Overcoming the fluorescent interference during Raman spectroscopy detection of microplastics

Fast Detection and Classification of Microplastics by a Wide-Field Fourier Transform Raman Microscope

A number of applications require methods to detect with high spatial resolution and chemical specificity microplastics (MPs) extracted from different matrices. Here we introduce a wide-field hyperspectral Fourier transform Raman microscope for the rapid detection and identification of MPs. The instrument, based on a common-path birefringent interferometer, combines high spatial (∼1 μm) and spectral (∼23 cm-1) resolution with fast measurement times (∼15 min for a 100 kpixel image) and enables the suppression of sample fluorescence by a proper choice of the scan interval of the interferometer. After validating the instrument on MPs of commercial origin, we demonstrate its ability to detect MPs extracted from different matrices, by filtering seawater and pretreated gastrointestinal tracts of fish, and analyzing the MPs concentrated onto the filters. We expect that our microscope will enable high-quality, cost-effective, and rapid identification of MPs, fulfilling also the requirements of large-scale monitoring plans of different environmental matrices.

Deep-Learning-Assisted Raman Spectral Analysis for Accurate Differentiation of Highly Structurally Similar CA Series Synthetic Cannabinoids

Precise discrimination of the crucial substances, e.g., synthetic cannabinoids (SCs) that are composed of low-active chemical groups and structurally similar to each other with tiny differences, is a pressing need and of great significance for safeguarding public security and human health. The structure-relevant vibrational spectroscopic techniques, e.g., Raman spectroscopy, could reflect structural fingerprint information on the target; however, the algorithm-assisted phrasing is inevitable. This work achieved the accurate identification of CA series SCs by proposing an attention mechanism involving a CNN algorithm to phrase the Raman data. Specifically, these SCs have only one different chemical group compared to each other, the attention mechanism was introduced to intensify the computation on their structural difference from the massive data, realizing the accurate discrimination. Furthermore, how the spectral peaks corresponded to the specific structure was revealed, which plays a decisive role for the algorithm to distinguish these substances, and provides an instructive reference for differentiating other SCs based on Raman spectra. Hence, this work provides a research paradigm for applying the advanced CNN algorithm-aided Raman spectral analysis to sub-differentiate the substances, strengthening the understanding of spectral information from the sub-molecular level and propelling the integration of interdisciplinary areas.

Microplastic contamination in sediments: Analytical techniques and case-based evaluations

Microplastics (MPs) pollution in sediments has gained critical attention due to its pervasive presence and potential ecological risks. This review synthesizes the latest advancements in analytical techniques, providing a comprehensive overview of separation and identification methods tailored to complex sedimentary matrices. Density-based approaches, such as ZnCl2 or NaI solutions, and enzymatic digestions are increasingly refined to isolate MPs of varying sizes, yet discrepancies in mesh sizes, reagent concentrations, and digestion protocols continue to complicate cross-study comparisons. Meanwhile, cutting-edge spectroscopic tools-μFTIR, Raman imaging, thermal analyses-have greatly enhanced polymer identification down to the tens-of-micrometers scale. Case studies spanning urban estuaries to remote deep-sea basins underscore the pervasive nature of MPs worldwide, with fibers and fragments frequently dominating sediment samples. Factors such as polymer density, hydrodynamics, and biofouling contribute to the diverse distribution patterns, revealing that even ostensibly pristine environments are not exempt from contamination. Although the precise ecological and toxicological consequences of long-term sediment-bound MPs remain partly unclear, growing evidence points to intricate interactions with co-occurring contaminants and potential trophic transfer. To address these knowledge gaps, this review emphasizes the urgent need for methodological standardization and collaborative initiatives, particularly for emerging challenges like nanoplastic detection. By integrating robust sampling approaches, advanced analytical tools, and interdisciplinary research, scientists and policymakers can more accurately map and mitigate the impacts of sediment-associated MPs on aquatic ecosystems.

Exploring Environmental Behaviors and Health Impacts of Biodegradable Microplastics

Biodegradable plastics (BPs) are promoted as eco-friendly alternatives to conventional plastics. However, compared to conventional microplastics (MPs), they degrade rapidly into biodegradable microplastics (BMPs), which may lead to a more significant accumulation of BMPs in the environment. This review systematically compares BMPs and MPs, summarizes current knowledge on their environmental behaviors and impacts on ecosystems and human health, and offers recommendations for future research. BMPs are detected in water, sediments, indoor dust, food, marine organisms, and human samples. Compared to MPs, BMPs are more prone to environmental transformations, such as photodegradation and biodegradation, which results in a shorter migration distance across different matrices. Like MPs, BMPs can adsorb pollutants and transport them into organisms, enhancing toxicity and health risks through the Trojan horse effect. Studies indicate that BMPs may negatively impact terrestrial and aquatic ecosystems more than MPs by disrupting nutrient cycling and inhibiting plant and animal growth. In vivo and in vitro research also shows that BMP degradation products increase bioavailability, exacerbating neurotoxicity and overall toxicity. However, findings on BMPs' environmental and health effects remain inconsistent. Further evaluation of the trade-offs between BMP risks and their biodegradability is needed to address these uncertainties.

Screening for microplastics in agricultural soils: Applying green chemistry principles in extraction and analysis

In recent years, microplastic (MP) pollution has garnered significant attention owing to its ability to permeate various ecosystems, including soil. These particles can infiltrate the environment, either directly or through the degradation of larger plastic items. Despite growing concerns, standardized methods for quantification are still lacking. This study aimed to screen for the presence of MPs in agricultural soils while incorporating green analytical principles in the methodology. A density separation followed by centrifugation was employed, based on the principles of the QuEChERS extraction method. This approach minimized sample quantities, reagent consumption, and waste production, ensuring efficient extraction and analysis. Recovery tests using certified soils spiked with pristine MPs, specifically polystyrene, polypropylene (PP), and ethylene-vinyl acetate for larger MPs (3-5 mm), and low-density polyethylene, polyamide 6, and tire wear particles for smaller MPs (15-300 μm), achieved recovery levels exceeding 69% for smaller MPs and over 91% for larger particles. Spectroscopic analysis revealed slight alterations in the Raman spectra of MPs after extraction. Transitioning to agricultural soil analysis has revealed challenges, including spectral interferences. Nine mesoplastics (5-20 mm) were detected, predominantly consisting of PP and polyethylene (PE), along with seven MPs, three of which were individually identified as PE-based, while the remainder were inconclusive, including one fiber. The evaluation of the method's sustainability using the Analytical Eco-Scale and Analytical Greenness Calculator Metric (AGREE), with scores of 82 out of 100 and 0.66 out of 1, respectively, demonstrated its potential as a reliable approach to MP analysis in soils. This study highlights the potential of integrating green analytical chemistry principles into MP extraction methodologies and emphasizes the value of the proposed QuEChERs-based approach for improving the sustainability and efficiency of MP monitoring in agricultural soils.

The optimization of sample preparation on zebrafish larvae in vibrational spectroscopy imaging

The zebrafish (Danio rerio) larvae are widely used in biomedical, pharmaceutical, and ecotoxicological studies. Their transparency and translational potential make them particularly valuable for fluorescence imaging. In addition to fluorescence imaging, microspectroscopy, which combines vibrational spectroscopy: Raman or Fourier transform infrared (FT-IR) with microscopy, allows the collection of spatially resolved, label-free information. According to available literature, it was the first application of FT-IR imaging in zebrafish larvae. This study aims to compare different fixation methods for 10-day post-fertilization (dpf) zebrafish larvae using vibrational spectroscopy imaging. Paraformaldehyde (PFA), glutaraldehyde (GA), low temperature, and embedding in gelatin and agarose were investigated. Amides, lipids, and phosphates distribution were more informative in embedded samples but with challenging handling of the sample due to stiffness at -20 °C. FT-IR and Raman mapping revealed that frozen samples had better-preserved tissue structure than chemical fixation. PFA showed uniform amide distribution, while GA treatment exhibited tissue disruptions and denser protein networks in both. Handling of embedded samples is challenging for an operator, but provides more reliable results in developmental biology or disease modeling, compared to chemical treatment.

Affinity Ultrafiltration Mass Spectrometry for Screening Active Ingredients in Traditional Chinese Medicine: A Review of the Past Decade (2014-2024)

Discovering targets in natural products is a critical and challenging task in new drug development. Rapid and efficient screening of active ingredients from complex systems like traditional Chinese medicine (TCM) is now crucial in drug research. Affinity ultrafiltration (AUF) technology is widely used to screen active ingredients in natural medicines. AUF-liquid chromatography-mass spectrometry (AUF-LC-MS) leverages the affinity between natural medicine extracts and targets to isolate active ingredients from complex matrices, employing LC-MS for detection and activity assessment. This review discusses the developments in employing AUF-LC-MS to analyze TCM and TCM compound preparations over the last decade. This review succinctly presents the advantages and limitations of AUF-LC-MS, illustrating its benefits through the example of screening for active ingredients in natural pharmaceuticals.

Analytical Challenges and Strategies for Particle-Based Analysis of Airborne Micro(nano)plastics in Size-Fractionated Samples Using Microscopy, SEM/EDX, and Raman Spectroscopy

Inhalable micro(nano)plastics (MNPs) have emerged as a significant global concern due to their abundance and persistence in the atmosphere. Despite a growing body of literature addressing the analytical requirements of airborne MNPs, the issue of inhalable fractions and analysis of slotted substrates remains unclear. Therefore, the objective of this study is to perform a systematic particle-based analysis and characterization of inhalable microplastics (MPs) collected by a high-volume sampler equipped with a five-stage cascade impactor with a size range of 10 μm to <0.49 μm. The efficacy of collection substrates (Teflon and aluminum) was evaluated, as was the impact of particle transfer from the slotted filters on the analysis area and pretreatment methods including chemical digestion for further analysis. The distribution of MNP particles across different slots of a Teflon filter was investigated using Raman microspectrometry to select an appropriate subsample. The results showed the suitability of Teflon filters without any pretreatment for particles down to a single micrometer. As observed by the SEM/EDX analysis, the airborne particles collected in a filter with a submicrometer range (<0.95 μm) showed a decrease in carbon-rich components compared to those stages with higher cutoff sizes. A minimum of 20 particles were analyzed per 1 cm2 of the slotted filter using Raman spectrometry, which revealed a homogeneous distribution of MPs across different slots and yielded a concentration of 452 ± 134 MP/m3 in the first stage of the cascade sampler. The detected MPs were morphologically classified into two main groups: fragments with a size range of 2.8-24.8 μm and fibers with a size range of 28.6-212 μm. Subsequently, the particles were chemically identified as carbon black (tires) and polypropylene. In conclusion, particle-based analysis of size-segregated airborne MNPs presents certain challenges when attempting to analyze particles as small as a single micrometer due to the fact that the aerodynamic diameter of the particles in question and the corresponding analytical limitations that result from this become particularly problematic, especially for cutoffs smaller than 3 μm.

Challenges in Raman spectroscopy of (micro)Plastics: The interfering role of colourants

Rising plastic consumption leads to widespread microplastic (MP) contamination. Raman spectroscopy is widely used for MP identification due to its ability to analyse particles as small as 1 μm. However, it faces challenges such as interference from pigments and additives. In this study, we aim to assess the accuracy of Raman micro-spectroscopy in identifying coloured plastic samples by applying various oxidative treatments to eliminate the possible interference effect caused by colourants associated with the sample. Standard and coloured microplastics were analysed using a Raman imaging microscope. Coloured plastics were treated with H2O2 30%, Sodium hypochlorite 5%, and Fenton reagent (H2O2 30% and Ferrous sulphate 0.2 M) for 24, 48, and 72 h. The Raman spectra were acquired after treatment to assess the impact of the treatment procedure on the polymer identification. Our results revealed that colourants significantly impact Raman spectra by peak broadening and/or fluorescence effects, which reduces identification accuracy and match scores Red pigments particularly obscure polymer identification. Treatments like oxidation and Fenton's reagent showed limited effectiveness. Additives in plastic samples can affect the accuracy of polymer identification by the Raman spectroscopy technique. Common treatment procedures do not improve the accuracy of identification. In order to improve the reliability of Raman analysis, essential factors such as utilizing multiple excitation lasers and appropriate CCD detectors, establishing a comprehensive reference library of colourants and additives, and employing advanced techniques like time-gated Raman spectroscopy or Surface-Enhanced Raman Spectroscopy (SERS) should be considered.

In situ surface-enhanced Raman spectroscopy for the detection of nanoplastics: A novel approach inspired by the aging of nanoplastics

Nanoplastics (NPs) present a hidden risk to organisms and the environment via migration and enrichment. Detecting NPs remains challenging because of their small size, low ambient concentrations, and environmental variability. There is an urgency to exploit detection approaches that are more compatible with real-world environments. Herein, this study provides a surface-enhanced Raman spectroscopy (SERS) technique for the in situ reductive generation of silver nanoparticles (Ag NPs), which is based on photoaging-induced modifications in NPs. The feasibility of generating Ag NPs on the surface of NPs was derived by exploring the photoaging mechanism, which was then utilized to SERS detection. The approach was applied successfully for the detection of polystyrene (PS), polyvinyl chloride (PVC), and polyethylene terephthalate (PET) NPs with excellent sensitivity (e.g., as low as 1 × 10-6 mg/mL for PVC NPs, and an enhancement factor (EF) of up to 2.42 × 105 for small size PS NPs) and quantitative analytical capability (R2 > 0.95579). The method was successful in detecting NPs (PS NPs) in lake water. In addition, satisfactory recoveries (93.54-105.70 %, RSD < 12.5 %) were obtained by spiking tap water as well as lake water, indicating the applicability of the method to the actual environment. Therefore, the proposed approach offers more perspectives for testing real environmental NPs.

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.

Microplastics in water: Occurrence, fate and removal

A global study on tap water samples has found that up to 83% of these contained microplastic fibres. These findings raise concerns about their potential health risks. Ingested microplastic particles have already been associated with harmful effects in animals, which raise concerns about similar outcomes in humans. Microplastics are ubiquitous in the environment, commonly found disposed in landfills and waste sites. Within indoor environments, the common sources are synthetic textiles, plastic bottles, and packaging. From the various point sources, they are globally distributed through air and water and can enter humans through various pathways. The finding of microplastics in fresh snow in the Antarctic highlights just how widely they are dispersed. The behaviour and health risks from microplastic particles are strongly influenced by their physicochemical properties, which is why their surfaces are important. Surface interactions are also important in pollutant transport via adsorption onto the microplastic particles. Our review covers the latest findings in microplastics research including the latest statistics in their abundance, their occurrence and fate in the environment, the methods of reducing microplastics exposure and their removal. We conclude by proposing future research directions into more effective remediation methods including new technologies and sustainable green remediation methods that need to be explored to achieve success in microplastics removal from waters at large scale.

Establishment and application of a screening method for α-glucosidase inhibitors based on dual sensing and affinity chromatography

α-Glucosidase plays a direct role in the metabolic pathways of starch and glycogen, any dysfunction in its activity could result in metabolic disease. Concurrently, this enzyme serves as a target for diverse drugs and inhibitors, contributing to the regulation of glucose metabolism in the human body. Here, an integrated analytical method was established to screen inhibitors of α-glucosidase. This step-by-step screening model was accomplished through the biosensing and affinity chromatography techniques. The newly proposed sensing program had a good linear relationship within the enzyme activity range of 0.25 U mL-1 to 1.25 U mL-1, which can quickly identify active ingredients in complex samples. Then the potential active ingredients can be captured, separated, and identified by an affinity chromatography model. The combination of the two parts was achieved by an immobilized enzyme technology and a microdevice for reaction, and the combination not only ensured efficiency and accuracy for inhibitor screening but also eliminated the occurrence of false positive results in the past. The emodin, with a notable inhibitory effect on α-glucosidase, was successfully screened from five traditional Chinese medicines using this method. The molecular docking results also demonstrated that emodin was well embedded into the active pocket of α-glucosidase. In summary, the strategy provided an efficient method for developing new enzyme inhibitors from natural products.

Fabrication strategies for chiral self-assembly surface

Chiral self-assembly is the spontaneous organization of individual building blocks from chiral (bio)molecules to macroscopic objects into ordered superstructures. Chiral self-assembly is ubiquitous in nature, such as DNA and proteins, which formed the foundation of biological structures. In addition to chiral (bio) molecules, chiral ordered superstructures constructed by self-assembly have also attracted much attention. Chiral self-assembly usually refers to the process of forming chiral aggregates in an ordered arrangement under various non-covalent bonding such as H-bond, π-π interactions, van der Waals forces (dipole-dipole, electrostatic effects, etc.), and hydrophobic interactions. Chiral assembly involves the spontaneous process, which followed the minimum energy rule. It is essentially an intermolecular interaction force. Self-assembled chiral materials based on chiral recognition in electrochemistry, chiral catalysis, optical sensing, chiral separation, etc. have a broad application potential with the research development of chiral materials in recent years.

Micro(nano)plastics in marine medaka: Entry pathways and cardiotoxicity with triphenyltin

The simultaneous presence of micro(nano)plastics (MNPs) and pollutants represents a prevalent environmental challenge that necessitates understanding their combined impact on toxicity. This study examined the distribution of 5 μm (PS-MP5) and 50 nm (PS-NP50) polystyrene plastic particles during the early developmental stages of marine medaka (Oryzias melastigma) and assessed their combined toxicity with triphenyltin (TPT). Results showed that 2 mg/L PS-MP5 and PS-NP50 could adhere to the embryo surface. PS-NP50 can passively enter the larvae and accumulate predominantly in the intestine and head, while PS-MP5 cannot. Nonetheless, both types can be actively ingested by the larvae and distributed in the intestine. 2 mg/L PS-MNPs enhance the acute toxicity of TPT. Interestingly, high concentrations of PS-NP50 (20 mg/L) diminish the acute toxicity of TPT due to their sedimentation properties and interactions with TPT. 200 μg/L PS-MNPs and 200 ng/L TPT affect complement and coagulation cascade pathways and cardiac development of medaka larvae. PS-MNPs exacerbate TPT-induced cardiotoxicity, with PS-NP50 exhibiting stronger effects than PS-MP5, which may be related to the higher adsorption capacity of NPs to TPT and their ability to enter the embryos before hatching. This study elucidates the distribution of MNPs during the early developmental stages of marine medaka and their effects on TPT toxicity, offering a theoretical foundation for the ecological risk assessment of MNPs.