Stimulated Raman microspectroscopy as a new method to classify microfibers from environmental samples

Fast compressive Raman micro-spectroscopy to image and classify microplastics from natural marine environment

The fast and reliable detection of micron-sized plastic particles from the natural marine environment is an important topic that is mostly addressed using spontaneous Raman spectroscopy. Due to the long (>tens of ms) integration time required to record a viable Raman signal, measurements are limited to a single point per microplastic particle or require very long acquisition times (up to tens of hours). In this work, we develop, validate, and demonstrate a compressive Raman technology using binary spectral filters and single-pixel detection that can image and classify six types of marine microplastic particles over an area of 1 mm2 with a pixel dwell time down to 1.75 ms/pixel and a spatial resolution of 1 µm. This is x10-100 faster than reported in previous studies.

Identification of marine microplastics based on laser-induced fluorescence and principal component analysis

Although the laser-induced fluorescence method shows great potential for microplastic particle detection, overlapping fluorescence signals make accurate type and proportion identification difficult. This paper presents the identification of marine microplastics based on laser-induced fluorescence and principal component analysis. This method works by measuring the fluorescence spectra of water-containing microplastic samples irradiated with a 405-nm laser, which are then analyzed using the principal component analysis (PCA) method. The nine types of microplastics were differentiated based on their positions in the PCA score plot. The mixed sample was positioned between the pure microplastic samples. The component ratio determines its position relative to that of the pure microplastic samples. The first two principal components of the mixed microplastics were linearly dependent. Natural seawater had less influence on the detection, and a mass concentration as low as 0.03% was detected.

Limits of the detection of microplastics in fish tissue using stimulated Raman scattering microscopy

We demonstrate the detection sensitivity of microplastic beads within fish tissue using stimulated Raman scattering (SRS) microscopy. The intrinsically provided chemical contrast distinguishes different types of plastic compounds within fish tissue. We study the size-dependent signal-to-noise ratio of the microplastic beads and determine a lower boundary for the detectable size. Our findings demonstrate how SRS microscopy can serve as a complementary modality to conventional Raman scattering imaging in order to detect and identify microplastic particles in fish tissue.

A review on methods for extracting and quantifying microplastic in biological tissues

Literature about the occurrence of microplastic in biological tissues has increased over the last few years. This review aims to synthesis the evidence on the preparation of biological tissues, chemical identification of microplastic and accumulation in tissues. Several microplastic's extraction approaches from biological tissues emerged (i.e., alkaline, acids, oxidizing and enzymatic). However, criteria used for the selection of the extraction method have yet to be clarified. Similarly, analytical methodologies for chemical identification often does not align with the size of particles. Furthermore, sizes of microplastics found in biological tissues are likely to be biologically implausible, due to the size of the biological barriers. From this review, it emerged that further assessment are required to determine whether microplastic particles were truly internalized, were in the vasculature serving these organs, or were an artefact of the methodological process. The importance of a standardisation of quality control/quality assurance emerged. Findings arose from this review could have a broad implication, and could be used as a basis for further investigations, to reduce artifact results and clearly assess the fate of microplastics in biological tissues.

Stimulated Raman Scattering Microscopy Reveals Bioaccumulation of Small Microplastics in Protozoa from Natural Waters

Microplastics (MPs) are pollutants of global concern, and bioaccumulation determines their biological effects. Although microorganisms form a large fraction of our ecosystem's biomass and are important in biogeochemical cycling, their accumulation of MPs has never been confirmed in natural waters because current tools for field biological samples can detect only MPs > 10 μm. Here, we show that stimulated Raman scattering microscopy (SRS) can image and quantify the bioaccumulation of small MPs (<10 μm) in protozoa. Our label-free method, which differentiates MPs by their SRS spectra, detects individual and mixtures of different MPs (e.g., polyethylene, polypropylene, polyvinyl chloride, polyethylene terephthalate, polystyrene, and poly(methyl methacrylate)) in protozoa. The ability of SRS to quantify cellular MP accumulation is similar to that of flow cytometry, a fluorescence-based method commonly used to determine cellular MP accumulation. Moreover, we discovered that protozoa in water samples from Yangtze River, Xianlin Wastewater Treatment Plant, Lake Taihu and the Pearl River Estuary accumulated MPs < 10 μm, but the proportion of MP-containing cells was low (∼2-5%). Our findings suggest that small MPs could potentially enter the food chain and transfer to organisms at higher trophic levels, posing environmental and health risks that deserve closer scrutiny.

Rapid single-particle chemical imaging of nanoplastics by SRS microscopy

Plastics are now omnipresent in our daily lives. The existence of microplastics (1 µm to 5 mm in length) and possibly even nanoplastics (<1 μm) has recently raised health concerns. In particular, nanoplastics are believed to be more toxic since their smaller size renders them much more amenable, compared to microplastics, to enter the human body. However, detecting nanoplastics imposes tremendous analytical challenges on both the nano-level sensitivity and the plastic-identifying specificity, leading to a knowledge gap in this mysterious nanoworld surrounding us. To address these challenges, we developed a hyperspectral stimulated Raman scattering (SRS) imaging platform with an automated plastic identification algorithm that allows micro-nano plastic analysis at the single-particle level with high chemical specificity and throughput. We first validated the sensitivity enhancement of the narrow band of SRS to enable high-speed single nanoplastic detection below 100 nm. We then devised a data-driven spectral matching algorithm to address spectral identification challenges imposed by sensitive narrow-band hyperspectral imaging and achieve robust determination of common plastic polymers. With the established technique, we studied the micro-nano plastics from bottled water as a model system. We successfully detected and identified nanoplastics from major plastic types. Micro-nano plastics concentrations were estimated to be about 2.4 ± 1.3 × 105 particles per liter of bottled water, about 90% of which are nanoplastics. This is orders of magnitude more than the microplastic abundance reported previously in bottled water. High-throughput single-particle counting revealed extraordinary particle heterogeneity and nonorthogonality between plastic composition and morphologies; the resulting multidimensional profiling sheds light on the science of nanoplastics.

Uptake and transport of micro/nanoplastics in terrestrial plants: Detection, mechanisms, and influencing factors

The pervasive dispersion of micro/nanoplastics in various environmental matrices has raised concerns regarding their potential intrusion into terrestrial ecosystems and, notably, plants. In this comprehensive review, we focus on the interaction between these minute plastic particles and plants. We delve into the current methodologies available for detecting micro/nanoplastics in plant tissues, assess the accumulation and distribution of these particles within roots, stems, and leaves, and elucidate the specific uptake and transport mechanisms, including endocytosis, apoplastic transport, crack-entry mode, and stomatal entry. Moreover, uptake and transport of micro/nanoplastics are complex processes influenced by multiple factors, including particle size, surface charge, mechanical properties, and physiological characteristics of plants, as well as external environmental conditions. In conclusion, this review paper provided valuable insights into the current understanding of these mechanisms, highlighting the complexity of the processes and the multitude of factors that can influence them. Further research in this area is warranted to fully comprehend the fate of micro/nanoplastics in plants and their implications for environmental sustainability.

Atmospheric microfibrous deposition over the Eastern Red Sea coast

The transport of microplastics through the atmosphere has been acknowledged as a significant route for their dispersion across different environments. Microplastics of fibrous shape often prevail in environmental samples, although their composition identification might be challenging and lead to an overestimation of plastic microfibers (MFs). Conversely, MFs of natural origin are rarely reported in microplastics studies despite the lack of consensus on the risks they may pose to the environment. In this study, airborne MFs collected in a sparsely populated residential area on the shore of the Eastern Red Sea were analyzed to investigate their abundance and polymer composition and assess their potential transport and deposition rates. The length of observed fibers ranged from 183 μm to 11,877 μm, with 3 % of fibers being >5 mm. The average length of MFs (< 5 mm) was 1378 ± 934 μm. Plastic MFs comprised 10 % of all identified MFs, with polyester being the most common plastic polymer (81.25 %). The mean abundance of airborne MFs was 0.9 ± 0.8 × 10-2 MFs m-3. The estimated mean atmospheric microfibrous deposition was 70 MFs m-2 d-1, with a component of 8 plastic MFs m-2 d-1. Based on the HYSPLIT backward trajectory analysis, fibers of local origin (estimated to travel approximately 25 km before sampling) were deposited at the sampling location. Air masses of northwestern origin traveling along the coast of the Eastern Red Sea dominated, potentially reducing the abundance of airborne MFs.

Classification of household microplastics using a multi-model approach based on Raman spectroscopy

The extensive use of plastics leads to the release and diffusion of microplastics. Household plastic products occupy a large part and are closely related to daily life. Due to the small size and complex composition of microplastics, it is challenging to identify and quantify microplastics. Therefore,a multi-model machine learning approach was developed for classification of household microplastics based on Raman spectroscopy. In this study, Raman spectroscopy and machine learning algorithm are combined to realize the accurate identification of seven standard microplastic samples, real microplastics samples and real microplastic samples post-exposure to environmental stresses. Four single-model machine learning methods were used in this study, including Support vector machine (SVM), K-nearest neighbor (KNN), Linear discriminant analysis (LDA), and Multi-layer perceptron (MLP) model. The principal components analysis (PCA) was utilized before SVM, KNN and LDA. The classification effect of four models on standard plastic samples is over 88%, and reliefF algorithm was used to distinguish HDPE and LDPE samples. A multi-model is proposed based on four single models including PCA-LDA, PCA-KNN and MLP. The recognition accuracy of multi-model for standard microplastic samples, real microplastic samples and microplastic samples post-exposure to environmental stresses is over 98%. Our study demonstrates that the multi-model coupled with Raman spectroscopy is a valuable tool for microplastic classification.

Modified glucose as a sensor to track the metabolism of individual living endothelial cells - Observation of the 1602 cm−1 band called “Raman spectroscopic signature of life”

A relatively new approach to subcellular research is Raman microscopy with the application of sensors called Raman probes. This paper describes the use of the sensitive and specific Raman probe, 3-O-propargyl-d-glucose (3-OPG), to track metabolic changes in endothelial cells (ECs). ECs play a significant role in a healthy and dysfunctional state, the latter is correlated with a range of lifestyle diseases, particularly with cardiovascular disorders. The metabolism and glucose uptake may reflect the physiopathological conditions and cell activity correlated with energy utilization. To study metabolic changes at the subcellular level the glucose analogue, 3-OPG was used, which shows a characteristic and intense Raman band at 2124 cm^-1.3-OPG was applied as a sensor to track both, its accumulation in live and fixed ECs and then metabolism in normal and inflamed ECs, by employing two spectroscopic techniques, i.e. spontaneous and stimulated Raman scattering microscopies. The results indicate that 3-OPG is a sensitive sensor to follow glucose metabolism, manifested by the Raman band of 1602 cm^-1. The 1602 cm^-1 band has been called the "Raman spectroscopic signature of life" in the cell literature, and here we demonstrate that it is attributed to glucose metabolites. Additionally, we have shown that glucose metabolism and its uptake are slowed down in the cellular inflammation. We showed that Raman spectroscopy can be classified as metabolomics, and its uniqueness lies in the fact that it allows the analysis of the processes of a single living cell. Gaining further knowledge on metabolic changes in the endothelium, especially in pathological conditions, may help in identifying markers of cellular dysfunction, and more broadly in cell phenotyping, better understanding of the mechanism of disease development and searching for new treatments.

Review of research on migration, distribution, biological effects, and analytical methods of microfibers in the environment

Microplastics have been proven to be one of the critical environmental pollution issues. Moreover, microfibers, the most prominent form of microplastics in the environment, have likewise attracted the attention of various countries. With the increase in global population and industrialization, the production and use of fibers continue to increase yearly. As a result, a large number of microfibers are formed. If fiber products are not used or handled correctly, it will cause direct/indirect severe microfiber environmental pollution. Microfibers will be further broken into smaller fiber fragments when they enter the natural environment. Presently, researchers have conducted extensive research in the identification of microfibers, laying the foundation for further resourcefulness research. This work used bibliometric analysis to review the microfiber contamination researches systematically. First, the primary sources of microfibers and the influencing factors are analyzed. We aim to summarize the influence of the clothing fiber preparation and care processes on microfiber formation. Then, this work elaborated on the migration in/between water, atmosphere, and terrestrial environments. We also discussed the effects of microfiber on ecosystems. Finally, microfibers' current and foreseeable effective treatment, disposal, and resource utilization methods were explained. This paper will provide a structured reference for future microfiber research.

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.

Analysis of Microplastics in Aquatic Shellfish by Pyrolysis-Gas Chromatography/Mass Spectrometry after Alkali Digestion and Solvent Extraction

Microplastics are harmful to both marine life and humans. Herein, a pyrolysis-gas chromatography-mass spectrometry (Py-GC/MS) technique for the detection of microplastics in aquatic shellfish is demonstrated. The organic matter in aquatic shellfish was removed by alkali digestion. Subsequently, using hexafluoroisopropanol as the extraction solvent, the extraction method was optimized. The influence of the digestion process on the nature of microplastics was investigated by analyzing the samples before and after the alkali treatment via infrared spectrometry, laser particle sizing, and scanning electron microscopy. Spiked recovery experiments and an analysis of actual samples were performed using PA6 and PA66 as analytes. A quantitative analysis of the characteristic ion fragment produced by high-temperature cracking was performed after chromatographic separation and mass spectrometry identification. The linear range of this method for PA6 and PA66 was 2-64 μg. The limits of detection of PA6 and PA66 were 0.2 and 0.6 μg, while the limits of quantitation were 0.6 and 2.0 μg, respectively. Recovery ranged from 74.4 to 101.62%, with a precision of 4.53-7.56%. The results suggest that the Py-GC/MS technique is suitable for the analysis and detection of trace microplastics in aquatic shellfish.

Quantification and morphological characterization of microfibers emitted from textile washing

Microplastics are a subject of growing interest as they are a potential threat for living organisms. Textile microfibers (MFs) are an important microplastics sub-group that have been reported as a major source of microplastics release into the environment. This pollution occurs mainly during the washing of synthetic garments. However, standardized methods to quantify and characterize these MFs are scarce. This study proposes a new analytical protocol to characterize these MFs in number and size by means of filtration techniques, optical and electronic microscopy and automatic image post-processing. This approach was developed and validated on effluents from washing machines produced in different conditions (5 different garments, sequential cycles, and presence or not of detergent). Among the analyzed effluents, it was found that 40 to 75% of microfibers have a length comprised between 50 and 200 μm, with average microfiber diameters ranging from 8 to 17 μm depending on the type of textile. The emission range of microfibers was estimated to be between 220,000 to 2,820,000 microfibers per kg of textile depending on the type of garment and the washing conditions. The counting method developed is adapted to a certain range of textiles, such as 100% polyester fleece jackets (PET-1), 100% smooth polyester T-shirt (PET-2) and 100% acrylic sweater (PAN), and is not affected by the presence of detergent. The proposed method of characterization of these MFs lengths can also be extrapolated to the counting of other objects that have a similar morphology to the analyzed fibers. Hence, it can be helpful to develop new testing capture technologies and, thus, contribute to the enhancement of filtering techniques of several pollutants.

Advanced microplastic monitoring using Raman spectroscopy with a combination of nanostructure-based substrates

Micro(nano)plastic (MNP) pollutants have not only impacted human health directly, but are also associated with numerous chemical contaminants that increase toxicity in the natural environment. Most recent research about increasing plastic pollutants in natural environments have focused on the toxic effects of MNPs in water, the atmosphere, and soil. The methodologies of MNP identification have been extensively developed for actual applications, but they still require further study, including on-site detection. This review article provides a comprehensive update on the facile detection of MNPs by Raman spectroscopy, which aims at early diagnosis of potential risks and human health impacts. In particular, Raman imaging and nanostructure-enhanced Raman scattering have emerged as effective analytical technologies for identifying MNPs in an environment. Here, the authors give an update on the latest advances in plasmonic nanostructured materials-assisted SERS substrates utilized for the detection of MNP particles present in environmental samples. Moreover, this work describes different plasmonic materials-including pure noble metal nanostructured materials and hybrid nanomaterials-that have been used to fabricate and develop SERS platforms to obtain the identifying MNP particles at low concentrations. Plasmonic nanostructure-enhanced materials consisting of pure noble metals and hybrid nanomaterials can significantly enhance the surface-enhanced Raman scattering (SERS) spectra signals of pollutant analytes due to their localized hot spots. This concise topical review also provides updates on recent developments and trends in MNP detection by means of SERS using a variety of unique materials, along with three-dimensional (3D) SERS substrates, nanopipettes, and microfluidic chips. A novel material-assisted spectral Raman technique and its effective application are also introduced for selective monitoring and trace detection of MNPs in indoor and outdoor environments.

Stimulated Raman scattering microscopy in chemistry and life science - Development, innovation, perspectives

In this review, we present a summary of the basics of the Stimulated Raman Scattering (SRS) phenomenon, methods of detecting the signal, and collection of the SRS images. We demonstrate the advantages of SRS imaging, and recent developments, but also the limitations, especially in image capture speeds and spatial resolution. We also compare the use of SRS microscopy in biological system studies with other techniques such as fluorescence microscopy, second-harmonic generation (SHG)-based microscopy, coherent anti-Stokes Raman scattering (CARS), and spontaneous Raman, and we show the compatibility of SRS-based systems with other discussed methods. The review is also focused on indicating innovations in SRS microscopy, on the background of which we present the layout and performance of our homemade setup built from commercially available elements enabling for imaging of the molecular structure of single cells over the spectral range of 800-3600 cm^-1. Methods of image analysis are discussed, including machine learning methods for obtaining images of the distribution of selected molecules and for the detection of pathological lesions in tissues or malignant cells in the context of clinical diagnosis of a wide range of diseases with the use of SRS microscopy. Finally, perspectives for the development of SRS microscopy are proposed.

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.

Label-Free Live-Cell Imaging of Internalized Microplastics and Cytoplasmic Organelles with Multicolor CARS Microscopy

As the bioaccumulation of microplastics (MPs) is considered as a potential health risk, many efforts have been made to understand the cellular dynamics and cytotoxicity of MPs. Here, we demonstrate that label-free multicolor coherent anti-Stokes Raman scattering (CARS) microscopy enables separate vibrational imaging of internalized MPs and lipid droplets (LDs) with indistinguishable shapes and sizes in live cells. By simultaneously obtaining polystyrene (PS)- and lipid-specific CARS images at two very different frequencies, 1000 and 2850 cm^-1, respectively, we successfully identify the local distribution of ingested PS beads and native LDs in Caenorhabditis elegans. We further show that the movements of PS beads and LDs in live cells can be separately tracked in real time, which allows us to characterize their individual intracellular dynamics. We thus anticipate that our multicolor CARS imaging method could be of great use to investigate the cellular transport and cytotoxicity of MPs without additional efforts for pre-labeling to MPs.

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.

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.

Habitat-forming species trap microplastics into coastal sediment sinks

Nearshore biogenic habitats are known to trap sediments, and may therefore also accumulate biofouled, non-buoyant microplastics. Using a current-generating field flume (TiDyFLOW), we experimentally assessed the mechanisms of microplastic trapping of two size classes, 0.5 mm and 2.5 mm particle size, by three contrasting types of biogenic habitats: 1) seagrasses, 2) macroalgae, and 3) scleractinian corals. Results showed that benthic organisms with a complex architecture and rough surface - such as hard corals - trap the highest number of microplastics in their aboveground structure. Sediment was however the major microplastic sink, accumulating 1 to 2 orders of magnitude more microplastics than the benthic structure. Microplastic accumulation in the sediment could be explained by near-bed turbulent kinetic energy (TKE), indicating that this is governed by the same hydrodynamic processes leading to sediment trapping. Thus, the most valuable biogenic habitats in terms of nursery and coastal protection services also have the highest capacity of accumulating microplastics in their sediments. A significantly larger fraction of 0.5 mm particles was trapped in the sediment compared to 2.5 mm particles, because especially the smaller microplastics are entrained into the sediment. Present observations contribute to explaining why especially microplastics smaller than 1 mm are missing in surface waters.

New Insights into the Microplastic Enrichment in the Blue Carbon Ecosystem: Evidence from Seagrass Meadows and Mangrove Forests in Coastal South China Sea

Microplastics were recently found to aggregate in the blue carbon ecosystems (BCEs), which are known for their ability to store carbon by slowing down the water flow. However, evidence is largely lacking on how the accumulation of microplastics is related to carbon sequestration in BCEs and if this trap effect is driven by its biological characteristics. In this study, the trap effect of microplastics by BCEs was evaluated for various seagrasses (Zostera japonica, Halophila ovalis, and Halophila beccarii) and mangroves (Aegiceras corniculatum and Avicennia marina). Significant accumulation was found in the seagrass meadow dominated by H. beccarii and the mangrove forest dominated by A. marina, with microplastics enriched by 1.3 to 17.6 times compared to their corresponding unvegetated sites. The abundance of microplastics varied greatly from 17.68 ± 8.10 to 611.75 ± 81.52 particles per kg of dry sediment, with the highest abundance in A. marina mangrove sediments. A strong positive correlation was found between the abundance of microplastics and the particulate organic carbon content at all study sites (Pearson, R = 0.86, p < 0.01). Higher diversity of microplastic colors and size was found in the H. beccarii meadow, and higher diversity of shapes was found in the A. marina forest. Our results added new insights to the understanding of the mechanism of microplastic trapping by BCEs and coupled the behavior of microplastics with the organic carbon in the sediment.

Hadamard-transform spectral acquisition with an acousto-optic tunable filter in a broadband stimulated Raman scattering microscope

We present a novel configuration for high spectral resolution multiplexing acquisition based on the Hadamard transform in stimulated Raman scattering (SRS) microscopy. The broadband tunable output of a dual-beam femtosecond laser is filtered by a fast, narrowband, and multi-channel acousto-optic tunable filter (AOTF). By turning on and off different subsets of its 8 independent channels, the AOTF generates the spectral masks given by the Hadamard matrix. We demonstrate a seamless and automated operation in the Raman fingerprint and CH-stretch regions. In the presence of additive noise, the spectral measurements using the multiplexed method show the same signal-to-noise ratio of conventional single-wavenumber acquisitions performed with 4 times longer integration time.