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

Improved neural networks for the classification of microplastics via inferior quality Raman spectra

Machine learning algorithms are proficient in the rapid extraction of features for the classification of microplastic Raman spectra. Nevertheless, the classification of Raman spectra from microplastics, particularly in the presence of complex environmental interference, remains a substantial challenge. In this study, an improved ResNet model incorporating the Squeeze-and-Excitation (SE) module is employed for the classification and identification of Raman spectra of microplastics across varying quality levels under diverse experimental conditions with insufficient laser power and short spectrum acquisition time. The improved ResNet model exhibits superior accuracy in classifying inferior quality Raman spectra characterized by significant noise and low signal-to-noise ratios, as compared to traditional CNN, without a considerable escalation in parameter size or computational burden. Even under the most adverse experimental conditions assessed, the model achieved a notable recognition accuracy of 97.83 %. Moreover, the application of Grad-CAM visualization provides insights into the criteria underlying machine learning-based spectral classification. This research underscores the capacity of machine learning algorithms in the analysis and interpretation of inferior quality Raman spectra within complex and non-ideal experimental scenarios.

Machine Learning Advancements and Strategies in Microplastic and Nanoplastic Detection

Microplastics (MPs) and nanoplastics (NPs) present formidable global environmental challenges with serious risks to human health and ecosystem sustainability. Despite their significance, the accurate assessment of environmental MP and NP pollution remains hindered by limitations in existing detection technologies, such as low resolution, substantial data volumes, and prolonged imaging times. Machine learning (ML) provides a promising pathway to overcome these challenges by enabling efficient data processing and complex pattern recognition. This systematic Review aims to address these gaps by examining the role of ML techniques combined with spectroscopy in improving the detection and characterization of NPs. We focused on the application of ML and key tools in MP and NP detection, categorizing the literature into key aspects: (1) Developing tailored strategies for constructing ML models to optimize plastic detection while expanding monitoring capabilities. Emphasis is placed on harnessing the unique molecular fingerprinting capabilities offered by spectroscopy, including both infrared (IR) and Raman spectra. (2) Providing an in-depth analysis of the challenges and issues encountered by current ML approaches for NP detection. This Review highlights the critical role of ML in advancing environmental monitoring and improving our further, deeper investigation of the widespread presence of NPs. By identifying current key challenges, this Review provides valuable insights for future direction in environmental management and public health protection.

Size-matching effects in quantitative detection of PS nanoplastics using controllable and reusable Ag nanoarrays SERS substrates

This study proposes a strategy for the highly sensitive detection of polystyrene nanoplastics (PS NPs) with varying particle sizes. Ag nanoarrays (AgNAs) with different inter-column spacings and heights are fabricated via thermal deposition of Ag in anodized aluminum oxide (AAO) templates. The size-matching effects between PS NPs and the parameters of the AgNAs (inter-column spacing and height) are investigated. Utilizing this size-matching effect, the AgNAs substrate enables sensitive detection of PS NPs with particle sizes of 130 nm, 180 nm, and 230 nm, with limits of detection (LODs) of 10 μg/mL. In real water samples (river water, rainwater, and tap water), the AgNAs substrate also demonstrates good performance, achieving a LOD of 10 μg/mL for detecting 130 nm PS NPs. Additionally, toluene is used to remove PS NPs from the AgNAs surface, allowing the substrate to be reused across multiple cycles. After at least 30 detection cycles, the surface-enhanced Raman scattering (SERS) performance of the AgNAs shows no significant decline, with a relative standard deviation (RSD) of 6.8 %. The AgNAs exhibit excellent stability and reusability in detecting PS NPs, indicating strong potential for practical applications.

Atmospheric emissions of microplastics entrained with dust from potential source regions

Atmospheric microplastics play an important role in the microplastic cycle. However, their behaviors in high-altitude remote areas were still poorly constrained. Based on one year of samples from the northeast Tibetan Plateau, we investigated the status of atmospheric microplastics and their relationships with dust. The results indicated that number-based concentrations of atmospheric microplastics were 4.07 ± 2.37 items m-3 with the maximum in spring, while mass-based concentrations were 0.126 ± 0.152 μg m-3 with the maximum in winter. Atmospheric microplastics < 50 μm accounted for 92.9 %, with 95.4 % being fragments, emphasizing the pervasive occurrence of small-sized fragmented microplastics in the northeast Tibetan Plateau. Analysis of Lagrangian particle dispersion model combined with potential source contributions revealed that dust emission in potential source regions significantly impacted atmospheric microplastic concentrations. The threshold shear velocity of microplastics and dust exhibited similar values, supporting their co-emissions from potential source regions. Once microplastics are entrained into the airflow, the lower updraft wind speed required for microplastic suspension facilitates long-range atmospheric transport. This study enhanced our insights into the atmospheric microplastic sources and supported future mitigation strategies for microplastic exposure in the remote ecosystem.

Systematic quantitation for microplastics and nanoplastics based on size-fractionated filtration hyphenated to Raman/SERS and slope-matching strategy

The issue of micro/nanoplastics has attracted widespread attention. The accurate quantitation of micro/nanoplastics remains challenging due to their heterogeneous size distributions. Herein, a systematic method was proposed that integrates Raman or surface-enhanced Raman spectroscopy (SERS) hyphenated to size-fractionated filtration (SFF-R/S) and a slope-matching strategy, thereby enhancing quantitative accuracy in spectral data acquisition and data handling. Micro/nanoplastics were categorized into four size fractions (>1 μm, 500 nm-1 μm, 50-500 nm, and <50 nm). Raman spectroscopy was employed to analyze larger particles, while SERS was used for 50-500 nm and sub-50 nm nanoplastics. In SFF-R/S, the spectral interferences between fractions were eliminated, thereby improving the accuracy of spectral intensities. In external quantitation, a slope-matching method was used to improve analytical accuracy by estimating particle size. The relative error was < 10 % for single fraction quantitation and < 5 % for mixtures. This systematic method works well with micro/nanoplastics of different polymers and showed a detection limit lowered to 2 × 10-5 g·L-1 for polystyrene (PS) nanoplastics. Its practical utility was validated by the analysis of released micro/nanoplastics from disposable PS cups. This work provides information on chemical components, concentrations, and size distribution of micro/nanoplastics mixtures, which advances our understanding of their environmental behavior and physiological effects.

Machine learning-driven optical microfiltration device for improved nanoplastic sampling and detection in water systems

The rising presence of nanoplastics in water poses toxicity risks and long-term ecological and health impacts. Detecting nanoplastics remains challenging due to their small size, complex chemistry, and environmental interference. Traditional filtration combined with Raman spectroscopy is time-consuming, labor-intensive, and often lacks accuracy and sensitivity. This study presents an agarose-based microfiltration device integrated with machine learning-assisted Raman analysis for nanoplastic capture and identification. The 1 % agarose microfluidic channel features circular micropost arrays enabling dual filtration: nanoplastics diffuse into the porous matrix, while larger particles (>1000 nm) are blocked by the microposts. Unlike conventional systems, this design achieves both physical separation and preconcentration, enhancing nanoplastic detectability. Upon dehydration, the agarose forms a transparent film, significantly improving Raman compatibility by minimizing background interference. This transformation enables direct Raman analysis of retained nanoparticles with enhanced signal clarity and sensitivity. Using 100-nm polystyrene nanoparticles (PSNPs) as a model, we evaluated device performance in distilled water and seawater across concentrations (6.25-50 µg/mL) and flow rates (2.5-100 µL/min). Maximum capture efficiencies of 80 % (seawater) and 66 % (distilled water) were achieved at 2.5 µL/min. A convolutional neural network (CNN) further enhanced spectral analysis, reducing mapping time by 50 % and enabling PSNP detection in seawater at 6.25 µg/mL. This agarose-based system offers a scalable, cost-effective platform for nanoplastic sampling, demonstrating the potential of combining microfluidics with machine learning-assisted Raman spectroscopy to address critical environmental and public health challenges.

Raman Peak Features Matching: Enhancing Spectral Analysis through Feature Augmentation

Raman spectroscopy has emerged as a pivotal technology in modern scientific research and industrial applications, offering nondestructive, high-resolution analysis with robust molecular fingerprinting capabilities. The extraction of Raman spectral features is a critical step in spectral data analysis, directly influencing sample identification, classification, and quantitative outcomes. However, integrating important data features from machine learning models with context-specific biosignatures to derive meaningful insights into spectral analysis remains a significant challenge. Herein, the Raman Peak Feature Matching (RPFM) method is proposed, which matches protein peak features with salient breast cell data features extracted from the machine learning models. Feature augmentation is subsequently applied to the matching-retained breast cell features, thereby enhancing spectral analysis capabilities. The RPFM method is applied to breast cell spectra for feature augmentation with a reclassification accuracy of 97.12% using a linear support vector machine model, achieving an 8.34% improvement over the model's performance without feature augmentation. The RPFM method has also been successfully implemented in generalized linear logistic regression and tree-based eXtreme gradient boosting, demonstrating its versatility across diverse machine learning algorithms. The RPFM method leverages data-driven machine learning models while compensating for the inability to take into account specific specialized background knowledge. This methodology significantly advances the accuracy and efficacy of spectral analysis in biological and medical applications, offering a novel framework for machine learning algorithms to perform augmented Raman spectral analysis.

A HoLDI mass spectrometry platform for airborne nanoplastic detection

There are no established protocols for nanoplastic detection within complex environmental matrices. Mass spectrometry (MS) analysis of environmental nanoplastics is impeded by methodological constraints. We present a versatile platform evolved from matrix-assisted laser desorption/ionization (MALDI) MS for airborne nano/microplastic research. The 3D-printed hollow-laser desorption/ionization (HoLDI) target enables efficient, high-throughput analysis of aerosols collected on simple substrates without pre/post-treatments. HoLDI-MS determines the chemical composition and relative quantity of real-world airborne nano/microplastics, while used with conventional portable samplers and particle analyzers. Polyethylene, polyethylene glycol, and polydimethylsiloxanes are detected in an indoor environment, with a higher amount in the micro-sized range. Polycyclic aromatic hydrocarbons present in an outdoor setting, with a higher quantity in the nano-sized range. Morphological and elemental data provide additional evidence for observed contaminants and support multidisciplinary research interests. HoLDI holds promise as a standardized analytical framework for any air or water samples, facilitating research harmonization worldwide.

From automated Raman to cost-effective nanoparticle-on-film (NPoF) SERS spectroscopy: A combined approach for assessing micro- and nanoplastics released into the oral cavity from chewing gum

Microplastics (MPs) and Nanoplastics (NPs), a burgeoning health hazard, often go unnoticed due to suboptimal analytical tools, making their way inside our bodies through various means. Surface Enhanced Raman Spectroscopy (SERS), although is utilized in detecting NPs, challenges arise at low concentrations due to their low Raman cross section and inability to situate within hotspots owing to their ubiquitous size and shape. This study presents an innovative and cost-effective approach employing household metallic foils (aluminium and copper) as nanoparticle-on-film (NPoF) substrates for targeting such analytes. Leveraging from the near field enhancements due to plasmonic coupling amidst third-generation hotspots (TGHs) and second-generation hotspots (SGHs), the enhanced SERS activity is achieved. Furthermore, following an extensive comparison of the substrates' flexibility, sensitivity, reproducibility, and robustness, the copper foil-based NPoF platform was used to detect 100 nm polystyrene plastics down to 1 μg/ml concentration. Subsequently, a systematic detection of more than 250,000 MPs with automated Raman spectroscopy was performed, followed by the detection of NPs using SERS with a NPoF substrate in saliva samples released from the gum base in the oral cavity during a one-hour chewing activity. Overall, we report a cost-effective and versatile NPoF substrate, having the potential to screen a diverse array of environmental pollutants envisioned as a potential point-of-site tool by coupling it with a handheld Raman instrument.

Common issues of data science on the eco-environmental risks of emerging contaminants

Data-driven approaches (e.g., machine learning) are increasingly used to replace or assist laboratory studies in the study of emerging contaminants (ECs). In the past ten years, an increasing number of models or approaches have been applied to ECs, and the datasets used are continuously enriched. However, there are large knowledge gaps between what we have found and the natural eco-environmental meaning. For most published reviews, the contents are organized by the types of ECs, but the common issues of data science, regardless of the type of pollutant, are not sufficiently addressed. To close or narrow the knowledge gaps, we highlight the following issues ignored in the field of data-driven EC research. Complicated biological and ecological data and ensemble models revealing mechanisms and spatiotemporal trends with strong causal relationships and without data leakage deserve more attention in the future. In addition, the matrix influence, trace concentration, and complex scenario have often been ignored in previous works. Therefore, an integrated research framework related to natural fields, ecological systems, and large-scale environmental problems, rather than relying solely on laboratory data-related analysis, is urgently needed. Beyond the current prediction purposes, data science can inspire the discovery of scientific questions, and mutual inspiration among data science, process and mechanism models, and laboratory and field research is a critical direction. Focusing on the above urgent and common issues related to data, frameworks, and purposes, regardless of the type of pollutant, data science is expected to achieve great advancements in addressing the eco-environmental risks of ECs.

Advancing micro-nano supramolecular assembly mechanisms of natural organic matter by machine learning for unveiling environmental geochemical processes

The nano-self-assembly of natural organic matter (NOM) profoundly influences the occurrence and fate of NOM and pollutants in large-scale complex environments. Machine learning (ML) offers a promising and robust tool for interpreting and predicting the processes, structures and environmental effects of NOM self-assembly. This review seeks to provide a tutorial-like compilation of data source determination, algorithm selection, model construction, interpretability analyses, applications and challenges for big-data-based ML aiming at elucidating NOM self-assembly mechanisms in environments. The results from advanced nano-submicron-scale spatial chemical analytical technologies are suggested as input data which provide the combined information of molecular interactions and structural visualization. The existing ML algorithms need to handle multi-scale and multi-modal data, necessitating the development of new algorithmic frameworks. Interpretable supervised models are crucial owing to their strong capacity of quantifying the structure-property-effect relationships and bridging the gap between simply data-driven ML and complicated NOM assembly practice. Then, the necessity and challenges are discussed and emphasized on adopting ML to understand the geochemical behaviors and bioavailability of pollutants as well as the elemental cycling processes in environments resulting from the NOM self-assembly patterns. Finally, a research framework integrating ML, experiments and theoretical simulation is proposed for comprehensively and efficiently understanding the NOM self-assembly-involved environmental issues.

One-step detection of nanoplastics in aquatic environments using a portable SERS chessboard substrate

Nanoplastics present a significant hazard to both the environment and human health. However, the development of rapid and sensitive analysis techniques for nanoplastics is limited by their small size, lack of specificity, and low concentrations. In this study, a surface-enhanced Raman scattering (SERS) chessboard substrate was introduced as a multi-channel platform for the pre-concentration and detection of nanoplastics, achieved by polydomain aggregating silver nanoparticles (PASN) on a hydrophilic and a punched hydrophobic PVDF combined filter membrane. Through a straightforward suction filtration process, nanoplastics were captured by the PASN gap in a single step for subsequent SERS detection, while excess moisture was promptly eliminated from the filter membrane. The PASN-based SERS chessboard substrate, benefiting from the enhanced electromagnetic (EM) field, effectively discriminated polystyrene (PS) nanoplastics ranging in size from 30 nm to 1000 nm. Furthermore, this substrate demonstrated favorable repeatability (RSD of 8.6 %), high sensitivity with a detection limit of 0.001 mg/mL for 100 nm of PS nanoplastics, and broad linear detection ranges spanning from 0.001 to 0.5 mg/mL (R2 = 0.9916). Additionally, the SERS chessboard substrate enabled quantitative analysis of nanoplastics spiked in tap and lake water samples. Notably, the entire pre-concentration and detection procedure required only 3 μL of sample and could be completed within 1 min. With the accessibility of portable detection instruments and the ability to prepare substrates on demand, the PASN-based SERS chessboard substrate is anticipated to facilitate the establishment of a comprehensive global nanoplastics map.

Non-destructive detection and identification of plasticizers in PVC objects by means of machine learning-assisted Raman spectroscopy

Vibrational spectroscopic techniques, such as Raman spectroscopy, as a non-destructive method combined with machine learning (ML), were successfully tested as a quick method of plasticizer identification in poly(vinyl chloride) - PVC objects in heritage collection. ML algorithms such as Convolutional Neural Network (CNN), Random Forest (RF), Support Vector Machines (SVM), and Linear Discriminant Analysis (LDA) were applied to the classification and identification of the most common plasticizers used in the case of PVC. The CNN model was able to successfully classify the five plasticizers under study from their Raman spectra with a high accuracy of (98%), whereas the highest accuracy (100%) was observed with the RF algorithm. The finding opens doors for the development of robust and economical tools for conservators and museum professionals for fast identification of materials in heritage collections.

Artificial intelligence in microplastic detection and pollution control

The rising prevalence of microplastics (MPs) in various ecosystems has increased the demand for advanced detection and mitigation strategies. This review examines the integration of artificial intelligence (AI) with environmental science to improve microplastic detection. Focusing on image processing, Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, and hyperspectral imaging (HSI), the review highlights how AI enhances the efficiency and accuracy of these techniques. AI-driven image processing automates the identification and quantification of MPs, significantly reducing the need for manual analysis. FTIR and Raman spectroscopy accurately distinguish MP types by analyzing their unique spectral features, while HSI captures extensive spatial and spectral data, facilitating detection in complex environmental matrices. Furthermore, AI algorithms integrate data from these methods, enabling real-time monitoring, traceability prediction, and pollution hotspot identification. The synergy between AI and spectral imaging technologies represents a transformative approach to environmental monitoring and emphasizes the need to adopt innovative tools for protecting ecosystem health.

Rapid Generation of Microplastics and Plastic-Derived Dissolved Organic Matter from Food Packaging Films under Simulated Aging Conditions

In this study, we show that low-density polyethylene films, a prevalent choice for food packaging in everyday life, generated high numbers of microplastics (MPs) and hundreds to thousands of plastic-derived dissolved organic matter (DOM) substances under simulated food preparation and storage conditions. Specifically, the plastic film generated 66-2034 MPs/cm2 (size range 10-5000 μm) under simulated aging conditions involving microwave irradiation, heating, steaming, UV irradiation, refrigeration, freezing, and freeze-thaw cycling alongside contact with water, which were 15-453 times that of the control (plastic film immersed in water without aging). We also noticed a substantial release of plastic-derived DOM. Using ultrahigh-resolution mass spectrometry, we identified 321-1414 analytes with molecular weights ranging from 200 to 800 Da, representing plastic-derived DOM containing C, H, and O. The DOM substances included both degradation products of polyethylene (including oxidized forms of oligomers) and toxic plastic additives. Interestingly, although no apparent oxidation was observed for the plastic film under aging conditions, plastic-derived DOM was more oxidized (average O/C increased by 27-46%) following aging with a higher state of carbon saturation and higher polarity. These findings highlight the future need to assess risks associated with MP and DOM release from plastic wraps.

Wet oxidation technology can significantly reduce both microplastics and nanoplastics

For the resource recovery of biomass waste, it is a challenge to simultaneously remove micro-/nano-plastics pollution but preserve organic resources. Wet oxidation is a promising technology for valorization of organic wastes through thermal hydrolysis and oxidation. This might in turn result in the degradation of microplastics in the presence of oxygen and high temperatures. Based on this hypothesis, this study quantified both microplastics and nanoplastics in an industrial-scale wet oxidation reactor from a full-size coverage perspective. Wet oxidation significantly reduced the size and mass of individual microplastics, and decreased total mass concentration of microplastics and nanoplastics by 94.8 % to 98.6 %. This technology also reduced the micro- and nanoplastic shapes and polymer types, resulting in a complete removal of fibers, clusters, polypropylene (PP) and poly(methyl methacrylate) (PMMA). The present study confirms that wet oxidation technology is effective in removing microplastics and nanoplastics while recovering organic waste.

Using artificial intelligence to rapidly identify microplastics pollution and predict microplastics environmental behaviors

With the massive release of microplastics (MPs) into the environment, research related to MPs is advancing rapidly. Effective research methods are necessary to identify the chemical composition, shape, distribution, and environmental impacts of MPs. In recent years, artificial intelligence (AI)-driven machine learning methods have demonstrated excellent performance in analyzing MPs in soil and water. This review provides a comprehensive overview of machine learning methods for the prediction of MPs for various tasks, and discusses in detail the data source, data preprocessing, algorithm principle, and algorithm limitation of applied machine learning. In addition, this review discusses the limitation of current machine learning methods for various task analysis in MPs along with future prospect. Finally, this review finds research potential in future work in building large generalized MPs datasets, designing high-performance but low-computational-complexity algorithms, and evaluating model interpretability.

Surface enhanced raman spectroscopy based sensitive and onsite detection of microplastics in water utilizing silver nanoparticles and nanodendrites

Plastics, of the order of microns in size, being not visible to the naked eye, are one of the significant contributors to pollution in the environment. Thus, the detection of micron-sized plastics (microplastics (MPs)) is crucial because of its hazardous toxic effects on our surroundings. In this work, we have proposed a quick and on-site detection of MPs, such as, polyvinyl chloride (PVC), polyvinyl alcohol (PVA) and polystyrene (PS) at ultra trace level using surface-enhanced Raman spectroscopy (SERS). To detect and analyse the spectra, two different nanostructures, such as, spherical shaped Ag nanoparticles (NPs), and shape anisotropic Ag nano-dendrites (NDs) were utilised to acquire the SERS spectra. A comprehensive analysis was further performed to check and investigate the amount of enhancements due to the mentioned nanostructures. We observed the Ag NDs exhibited amplified signal intensity compared to the Ag NPs due to the shape anisotropy leading to the surface charge confinement effect to create highly dense hotspots. However, the spherical shaped polystyrene beads of micron size exhibited better enhancement in Raman signal intensity when mixed with Ag NPs due to increased surface adsorption with the NPs. Therefore, the comparative study emphasizes the ability of using solution-based nanostructure as SERS for the onsite detection of microplastics having diverge size range at low concentration.

Differentiating Microplastics from Natural Particles in Aqueous Suspensions Using Flow Cytometry with Machine Learning

Microplastics (MPs) in natural waters are heterogeneously mixed with other natural particles including algal cells and suspended sediments. An easy-to-use and rapid method for directly measuring and distinguishing MPs from other naturally present colloids in the environment would expedite analytical workflows. Here, we established a database of MP scattering and fluorescence properties, either alone or in mixtures with natural particles, by stain-free flow cytometry. The resulting high-dimensional data were analyzed using machine learning approaches, either unsupervised (e.g., viSNE) or supervised (e.g., random forest algorithms). We assessed our approach in identifying and quantifying model MPs of diverse sizes, morphologies, and polymer compositions in various suspensions including phototrophic microorganisms, suspended biofilms, mineral particles, and sediment. We could precisely quantify MPs in microbial phototrophs and natural sediments with high organic carbon by both machine learning models (identification accuracies over 93%), although it was not possible to distinguish between different MP sizes or polymer compositions. By testing the resulting method in environmental samples through spiking MPs into freshwater samples, we further highlight the applicability of the method to be used as a rapid screening tool for MPs. Collectively, this workflow can be easily applied to a diverse set of samples to assess the presence of MPs in a time-efficient manner.

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.

Fast Detection and Classification of Microplastics below 10 μm Using CNN with Raman Spectroscopy

In light of the growing awareness regarding the ubiquitous presence of microplastics (MPs) in our environment, recent efforts have been made to integrate Artificial Intelligence (AI) technology into MP detection. Among spectroscopic techniques, Raman spectroscopy is preferred for the detection of MP particles measuring less than 10 μm, as it overcomes the diffraction limitations encountered in Fourier transform infrared (FTIR). However, Raman spectroscopy's inherent limitation is its low scattering cross section, which often results in prolonged data collection times during practical sample measurements. In this study, we implemented a convolutional neural network (CNN) model alongside a tailored data interpolation strategy to expedite data collection for MP particles within the 1-10 μm range. Remarkably, we achieved the classification of plastic types for individual particles with a mere 0.4 s of exposure time, reaching an approximate confidence level of 85.47(±5.00)%. We postulate that the result significantly accelerates the aggregation of microplastic distribution data in diverse scenarios, contributing to the development of a comprehensive global microplastic map.

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.

Microplastic pollution: a review of techniques to identify microplastics and their threats to the aquatic ecosystem

Microplastics (MPs), small synthetic particles, have emerged as perilous chemical pollutants in aquatic habitats, causing grave concerns about their disruptive effects on ecosystems. The fauna and flora inhabiting these specific environments consume these MPs, unwittingly introducing them into the intricate web of the food chain. In this comprehensive evaluation, the current methods of identifying MPs are amalgamated and their profound impacts on marine and freshwater ecosystems are discussed. There are many potential risks associated with MPs, including the dangers of ingestion and entanglement, as well as internal injuries and digestive obstructions, both marine and freshwater organisms. In this review, the merits and limitations of diverse identification techniques are discussed, including spanning chemical analysis, thermal identification, and spectroscopic imaging such as Fourier-transform infrared spectroscopy (FTIR), Raman spectroscopy, and fluorescent microscopy. Additionally, it discusses the prevalence of MPs, the factors that affect their release into aquatic ecosystems, as well as their plausible impact on various aquatic ecosystems. Considering these disconcerting findings, it is imperative that appropriate measures should be taken to assess the potential risks of MP pollution, protect aquatic life and human health, and foster sustainable development.

Micro(nano)plastics in the Human Body: Sources, Occurrences, Fates, and Health Risks

The increasing global attention on micro(nano)plastics (MNPs) is a result of their ubiquity in the water, air, soil, and biosphere, exposing humans to MNPs on a daily basis and threatening human health. However, crucial data on MNPs in the human body, including the sources, occurrences, behaviors, and health risks, are limited, which greatly impedes any systematic assessment of their impact on the human body. To further understand the effects of MNPs on the human body, we must identify existing knowledge gaps that need to be immediately addressed and provide potential solutions to these issues. Herein, we examined the current literature on the sources, occurrences, and behaviors of MNPs in the human body as well as their potential health risks. Furthermore, we identified key knowledge gaps that must be resolved to comprehensively assess the effects of MNPs on human health. Additionally, we addressed that the complexity of MNPs and the lack of efficient analytical methods are the main barriers impeding current investigations on MNPs in the human body, necessitating the development of a standard and unified analytical method. Finally, we highlighted the need for interdisciplinary studies from environmental, biological, medical, chemical, computer, and material scientists to fill these knowledge gaps and drive further research. Considering the inevitability and daily occurrence of human exposure to MNPs, more studies are urgently required to enhance our understanding of their potential negative effects on human health.

Toxicity Mechanisms of Nanoplastics on Crop Growth, Interference of Phyllosphere Microbes, and Evidence for Foliar Penetration and Translocation

Despite the increasing prevalence of atmospheric nanoplastics (NPs), there remains limited research on their phytotoxicity, foliar absorption, and translocation in plants. In this study, we aimed to fill this knowledge gap by investigating the physiological effects of tomato leaves exposed to differently charged NPs and foliar absorption and translocation of NPs. We found that positively charged NPs caused more pronounced physiological effects, including growth inhibition, increased antioxidant enzyme activity, and altered gene expression and metabolite composition and even significantly changed the structure and composition of the phyllosphere microbial community. Also, differently charged NPs exhibited differential foliar absorption and translocation, with the positively charged NPs penetrating more into the leaves and dispersing uniformly within the mesophyll cells. Additionally, NPs absorbed by the leaves were able to translocate to the roots. These findings provide important insights into the interactions between atmospheric NPs and crop plants and demonstrate that NPs' accumulation in crops could negatively impact agricultural production and food safety.

Enhanced 3-D asynchronous correlation data preprocessing method for Raman spectroscopy of Chinese handmade paper

We have developed a novel 3D asynchronous correlation method (3D-ACM) designed for the classification and identification of Chinese handmade paper samples using Raman spectra and machine learning. The 3D-ACM approach involves two rounds of tensor product and Hilbert transform operations. In the tensor product process, the outer product of the spectral data from different samples within the same category is computed, establishing inner connections among all samples within that category. The Hilbert transform introduces a 90-degree phase shift, resulting in a true three-dimensional spectral data structure. This expansion significantly increases the number of equivalent frequency points and samples within each category. This enhancement substantially boosts spectral resolution and reveals more hidden information within the spectral data. To maximize the potential of 3D-ACM, we employed six machine learning models: principal component analysis (PCA) with linear regression (LR), support vector machine (SVM) with LR, k-Nearest Neighbors (KNN), random forest (RF), and convolutional neural network (CNN). When applied to the 3D-ACM data preprocessing method, R-squared values of PLS-LR, KNN, RF and CNN supervised models, approached or equaled 1. This indicates exceptional performance comparable to unsupervised models like PCA. 3D-ACM stands as a versatile mathematical technique not confined to spectral data. It also eliminates the necessity for additional experimental setups or external control conditions, distinct from traditional two-dimensional correlation spectroscopy. Moreover, it preserves the original experimental data, setting it apart from conventional data preprocessing methods. This positions 3D-ACM as a promising tool for future material classification and identification in conjunction with machine learning.

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.