Direct identification and visualisation of real-world contaminating microplastics using Raman spectral mapping with multivariate curve resolution-alternating least squares

Analysis of spatiotemporal changes mechanism of cell wall biopolymers and monosaccharide components in kiwifruit during Botryosphaeria dothidea infection

To further reveal the interaction mechanism between plants and pathogens, this study used confocal Raman microscopy spectroscopy (CRM) combined with chemometrics to visualize the biopolymers distribution of kiwifruit cell walls at different infection stages at the cellular micro level. Simultaneously, the changes in the content of various monosaccharides in fruit were studied at the molecular level using high-performance liquid chromatography (HPLC). There were significant differences in the composition of various nutrient components in the cell wall structure of kiwifruit at different infection times after infection by Botryosphaeria dothidea. PCA could cluster samples with infection time of 0-9 d into different infection stages, and SVM was used to predict the PCA classification results, the accuracy >96 %. Multivariate curve resolution-alternating least squares (MCR-ALS) helped to identify single substance spectra and concentration signals from mixed spectral signals. The pure substance chemical imaging maps of low methylated pectin (LMP), high methylated pectin (HMP), cellulose, hemicellulose, and lignin were obtained by analyzing the resolved concentration data. The imaging results showed that the lignin content in the kiwifruit cell wall increased significantly to resist pathogens infection after the infection of B. dothidea. With the development of infection, B. dothidea decomposed various substances in the host cell walls, allowing them to penetrate the interior of fruit cells. This caused significant changes in the form, structure, and distribution of various chemicals on the fruit cell walls in time and space. HPLC showed that glucose was the main carbon source and energy substance obtained by pathogens from kiwifruit during infection. The contents of galactose and arabinose, which maintained the structure and function of the fruit cell walls, decreased significantly and the cell wall structure was destroyed in the late stage of pathogens infection. This study provided a new perspective on the cellular structure changes caused by pathogenic infection of fruit and the defense response process of fruit and provided effective references for further research on the mechanisms of host-pathogen interactions in fruit infected by pathogens.

Surface-Enhanced Raman Scattering Imaging Assisted by Machine Learning Analysis: Unveiling Pesticide Molecule Permeation in Crop Tissues

Surface-enhanced Raman scattering (SERS) imaging technology faces significant technical bottlenecks in ensuring balanced spatial resolution, preventing image bias induced by substrate heterogeneity, accurate quantitative analysis, and substrate preparation that enhances Raman signal strength on a global scale. To systematically solve these problems, artificial intelligence techniques are applied to analyze the signals of pesticides based on 3D and dynamic SERS imaging. Utilizing perovskite/silver nanoparticles composites (CaTiO3/Ag@BONPs) as enhanced substrates, enabling it not only to cleanse pesticide residues from the surface to pulp of fruits and vegetables, but also to investigate the penetration dynamics of an array of pesticides (chlorpyrifos, thiabendazole, thiram, and acetamiprid). The findings challenge existing paradigms, unveiling a previously unnoticed weakening process during pesticide invasion and revealing the surprising permeability of non-systemic pesticides. Of particular note is easy to overlook that the combined application of pesticides can inadvertently intensify their invasive capacity due to pesticide interactions. The innovative study delves into the realm of pesticide penetration, propelling a paradigm shift in the understanding of food safety. Meanwhile, this strategy provides strong support for the cutting-edge application of SERS imaging technology and also brings valuable reference and enlightenment for researchers in related fields.

Toy building bricks as a potential source of microplastics and nanoplastics

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

Development of a multi-spectroscopy method coupling μ-FTIR and μ-Raman analysis for one-stop detection of microplastics in environmental and biological samples

Current techniques for microplastics (MPs) analysis are diverse. However, most techniques have individual limitations like the detection limit of spatial resolution, susceptibility, high cost, and time-consuming detection. In this study, we proposed a multi-spectroscopy method coupling μ-FTIR and μ-Raman analysis for one-stop MPs detection, in which barium fluoride was used as the substrate alternative to the filter membrane. Compared with commonly used filter membranes (alumina, silver, PTFE and nylon membranes), the barium fluoride substrate showed better spectroscopic detection performance on microscopic observation, broader transmittable wavenumber range for μ-FTIR (750-4000 cm-1) and μ-Raman (250-4000 cm-1) detection, thus suitable for the multi-spectroscopy analysis of spiked samples. Further, the real environmental and biological samples (indoor air, bottled water and human exhaled breath) were collected and detected to verify the applicability of the developed multi-spectroscopy method. The results indicated that the average content of detected MPs could be increased by 30.4 ± 29.9 % for indoor air, 17.1 ± 13.2 % for bottled water and 38.4 ± 16.0 % for human exhaled breath, respectively in comparison with widely used μ-Raman detection, which suggested that MPs exposure might be underestimated using single spectroscopy detection. Moreover, the majority of underestimated MPs were colored and smaller sized (<50 μm) MPs, which could pose higher risks to human body. In addition, the proposed method consumed lower sample pre-treatment costs and was environmental-friendly since the barium fluoride substrate could be used repeatedly after being cleaned by organic solvent with reliable results (n = 10, CV = 10 %, ICC = 0.961), which reduced the cost of MPs detection by at least 2.49 times compared with traditional methods using silver membrane.

Identification and visualization of environmental microplastics by Raman imaging based on hyperspectral unmixing coupled machine learning

Microplastics (MPs) are ubiquitous contaminants that have become an emerging pollutant of concern, potentially threatening human health and ecosystem environments. Although current detection methods can accurately identify various types of MPs, it remains necessary to develop non-destructive and rapid methods to meet growing demands for detection. Herein, we combine a hyperspectral unmixing method and machine learning to analyse Raman imaging data of environmental MPs. Five MPs types including poly(butylene adipate-co-terephthalate) (PBAT), poly(butylene succinate) (PBS), p-polyethylene (PE), polystyrene (PS) and polypropylene (PP) were visualized and identified. Individual or mixed pure or aged MPs along with environmental samples were analysed by Raman imaging. Alternating volume maximization (AVmax) combined with unconstrained least squares (UCLS) method estimated end members and abundance maps of each of the MPs in the samples. Pearson correlation coefficients (r) were used as the evaluation index; the results showed that there is a high similarity between the raw spectra and the average spectra calculated by AVmax. This indicates that Raman imaging based on machine learning and hyperspectral unmixing is a novel imaging analysis method that can directly identify and visualize MPs in the environment.

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.

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.

Suspected sources of microplastics and nanoplastics: Contamination from experimental reagents and solvents

There is an increasing concern about the potential effects of microplastics (MPs) and nanoplastics on human health and other organisms. For the separation and detection of MPs, there are various approaches, and the distinct procedures led to different results. However, the presence of MPs in the reagents was not addressed, which could cause false and/or inaccurate results during MPs detection. In this study, the chemical reagents commonly used for the separation and detection of MPs were selected to ascertain whether these reagents introduce MPs. It was shown that a large number of MPs were detected in the reagent and solvent samples. The largest number of MPs (>1 μm) was detected in the KOH reagent, with the abundance of 3070 items/g. The order of MPs abundance in the selected reagents was: KOH > NaCl > CaCl2 > SDS > NaI > H2O2. The types of MPs were the same as the body and stopper of the reagent packaging bottles. MPs size detected in reagent bottles was primarily smaller than 10 μm. The abundance of MPs in the reagents were independent of their purity, however, there was a certain difference in MPs abundance in reagents from different manufacturers. Furthermore, the presence of nanoplastics (< 1 μm) was verified in the reagents through Py-GCMS, with the abundance (39.47-43.01 mg/kg) higher than that of MPs. The obtained results in this study raised specific requirements and cautions for MPs and nanoplastics related research in terms of quality control. Also, this work can facilitate a more accurate assessment of MPs concentrations in the environment.

Visual detection of microplastics using Raman spectroscopic imaging

As a new type of pollutant in the marine environment and terrestrial ecosystems, microplastics have attracted widespread attention. Assessing the ecological risk of microplastics relies on accurately detecting small-sized particles in the environment. Microplastics exhibit unique "fingerprint" characteristics in Raman spectroscopy, making them suitable for rapid identification. In this study, we achieved visualization of microplastics through pseudo-color images generated by Raman spectroscopy imaging. Pseudo-color imaging maps were generated by selecting characteristic peaks and the classical least-squares fitting method was used to visually represent the distribution of different microplastics. The study explored the potential of Raman spectroscopy and its mapping mode in distinguishing various types of mixed microplastics and demonstrated that this approach can identify microplastics in complex environmental samples. Specifically, a cloud-point extraction followed by membrane filtration method was successfully applied to identifying mixed-component microplastics. In summary, the category, quantity, location, and differentiation of microplastics can be accurately analyzed by Raman spectroscopy, which provides a basis for assessing their ecological risk.

A beaker method for determination of microplastic concentration by micro-Raman spectroscopy

Fourier-transform infrared (FT-IR) spectroscopy method for measuring small microplastic (SMP) concentration in marine environment is time-consuming and labor-intensive due to sample pre-treatment. In contrast, Raman spectroscopy is less influenced by water and can directly measure SMP samples in water, making it a more efficient method to measure SMP concentration. Therefore, a method that can directly estimate the concentration of SMPs in water was developed, and the relationship between SMP concentration and experimental Raman spectra were established by testing with standard polyethylene (PE) samples. It was found that average spectra acquired in water solution could reflect characteristic peaks of the plastic after baseline correction. Further investigation found that there is a significant functional relationship between correlation coefficient of sample spectra and the concentration of PE particles, and such relationship can be modelled by Langmuir model. The empirical functional relationships can be used to estimate SMP concentrations by measuring average Raman spectra. The developed methodology is helpful for developing rapid SMP identification and monitoring methods in a more complex manner.•A method of directly measuring MP concentration in water is proposed.•Experimental procedures are provided.•Data analysis methods are outlined.

Worker studies suggest unique liver carcinogenicity potential of polyvinyl chloride microplastics

BACKGROUND

Plastic debris pervades our environment. Some breaks down into microplastics (MPs) that can enter and distribute in living organisms causing effects in multiple target organs. MPs have been demonstrated to harm animals through environmental exposure. Laboratory animal studies are still insufficient to evaluate human impact. And while MPs have been found in human tissues, the health effects at environmental exposure levels are unclear.

AIM

We reviewed and summarized existing evidence on health effects from occupational exposure to MPs. Additionally, the diverse effects documented for workers were organized by MP type and associated co-contaminants. Evidence of the unique effects of polyvinyl chloride (PVC) on liver was then highlighted.

METHODS

We conducted two stepwise online literature reviews of publications focused on the health risks associated with occupational MP exposures. This information was supplemented with findings from animal studies.

RESULTS

Our analysis focused on 34 published studies on occupational health effects from MP exposure with half involving exposure to PVC and the other half a variety of other MPs to compare. Liver effects following PVC exposure were reported for workers. While PVC exposure causes liver toxicity and increases the risk of liver cancers, including angiosarcomas and hepatocellular carcinomas, the carcinogenic effects of work-related exposure to other MPs, such as polystyrene and polyethylene, are not well understood.

CONCLUSION

The data supporting liver toxicity are strongest for PVC exposure. Overall, the evidence of liver toxicity from occupational exposure to MPs other than PVC is lacking. The PVC worker data summarized here can be useful in assisting clinicians evaluating exposure histories from PVC exposure and designing future cell, animal, and population exposure-effect research studies.

A review of recent progress in the application of Raman spectroscopy and SERS detection of microplastics and derivatives

The global environmental concern surrounding microplastic (MP) pollution has raised alarms due to its potential health risks to animals, plants, and humans. Because of the complex structure and composition of microplastics (MPs), the detection methods are limited, resulting in restricted detection accuracy. Surface enhancement of Raman spectroscopy (SERS), a spectral technique, offers several advantages, such as high resolution and low detection limit. It has the potential to be extensively employed for sensitive detection and high-resolution imaging of microplastics. We have summarized the research conducted in recent years on the detection of microplastics using Raman and SERS. Here, we have reviewed qualitative and quantitative analyses of microplastics and their derivatives, as well as the latest progress, challenges, and potential applications.

Development of automated microplastic identification workflow for Raman micro-imaging and evaluation of the uncertainties during micro-imaging

In this study, an automated identification workflow for Raman micro-imaging (RMI) was developed, and the performance was evaluated by artificial samples of microplastic (MP) microsphere with different sizes and types. Theoretical detection rate and estimated particle size were derived and compared with experimental data. Results show that the proposed workflow can identify plastic types and estimate the size of the MP microspheres under different conditions for most cases. However, size of laser spot and discrepancy between sample surface and focal plane can influence RMI results in two ways. Firstly, small particles are more likely to be detected. Secondly, estimated sizes of particles are more likely to be overestimated. The derived uncertainties can serve as a reference for future experimental design and further investigation of more complex situations. The workflow is accessible online, and interested researchers can adjust the parameter values as necessary to suit their specific circumstances.

Raman spectra characterization of size-dependent aggregation and dispersion of polystyrene particles in aquatic environments

Aqueous environments are generally thought to be a source of pooling and re-distribution for both micro-plastics (MPs) and nano-plastics (NPs); however, significantly less data on NPs than MPs have been reported. The occurrence of salts, proteins, and other organic matter may promote or inhibit the aggregation of NPs to form agglomeration particles, making their detection more difficult. In this study, 80 and 500 nm polystyrene nano-plastics (PS-NPs) modified by four different functional groups (PS-Bare, PS-COOH, PS-NH₂, and PS-CHO-500 nm) were selected to mimic the flocculation and/or sedimentation of NPs in salts (NaCl, CaCl₂, and Na₂SO₄) and protein solutions. The results showed that the 80 nm PS-NPs are only colloidal in pure water. All four strong electrolyte solutions that were tested significantly promoted the aggregation of PS-NPs, including those that were protein-coated. In addition, 500 nm PS-CHO did not flocculate but gradually settled into sedimentation. Therefore, Raman spectrometry can be used to analyze assembled PS-NPs, but is not suitable for analyzing normal PS-NPs. By combining fractal morphology, this study provides insight into the comprehensive analysis of PS-NPs in water solutions, including the digestion of biological samples.

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.

Quantification analysis of microplastics released from disposable polystyrene tableware with fluorescent polymer staining

Ingesting microplastics (MPs) from plastic tableware is an important source of health risk to human bodies. However, the comprehensive information of MPs released from disposable tableware has not been explored. Herein, a new visual quantification method for polystyrene MPs is proposed with carbon nitride fluorescent polymers staining, which can overcome the disadvantages of high signal background and photobleaching derived from organic dyes staining. Combining with fluorescence microscope and ImageJ software, the quantity, shape, and size distribution of MPs carried by the brand-new disposable polystyrene tableware (DPT) samples before usage and released from the clean DPT samples in different simulated usage scenes were studied. The brand-new DPT samples were found to carry a large number of MPs particles and the clean DPT samples could release MPs during usage. Fiber and fragment are the main morphology of the detected MPs and fiber accounts for 45-52 %. The particles with size <50 μm are the majority of the detected MPs and the distribution fraction of MPs particles is gradually decreased with the raising of particle size within 50 μm. The released MPs particles are increased with the raising of contact time and temperature, and greatly boosted for the DPT samples with cracks. The DPT samples are more like to release MPs in weak acidic condition (pH 4.0) than in weak alkaline (pH 8.3) and neutral (pH 7.0) conditions. The obtained results help to assess the food safety of tack-out food and the health risk of MPs exposure to human.

Analysis of microplastics released from plastic take-out food containers based on thermal properties and morphology study

Plastic take-out food containers may release microplastics (MPs) into food and pose a potential risk to food safety and human health. Here, after being subjected to hot water treatment, MPs released from three types of plastic food containers (polypropylene, PP; polyethylene, PE; expanded polystyrene, EPS) were identified by micro-Raman spectroscopy. The results showed that the size of released MPs ranged from 0.8-38 μm and over 96% MPs were smaller than 10 μm. Various MPs concentrations were found from the three types of containers, that is, 1.90 × 10⁴, 1.01 × 10⁵, and 2.82 × 10⁶ particles/L on average from PP, PE, and EPS, respectively. Moreover, based on thermal and morphology analysis, we discovered that both relaxations of the polymer chains in the rubbery state and defects caused by processing techniques might contribute to the release of MPs. Thus, such release can be reduced by increasing the thermal stability of the materials and mitigating the defects generated during production.

Feasibility of diffuser-incorporated near-infrared trans-reflectance measurement for quantitative detection of microplastics captured in perfluorocarbon

A diffuser-incorporated near-infrared (NIR) trans-reflectance measurement is demonstrated for quantitative detection of polyethylene (PE) particles captured in perfluorohexane (PFH, C₆F₁₄). PFH effectively captures PE particles through its hydrophobicity and absorbs little NIR radiation, recommending it for use in background-free NIR detection of captured PE particles. A reflective metal disk was used to push the captured PE particles in PFH toward the bottom of the vial that contained the sample, and the trans-reflectance measurement was performed by illuminating NIR radiation from the bottom of the vial at 45^o. Reproducibility is limited by the variation in the positions of small PE-particle aggregates at the water/PFH interface and the difficulty in ensuring full NIR sampling (coverage) of large aggregates. An effective way to secure improved reproducibility under these circumstances is illumination of broader and more uniform NIR radiation for measurement. For this purpose, a polytetrafluoroethylene (PTFE) disk was uniquely incorporated as a diffuser for the trans-reflectance measurement. Compared to the measurement with no diffuser, the diffuser-incorporated scheme produced more distinct PE peaks of low-quantity samples (0.1 and 0.2 mg) and enhanced the reproducibility in measurements of all the samples (0.1-4.0 mg of PE). As a result, the correlation between peak intensity and particle quantity was excellent (R²: 0.997), and a limit of detection of 0.07 mg was achieved.

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.

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.

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.

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.