, a sandwich-type microelectrode sensor was developed for the size assessment of nanoplastics. β-Mercaptoethylamine was modified on the microelectrode to enhance its surface positive charge density. Polystyrene (PS) nanoplastics were captured on the sensor through electrostatic interactions. Ferrocene was used as an electrochemical beacon and attached to PS via hydrophobic interactions. The results show a nonlinear dependence of the sensor's current response on the PS particle size. The size resolving ability of the microelectrode is mainly attributed to the small size of the electrode and the resulting attenuation of the electric field strength. For mixed samples with different particle sizes, this method can provide accurate average particle sizes. Through an effective pretreatment process, the method can be applied to PS nanoplastics with different surface properties, ensuring its application in evaluating different chemical recycling methods.

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.

Underappreciated microplastic galaxy biases the filter-based quantification

Long-term environmental loading of microplastics (MPs) causes alarming exposure risks for a variety of species worldwide, considered a planetary threat to the well-being of ecosystems. Robust quantitative estimates of MP extents and featured diversity are the basis for comprehending their environmental implications precisely, and of these methods, membrane-based characterizations predominate with respect to MP inspections. However, though crucial to filter-based MP quantification, aggregation statuses of retained MPs on these substrates remain poorly understood, leaving us a "blind box" that exaggerates uncertainty in quantitive strategies of preselected areas without knowing overview loading structure. To clarify this uncertainty and estimate their impacts on MP counting, using MP imaging data assembled from peer-reviewed studies through a systematic review, here we analyze the particle-specific profiles of MPs retained on various substrates according to their centre of mass with a fast-random forests algorithm. We visualize the formation of distinct galaxy-like MP aggregation-similar to the solar system and Milky Way System comprised of countless stars-across the pristine and environmental samples by leveraging two spatial parameters developed in this study. This unique pattern greatly challenges the homogeneously or randomly distributed MP presumption adopted extensively for simplified membrane-based quantification purposes and selective ROI (region of interest) estimates for smaller-sized plastics down to the nano-range, as well as the compatibility theory using pristine MPs as the standard to quantify the presence of environmental MPs. Furthermore, our evaluation with exemplified numeration cases confirms these location-specific and area-dependent biases in many imaging analyses of a selective filter area, ascribed to the minimum possibility of reaching an ideal turnover point for the selective quantitive strategies. Consequently, disproportionate MP schemes on loading substrates yield great uncertainty in their quantification processing, highlighting the prompt need to include pattern-resolved calibration prior to quantification. Our findings substantially advance our understanding of the structure, behavior, and formation of these MP aggregating statuses on filtering substrates, addressing a fundamental question puzzling scientists as to why reproducible MP quantification is barely achievable even for subsamples. This study inspires the following studies to reconsider the impacts of aggregating patterns on the effective counting protocols and target-specific removal of retained MP aggregates through membrane separation techniques.

Emergence of nanoplastics in the aquatic environment and possible impacts on aquatic organisms

Plastic production on a global scale is instrumental in advancing modern society. However, plastic can be broken down by mechanical and chemical forces of humans and nature, and knowledge of the fate and effects of plastic, especially nanoplastics, in the aquatic environment remains poor. We provide an overview of current knowledge on the environmental occurrence and toxicity of nanoplastics, and suggestions for future research. There are nanoplastics present in seas, rivers, and nature reserves from Asia, Europe, Antarctica, and the Arctic Ocean at levels of 0.3-488 microgram per liter. Once in the aquatic environment, nanoplastics accumulate in plankton, nekton, benthos through ingestion and adherence, with multiple toxic results including inhibited growth, reproductive abnormalities, oxidative stress, and immune system dysfunction. Further investigations should focus on chemical analysis methods for nanoplastics, effect and mechanism of nanoplastics at environmental relevant concentrations in aquatic organisms, as well as the mechanism of the Trojan horse effect of nanoplastics.

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.

Sub-10 nm Nanoparticle Detection Using Multi-Technique-Based Micro-Raman Spectroscopy

Microplastic pollution is a growing public concern as these particles are ubiquitous in various environments and can fragment into smaller nanoplastics. Another environmental concern arises from widely used engineered nanoparticles. Despite the increasing abundance of these nano-sized pollutants and the possibility of interactions with organisms at the sub cellular level, with many risks still being unknown, there are only a few publications on this topic due to the lack of reliable techniques for nanoparticle characterization. We propose a multi-technique approach for the characterization of nanoparticles down to the 10 nm level using standard micro-Raman spectroscopy combined with standard atomic force microscopy. We successfully obtained single-particle spectra from 25 nm sized polystyrene and 9 nm sized TiO2 nanoparticles with corresponding mass limits of detection of 8.6 ag (attogram) and 1.6 ag, respectively, thus demonstrating the possibility of achieving an unambiguous Raman signal from a single, small nanoparticle with a resolution comparable to more complex and time-consuming technologies such as Tip-Enhanced Raman Spectroscopy and Photo-Induced Force Microscopy.

Quantification of nanoplastics uptake and transport in lettuce by pyrolysis gas chromatography-mass spectrometry

Nanoplastics (NPs) can enter the edible parts of crop and threaten human health, which attract widespread attention. However, the precise quantification of NPs in crop is still a tremendous challenge. Herein, a method with Tetramethylammonium hydroxide (TMAH) digestion, dichloromethane extraction combined with pyrolysis gas chromatography-mass spectrometry (Py-GC/MS) quantification was present to quantify polystyrene (PS) NPs uptake in lettuce (Lactuca sativa). 25% of TMAH was optimized as extraction solvent and 590 °C was selected as pyrolysis temperature. Recoveries of 73.4-96.9% were obtained for PS-NPs at spiking level of 4-100 μg/g in control samples (RSD < 8.6%). The method exhibited good intra-day and inter-day reproducibility, detection limits of 34-38 ng/g and linearity with 0.998-0.999. The reliability of Py-GC/MS method was verified by europium-chelated PS using inductively coupled plasma mass spectrometry (ICP-MS). To simulate different environmental conditions, hydroponic culture and soil incubated lettuce were exposed to different concentrations of NPs. Higher levels of PS-NPs were detected in roots and very few was transferred to the shoots. NPs in lettuce were confirmed by laser scanning confocal microscopy (LSCM). The developed method provides new opportunities for the quantification of NPs in crops.

Automatic Identification of Individual Nanoplastics by Raman Spectroscopy Based on Machine Learning

The increasing prevalence of nanoplastics in the environment underscores the need for effective detection and monitoring techniques. Current methods mainly focus on microplastics, while accurate identification of nanoplastics is challenging due to their small size and complex composition. In this work, we combined highly reflective substrates and machine learning to accurately identify nanoplastics using Raman spectroscopy. Our approach established Raman spectroscopy data sets of nanoplastics, incorporated peak extraction and retention data processing, and constructed a random forest model that achieved an average accuracy of 98.8% in identifying nanoplastics. We validated our method with tap water spiked samples, achieving over 97% identification accuracy, and demonstrated the applicability of our algorithm to real-world environmental samples through experiments on rainwater, detecting nanoscale polystyrene (PS) and polyvinyl chloride (PVC). Despite the challenges of processing low-quality nanoplastic Raman spectra and complex environmental samples, our study demonstrated the potential of using random forests to identify and distinguish nanoplastics from other environmental particles. Our results suggest that the combination of Raman spectroscopy and machine learning holds promise for developing effective nanoplastic particle detection and monitoring strategies.

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.

Application of Infrared and Near-Infrared Microspectroscopy to Microplastic Human Exposure Measurements

Microplastic pollution is a global issue for the environment and human health. The potential for human exposure to microplastic through drinking water, dust, food, and air raises concern, since experimental in vitro and in vivo toxicology studies suggest there is a level of hazard associated with high microplastic concentrations. However, to infer the likelihood of hazards manifesting in the human population, a robust understanding of exposure concentrations is needed. Infrared and near-infrared microspectroscopies have routinely been used to analyze microplastic in different exposure matrices (air, dust, food, and water), with technological advances coupling multivariate and machine learning algorithms to spectral data. This focal point article will highlight the application of infrared and Raman modes of spectroscopy to detect, characterize, and quantify microplastic particles, with a focus on human exposure to microplastic. Methodologies and state-of-the-art approaches will be reported and potential confounding variables and challenges in microplastic analysis discussed. The article provides an up-to-date review of the literature on microplastic exposure measurement using (near) infrared spectroscopies as an analytical tool, highlighting the recent advances in this rapidly advancing field.

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.

Imaging and identification of single nanoplastic particles and agglomerates

Pollution by nanoplastic is a growing environmental and health concern. Currently the extent of nanoplastic in the environment can only be cumbersomely and indirectly estimated but not measured. To be able to quantify the extent of the problem, detection methods that can identify nanoplastic particles that are smaller than 1 [Formula: see text]m are critically needed. Here, we employ surface-enhanced Raman scattering (SERS) to image and identify single nanoplastic particles down to 100 nm in size. We can differentiate between single particles and agglomerates and our method allows an improvement in detection speed of [Formula: see text] compared to state-of-the art surface-enhanced Raman imaging. Being able to resolve single particles allows to measure the SERS enhancement factor on individual nanoplastic particles instead of averaging over a concentration without spatial information. Our results thus contribute to the better understanding and employment of SERS for nanoplastic detection and present an important step for the development of future sensors.

Misinterpretation in microplastic detection in biological tissues: When 2D imaging is not enough

The presence of microplastics in the food chain is a public concern worldwide, and its analysis is an analytical challenge. In our research, we apply Raman imaging to study the presence of 1 μm polystyrene microplastics in cryosections of Mytilus galloprovincialis due to its wide geographic distribution, widespread occurrence in the food web, and general high presence in the environment. Ingested microplastics are accumulated in the digestive tract, but a large number can also be rapidly eliminated. Some authors state that the translocation of microplastics to the epithelial cells is possible, increasing the risk of microplastics transmission along the food chain. However, as seen in our study, a surface imaging approach (2D) is probably not enough to confirm the internalization of particles and avoid misinterpretation. In fact, while some microplastic particles were detected in the epithelium by 2D Raman imaging, further 3D Raman imaging analysis demonstrated that those particles were dragged from the lumens to the epithelium during sample preparation due to the blade drag effect of the cryotome, and subsequently located on the surface of the analyzed cryosection, discarding the translocation to the epithelial cells. This effect can also happen when the samples are fortuitously contaminated during sample preparation. Several research articles that use similar analytical techniques have shown the presence of microplastics in different types of tissue. It is not our intention to put such results in doubt, but the present work points out the necessity of appropriate three-dimensional analytical methods including data interpretation and the need to go a step further than just surface imaging analysis.

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.

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.

Nanoplastics are significantly different from microplastics in urban waters

Microplastics (MPs) and nanoplastics (NPs) are ubiquitous and intractable in urban waters. Compared with MPs, the smaller NPs have shown distinct physicochemical features, such as Brownian motion, higher specific surface area, and stronger interaction with other pollutants. Therefore, the qualitative and quantitative analysis of NPs is more challenging than that of MPs. Moreover, these characteristics endow NPs with significantly different environmental fate, interactions with pollutants, and eco-impacts from those of MPs in urban waters. Herein, we critically analyze the current advances in the difference between MPs and NPs in urban waters. Analytical challenges, fate, interactions with surrounding pollutants, and eco-impacts of MPs and NPs are comparably discussed., The characterizations and fate studies of NPs are more challenging compared to MPs. Furthermore, NPs in most cases exhibit stronger interactions with other pollutants and more adverse eco-impacts on living things than MPs. Subsequently, perspective in this field is proposed to stimulate further size-dependent studies on MPs and NPs. This review would benefit the understanding of the role of NPs in the urban water ecosystem and guide future studies on plastic pollution management.

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).

Toxicity of nanoplastics to aquatic organisms: Genotoxicity, cytotoxicity, individual level and beyond individual level

Due to the small size, high mobility and large surface area, nanoplastics (NPs) showed high potential risks to aquatic organisms. This paper reviews the toxicity of NPs to aquatic organism at various trophic levels including bacteria, plankton (algae), zooplankton, benthos, and nekton (fish). The effects at individual level caused by NPs were explained and proved by cytotoxicity and genotoxicity, and the toxicity of NPs beyond individual level was also illustrated. The toxicity of NPs is determined by the size, dosage, and surface property of NPs, as well as environmental factors, the presence of co-contaminants and the sensitivity of tested organisms. Furthermore, the joint effects of NPs with other commonly detected pollutants such as organic pollutants, metals, and nanoparticles etc. were summarized. In order to reflect the toxicity of NPs in the real natural environment, studies on toxicity assessment of NPs with the coexistence of various environmental factors and contaminants, particularly under the concentrations in natural environment are suggested.

Multi-feature round silicon membrane filters enable fractionation and analysis of small micro- and nanoplastics with Raman spectroscopy and nano-FTIR

Visualization of small micro-(20-1 μm) and nanoplastics (<1 μm) combined with chemical identification is still a challenge. To address this, we designed and manufactured easy-to-handle silicon membrane filters with a standard round filter geometry of 25 mm in diameter and a 10 mm diameter filtration area, holding hexagonal sections with periodically arranged pores of either 250 nm or 1 μm. Due to their flat and reflective surface, the filters serve as a versatile substrate for spectroscopic identification of particles. Optical markers at different levels of magnification, including the bare eye, allow for an easy transfer and repositioning of samples between instruments and methods as well as for a re-measurement of nanoscale particles. We demonstrate how nanoscale particles of weakly absorbing polymers such as polyethylene and polystyrene are analyzed on these filters by nano-FTIR, a combination of atomic force microscopy and Fourier transform infrared spectroscopy. By sequential filtration we separated the fractions of small micro and nanoplastics from a degraded polylactic acid coffee cup lid and achieved subsequent identification by Raman and nano-FTIR spectroscopy. The applications presented in this study will enable future research regarding the identification of small polymer particles difficult to access by other methods.

Size-independent quantification of nanoplastics in various aqueous media using surfaced-enhanced Raman scattering

In this work, we successfully developed an intriguing preparation strategy to reduce the size-dependent effect of nanoplastics (NPLs), which is the limitation of NPLs quantification by surface-enhanced Raman scattering (SERS). This simple and low-cost technique enabled us to quantify different sizes (i.e., 100, 300, 600, and 800 nm) of polystyrene nanospheres (PS NSs) in various aqueous media. The SERS substrate was simply prepared by sputtering gold particles to cover on a glass cover slide. By dissolving PS NSs in toluene and preconcentrating by coffee-ring effect, SERS measurement can quantify NPLs at a very low concentration with a limit of detection (LOD) of approximately 0.10-0.26 μg/mL. The experiment was also conducted in the presence of interferences, including salts, sugars, amino acids, and detergents. The method was validated for quantitative analysis using a mixture of 100-, 300-, 600-, and 800-nm PS NSs in a ratio of 1:1:1:1 in real-world media (i.e., tap water, mineral water, and river water), which successfully approaches the evaluation of PS NSs in the range of 10-40 µg/mL with an LOD of approximately 0.32-0.52 µg/mL.

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.

Estimation of contamination level in microplastic-exposed crayfish by laser confocal micro-Raman imaging

Crayfish is one of the most important freshwater aquaculture species in China. The potential risks of crayfish consumption caused by environmental microplastic pollution have attracted much attention. In this study, a total of 72 crayfish samples were exposed to the microplastic concentrations of 1 mg/L, 3 mg/L, and 9 mg/L for 7, 14, and 28 days, and microplastic contamination levels in crayfish were then explored by laser confocal micro-Raman (LCM-Raman) imaging and scanning electron microscope (SEM). LCM-Raman imaging showed better performance in microplastics identification. Besides, the average percentage of the contaminated area in visualized LCM-Raman images was used to quantitatively assess contamination levels. Following 28 days of exposure to 9 mg/L microplastics, microplastic accumulation reached about 13,000 particles per crayfish. The results confirmed that LCM-Raman imaging combined with image processing technology could be used to construct a high-performance analytical strategy for the assessment of microplastic contamination in crayfish.

Nanoplastics: Detection and impacts in aquatic environments - A review

The rise in the global production of plastics has led to severe concerns about the impacts of plastics in aquatic environments. Although plastic materials degrade over extreme long periods, they can be broken down through physical, chemical, and/or biological processes to form microplastics (MPs), defined here as particles between 1 μm and 5 mm in size, and later to form nanoplastics (NPls), defined as particles <1 μm in size. We know little about the abundance and effects of NPls, even though a lot of research has been conducted on the ecotoxicological impacts of MPs on both aquatic biota. Nevertheless, there is evidence that NPls can both bypass the cell membranes of microorganisms and bioaccumulate in the tissues and organs of higher organisms. This review analyzes 150 publications collected by searching through the databases Web of Science, SCOPUS, and Google Scholar using keywords such as nanoplastics*, aquatic*, detection*, toxic*, biofilm*, formation*, and extracellular polymeric substance* as singular or plural combinations. We highlight and critically synthesize current studies on the formation and degradation of NPls, NPls' interactions with aquatic biota and biofilm communities, and methods of detection. One reason for the missing data and studies in this area of research is the lack of a protocol for the detection of, and suitable methods for the characterization of, NPls in the field. Our primary aim is to identify gaps in knowledge throughout the review and define future directions of research to address the impacts of NPls in aquatic environments. The development of consistent and standardized sets of procedures would address the gaps in knowledge regarding the formation and degradation of NPls as well as sampling and characterizing natural NPls needed to observe the full extent of NPls on aquatic biota and biofilm communities.

Detection of Environmental Microfiber Pollutants through Vibrational Spectroscopic Techniques: Recent Advances of Environmental Monitoring and Future Prospects

A robust environmental monitoring system is highly essential for the instant detection of environmental microfiber pollutants for the sustainable management of the environment and human health. The extent of microfiber pollution is growing exponentially across the globe in both terrestrial and marine environments. An immediate and accurate environmental monitoring system is crucial to investigate the composition and distribution of these micropollutants. Fourier Transform Infrared Spectroscopy and Raman Spectroscopy are vibrational spectroscopic techniques that have the novel ability to detect microfibers within a minute concentration from diverse environmental samples. The major micropollutants which have been analyzed are polyethylene, polypropylene, nylon 6, polystyrene, and polyethylene terephthalate. After a detailed and critical study of the various aspects of spectroscopic analysis, the review is concluded with a comprehensive discussion of the significance of these robust methods and their application in future aspects for further preventing microfiber pollution in the marine environment. This study highlights the utilities and significance of vibrational spectroscopic detection techniques for the immediate and accurate identification of synthetic microfibers. This review also evaluated the implementation of spectroscopic methods as a precise tool for the characterization and monitoring of microfiber pollutants in the environment.

Fire releases micro- and nanoplastics: Raman imaging on burned disposable gloves

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.

Plants oxidative response to nanoplastic

Pollution of the environment with plastic is an important concern of the modern world. It is estimated that annually over 350 million tonnes of this material are produced, wherein, despite the recycling methods, a significant part is deposited in the environment. The plastic has been detected in the industrial areas, as well as farmlands and gardens in many world regions. Larger plastic pieces degraded in time into smaller pieces including microplastic (MP) and nanoplastic particles (NP). Nanoplastic is suggested to pose the most serious danger as due to the small size, it is effectively taken up from the environment by the biota and transported within the organisms. An increasing number of reports show that NP exert toxic effects also on plants. One of the most common plant response to abiotic stress factors is the accumulation of reactive oxygen species (ROS). On the one hand, these molecules are engaged in cellular signalling and regulation of genes expression. On the other hand, ROS in excess lead to oxidation and damage of various cellular compounds. This article reviews the impact of NP on plants, with special emphasis on the oxidative response.

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.

Revealing Trace Nanoplastics in Food Packages─An Electrochemical Approach Facilitated by Synergistic Attraction of Electrostatics and Hydrophobicity

Most food packages are made of plastics, nanoplastics released from which can be directly ingested and induce serious damage to organisms. Therefore, it is urgent to develop an effective and convenient method for nanoplastic determinations in food packages. In this work, we present a sandwich-based electrochemical strategy for nanoplastic determination. Positively charged Au nanoparticles were coated onto a Au electrode to selectively capture negatively charged nanoplastics in an aqueous environment. Subsequently, the nanoplastics were recognized by the signal molecule ferrocene via the hydrophobic interaction and determined by differential pulse voltammetry. Our sandwich-type detection depends on both electronegativity and hydrophobicity of nanoplastics, which make the method applicable for the assays of packages made of widely commercialized polystyrene (PS), polypropylene (PP), polyethylene (PE), and polyamide (PA). The method displays different sensitivities to above four nanoplastics but the same dynamic range from 1 to 100 μg·L^-1. Based on it, the nanoplastics released from several typical food packages were assayed. Teabags were revealed with significant nanoplastic release, while instant noodle boxes, paper cups, and take-out boxes release slightly. The good recoveries in nanoplastic-spiked samples confirm the accuracy and applicability of this method. This work provides a sensitive, low-cost, and simple method without complicated instruments and pretreatment, which is of great significance for the determination of nanoplastics released from food packages.

Investigating kitchen sponge-derived microplastics and nanoplastics with Raman imaging and multivariate analysis

Microplastics can be found almost everywhere, including in our kitchens. The challenge is how to characterise them, particularly for the small ones (<1 μm), referred to as nanoplastics, when they are mixed with larger particles and other components. Herewith we advance Raman imaging to characterise microplastics and nanoplastics released from a dish sponge that we use every day to clean our cookware and eating utensils. The scanning electron microscopy result shows significantly different structures of the soft and hard layers of the sponge, with the hard layer being more likely to shed particles. By scanning the sample surface to generate a spectrum matrix, Raman imaging can significantly improve signal-noise-ratio, compared with individual Raman spectra. Through mapping the characteristic peaks from the matrix that contains hundreds, even thousands of Raman spectra, it is confirmed that the particles released from the soft and hard layers of the sponge are mainly Nylon PA6 and polyethylene terephthalate, respectively. Using principal component analysis (PCA) to decode the spectrum matrix further enhances the signal-noise ratio, which enables mapping the whole set of the spectrum, rather than the selected peaks. By optimising the Raman scanning parameters, the PCA-Raman imaging is able to reliably capture and visualise microplastics and nanoplastics released from both sides of the dish sponge, including a plastic-surrounding-sand composite structure. Overall, PCA-Raman imaging is a holistic and effective approach to characterising miniature plastic particles.

Assessment of microplastics and nanoplastics released from a chopping board using Raman imaging in combination with three algorithms

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 mm² 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.

Visualization and (Semi-)quantification of submicrometer plastics through scanning electron microscopy and time-of-flight secondary ion mass spectrometry

Increasing numbers of studies have demonstrated the existence of nanoplastics (1-999 nm) in the environment and commercial products, but the current technologies for detecting and quantifying nanoplastics are still developing. Herein, we present a combination of two techniques, e.g., scanning electron microscopy (SEM) and time-of-flight secondary ion mass spectrometry (ToF-SIMS), to analyze submicron-sized plastics. A drop-casting of a 20-nL particle suspension on a Piranha solution-cleaned silicon wafer with dry ice incubation and subsequent freeze-drying was used to suppress the coffee-ring effect. SEM images were used to quantify particles, and this technique is applicable for 0.195-1.04-μm polystyrene (PS), 0.311-μm polyethylene terephthalate (PET), and 0.344-μm polyethylene (PE) at a minimum concentration of 2.49 × 10⁹ particles/mL. ToF-SIMS could not quantify the particle number, while it could semi-quantitatively estimate number ratios of submicron PE, PET, polyvinyl chloride (PVC), and PS particles in the mixture. Analysis of submicron plastics released from three hot water-steeped teabags (respectively made of PET/PE, polylactic acid (PLA), and PET) was revisited. The SEM-derived sizes and particle numbers were comparable to those measured by a nanoparticle tracking analysis (NTA) regardless of whether or not the hydro-soluble oligomers were removed. ToF-SIMS further confirmed the number ratios of different particles from a PET/PE composite teabag leachate. This method shows potential for application in analyzing more-complex plastic particles released from food contact materials.

Characterization of microplastics in indoor and ambient air in northern New Jersey

Airborne microplastics (MPs) could have negative impacts on human health and pollute water, soil, and sediment. This study explored the distributions, compositions, and morphology of airborne microplastics in the indoor and ambient air in New Jersey, United States. Microplastic fibers, films, and fragments of Polystyrene (PS), Polyethylene terephthalate (PET), Polyethyelene (PE), Polyvinyl chloride (PVC) and Polypropylene (PP) were identified in office, hallway, classroom, and single-family house in this study. The deposition rates of synthetic fibers with length from 35 μm to 1000 μm were highest in the single-family house ((1.96 ± 1.09) × 10⁴ fibers/m²/day) and lowest in the classroom ((6.20 ± 0.57) × 10³ fibers/m²/day), suggesting that residential houses are a major source of microplastic fibers. However, for film-like plastics with surface areas ranging from 200 μm² to 5000 μm², a high deposition rate of (8.13 ± 2.17) × 10³ films/m²/day was observed in the classroom, whereas the lowest deposition rate of (4.45 ± 0.27) × 10³ films/m²/day was found in the hallway, probably because plastic films such as PE are intensively used in the classroom environments. The deposition rate of microplastics in the ambient air acquired on a building roof was only about 2-8% of the indoor deposition rates. The microplastics with similar textures but different sizes were identified in both total atmospheric deposition and particulate samples (PM_2.5 and PM₁₀), suggesting degradation from microplastics to nanoplastics. The main microplastics found in indoor air samples were PE fragments or fibers, different from those in the outdoor ambient air that were dominated by PVC fragments. These results unravel the properties of airborne microplastics in urban environments that are important to understanding their fate, transport, and potential health risks.

Linking the physical and chemical characteristics of single small microplastics or nanoplastics via photolithographic silicon substrates

Small microplastics and nanoplastics are of growing concern as they pose more risks to ecological and human health than larger particles. However, characterization of the morphological and chemical features of single particles faces a major challenge if instrumental combination is not available. Here, we developed a marker system via computer aided design and crafted it on a silicon substrate (8 × 8 mm) via direct-write lithography. We dripped 20 μL of a solution containing tiny particles extracted from a highly weathered plastic fragment onto a silicon substrate. After the solution was oven-dried, the polymer composition of particles down to 895 nm was located via multiple markers and identified using micro-Raman. The lithographic substrate was then transferred to a scanning electron microscope with energy dispersive spectroscopy capability, and the surface morphology and element distribution were captured for the same particle. Similarly, the morphology and surface elevation were characterized using a scanning electron microscope and an atomic force microscope. The average retrieval rate for particles reached 86% if all characterization experiments were conducted within one week. Our results suggest that photolithographic silicon substrates provide a novel and economical way to link the physicochemical characteristics of small microplastics and parts of nanoplastics.

Raman imaging of microplastics and nanoplastics generated by cutting PVC pipe

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.

Applying Raman imaging to capture and identify microplastics and nanoplastics in the garden

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.

Key knowledge gaps for One Health approach to mitigate nanoplastic risks

There are increasing concerns over the threat of nanoplastics to environmental and human health. However, multidisciplinary barriers persist between the communities assessing the risks to environmental and human health. As a result, the hazards and risks of nanoplastics remain uncertain. Here, we identify key knowledge gaps by evaluating the exposure of nanoplastics in the environment, assessing their bio-nano interactions, and examining their potential risks to humans and the environment. We suggest considering nanoplastics a complex and dynamic mixture of polymers, additives, and contaminants, with interconnected risks to environmental and human health. We call for comprehensive integration of One Health approach to produce robust multidisciplinary evidence to nanoplastics threats at the planetary level. Although there are many challenges, this holistic approach incorporates the relevance of environmental exposure and multi-sectoral responses, which provide the opportunity to identify the risk mitigation strategies of nanoplastics to build resilient health systems.

Microplastics in the surface sediments of Krossfjord-Kongsfjord system, Svalbard, Arctic

Krossfjord-Kongsfjord system in the European Arctic has been assessed for abundance, source and distribution of microplastics in the surface sediments. The average abundance of microplastics in Krossfjord and Kongsfjord is 721.42 ± 217.89 (n = 5) pieces/kg and 783 ± 530.28 (n = 8) pieces/kg. Polymers like polyethylene and polypropylene are abundant in the sediment samples. Fibers are the most common shape of plastic particles. A high abundance of smaller plastic particles in the sediment may be due to the fragmentation of larger plastic particles during transportation. The microplastics' spatial distribution, shape, size, and composition suggest that the long-range transport by west Spitsbergen current and local inputs from anthropogenic activities are possible sources of microplastics found in the study area. Our results exhibit the presence of microplastic pollution, suggesting the influence of anthropogenic activity in the Arctic fjord and the need to control/reduce marine pollution which has become a potential threat to marine organisms.

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.

Automated analysis of microplastics based on vibrational spectroscopy: are we measuring the same metrics?

The traditional manual analysis of microplastics has been criticized for its labor-intensive, inaccurate identification of small microplastics, and the lack of uniformity. There are already three automated analysis strategies for microplastics based on vibrational spectroscopy: laser direct infrared (LDIR)-based particle analysis, Raman-based particle analysis, and focal plane array-Fourier transform infrared (FPA-FTIR) imaging. We compared their performances in terms of quantification, detection limit, size measurement, and material identification accuracy and speed by analyzing the same standard and environmental samples. LDIR-based particle analysis provides the fastest analysis speed, but potentially questionable material identification and quantification results. The number of particles smaller than 60 μm recognized by LDIR-based particle analysis is much less than that recognized by Raman-based particle analysis. Misidentification could occur due to the narrow tuning range from 1800 to 975 cm^-1 and dispersive artifact distortion of infrared spectra collected in reflection mode. Raman-based particle analysis has a submicrometer detection limit but should be cautiously used in the automated analysis of microplastics in environmental samples because of the strong fluorescence interference. FPA-FTIR imaging provides relatively reliable quantification and material identification for microplastics in environmental samples greater than 20 μm but might provide an imprecise description of the particle shapes. Optical photothermal infrared (O-PTIR) spectroscopy can detect submicron-sized environmental microplastics (0.5-5 μm) intermingled with a substantial amount of biological matrix; the resulting spectra are searchable in infrared databases without the influence of fluorescence interference, but the process would need to be fully automated.

Microplastics and nanoplastics science: collecting and characterizing airborne microplastics in fine particulate matter

Microplastic (MP) pollution in the environment is increasing, leading to growing concerns about human exposures and the subsequent impact on health. Although marine MP research has received significant attention in recent years, only a few studies have attempted characterization of MP in air and examined the MP uptake and influence via inhalation on human health. Moreover, the methods used for MP characterization in the marine environment require further optimization to be applicable to MP in the air. This paper details method for collecting and characterizing MP < 2.5 μm in air samples for the purposes of toxicological assessment. The first phase of the study evaluated (a) the suitability of various filter types to collect respirable airborne MP <2.5 μm, and; (b) the ability of Raman and enhanced darkfield-hyperspectral spectroscopy methods to identify MP reference standards collected from spiked filters and in cells after exposure to reference MP. In the second phase, these methods were employed to characterize MP <2.5 μm in personal, indoor and outdoor filter air samples and in cells following exposure to filter extracted material. The results showed the presence of a variety of MP in the respirable size fraction (0.1-1 µm aerodynamic diameter). Silver membrane filters were found not suitable for collecting and analyzing MP <2.5 μm. While it was easy to detect reference MP in cells post-exposure, the identity of only two types of air-borne MP was confirmed in cells. The study highlighted possible sources of artifacts and inconsistencies in analyzing airborne MP.

Identification and visualisation of microplastics via PCA to decode Raman spectrum matrix towards imaging

To visualise microplastics and nanoplastics via Raman imaging, we need to scan the sample surface over a pixel array to collect Raman spectra as a matrix. The challenge is how to decode this spectrum matrix to map accurate and meaningful Raman images. This study compares two decoding approaches. The first approach is used when the sample contains several known types of microplastics whose standard spectra are available. We can map the Raman intensity at selected characteristic peaks as images. In order to increase the image certainty, we employ a logic-based algorithm to merge several images that are simultaneously mapped at several characteristic peaks to one image. However, the rest of the signals other than the selected peaks are ignored, meaning a low signal-noise ratio. The second approach for decoding is used when samples are complicated and standard spectra are not available. We employ principal component analysis (PCA) to decode the spectrum matrix. By selecting principal components (PC) and generating PC score curves to mimic the Raman spectrum, we can justify and assign the suspected items to microplastics and other materials. By mapping the PC loadings as images, microplastics and other materials can be simultaneously visualised. We analyse a sample containing two known microplastics to validate the effectiveness of the PCA-based algorithm. We then apply this method to analyse "unknown" microplastics printed on paper to extract Raman spectra from the complicated background and individually assign the images to paper fabric/additive, black carbon and microplastics, etc. Overall, the PCA-based algorithm shows some advantages and suggests a further step to decode Raman spectrum matrices towards machine learning.

Photoluminescence-Based Techniques for the Detection of Micro- and Nanoplastics

The growing numbers related to plastic pollution are impressive, with ca. 70 % of produced plastic (>350 tonnes/year) being indiscriminately wasted in the environment. The most dangerous forms of plastic pollution for biota and human health are micro‐ and nano‐plastics (MNPs), which are ubiquitous and more bioavailable. Their elimination is extremely difficult, but the first challenge is their detection since existing protocols are unsatisfactory for microplastics and mostly absent for nanoplastics. After a discussion of the state of the art for MNPs detection, we specifically revise the techniques based on photoluminescence that represent very promising solutions for this problem. In this context, Nile Red staining is the most used strategy and we show here its pros and limitations, but we also discuss other more recent approaches, such as the use of fluorogenic probes based on perylene‐bisimide and on fluorogenic hyaluronan nanogels, with the added values of biocompatibility and water solubility.

Label-free identification of microplastics in human cells: dark-field microscopy and deep learning study

The development of an automatic method of identifying microplastic particles within live cells and organisms is crucial for high-throughput analysis of their biodistribution in toxicity studies. State-of-the-art technique in the data analysis tasks is the application of deep learning algorithms. Here, we propose the approach of polystyrene microparticle classification differing only in pigmentation using enhanced dark-field microscopy and a residual neural network (ResNet). The dataset consisting of 11,528 particle images has been collected to train and evaluate the neural network model. Human skin fibroblasts treated with microplastics were used as a model to study the ability of ResNet for classifying particles in a realistic biological experiment. As a result, the accuracy of the obtained classification algorithm achieved up to 93% in cell samples, indicating that the technique proposed will be a potent alternative to time-consuming spectral-based methods in microplastic toxicity research.

Chemical Analysis of Microplastics and Nanoplastics: Challenges, Advanced Methods, and Perspectives

Microplastics and nanoplastics have become emerging particulate anthropogenic pollutants and rapidly turned into a field of growing scientific and public interest. These tiny plastic particles are found in the environment all around the globe as well as in drinking water and food, raising concerns about their impacts on the environment and human health. To adequately address these issues, reliable information on the ambient concentrations of microplastics and nanoplastics is needed. However, micro- and nanoplastic particles are extremely complex and diverse in terms of their size, shape, density, polymer type, surface properties, etc. While the particle concentrations in different media can vary by up to 10 orders of magnitude, analysis of such complex samples may resemble searching for a needle in a haystack. This highlights the critical importance of appropriate methods for the chemical identification, quantification, and characterization of microplastics and nanoplastics. The present article reviews advanced methods for the representative mass-based and particle-based analysis of microplastics, with a focus on the sensitivity and lower-size limit for detection. The advantages and limitations of the methods, and their complementarity for the comprehensive characterization of microplastics are discussed. A special attention is paid to the approaches for reliable analysis of nanoplastics. Finally, an outlook for establishing harmonized and standardized methods to analyze these challenging contaminants is presented, and perspectives within and beyond this research field are discussed.

Capture and characterisation of microplastics printed on paper via laser printer's toners

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.