Morphology and optical properties of Au-Ag hybrid nanoparticles regulation and its ultra-sensitive SERS immunoassay detection in carbohydrate antigen 19-9.

The development of an ultra-sensitive detection method for carbohydrate antigen 19-9 (CA19-9) is very important for the early diagnosis of pancreatic cancer. In this work, we developed a new strategy to achieve a variety of Au-Ag hybrid nanoparticles from janus to core-satellite which is controlled by the volume of AgNO3 and the concentration of benzimidazolecarboxylic acid (MBIA). With the volume of AgNO3 increased, Au-Ag hybrid nanoparticles changed from janus to core-satellite and the characteristic absorption peak showed two opposite trends. The size and number of Ag islands were determined by the concentration of MBIA. Au-Ag core-satellites nanoparticles with a large number of small-sized Ag have the highest SERS intensity. Then we used them as SERS nanotags and Au-Polystyrene nanospheres modified by captured anti-CA19-9 antibody as solid substrates to realize the ultra-sensitive detection of CA19-9 with a low limit of detection of 1.25 × 10-6 IU/mL and a wide linear range of 1.00 × 10-5 -1.00 × 104 IU/mL. This work not only demonstrates that MBIA and AgNO3 were the key factors in the growth of Au-Ag hybrid nanoparticles from 2D to 3D structure but also supplies an ultra-sensitive detection method for CA19-9 which has a potential practicability in the clinical early diagnoses of pancreatic cancer.

RamanCluster: A deep clustering-based framework for unsupervised Raman spectral identification of pathogenic bacteria.

Raman spectroscopy serves as a powerful and reliable tool for the characterization of pathogenic bacteria. The integration of Raman spectroscopy with artificial intelligence techniques to rapidly identify pathogenic bacteria has become paramount for expediting disease diagnosis. However, the development of prevailing supervised artificial intelligence algorithms is still constrained by costly and limited well-annotated Raman spectroscopy datasets. Furthermore, tackling various high-dimensional and intricate Raman spectra of pathogenic bacteria in the absence of annotations remains a formidable challenge. In this paper, we propose a concise and efficient deep clustering-based framework (RamanCluster) to achieve accurate and robust unsupervised Raman spectral identification of pathogenic bacteria without the need for any annotated data. RamanCluster is composed of a novel representation learning module and a machine learning-based clustering module, systematically enabling the extraction of robust discriminative representations and unsupervised Raman spectral identification of pathogenic bacteria. The extensive experimental results show that RamanCluster has achieved high accuracy on both Bacteria-4 and Bacteria-6, with ACC values of 77 % and 74.1 %, NMI values of 75 % and 73 %, as well as AMI values of 74.6 % and 72.6 %, respectively. Furthermore, compared with other state-of-the-art methods, RamanCluster exhibits the superior accuracy on handling various complicated pathogenic bacterial Raman spectroscopy datasets, including situations with strong noise and a wide variety of pathogenic bacterial species. Additionally, RamanCluster also demonstrates commendable robustness in these challenging scenarios. In short, RamanCluster has a promising prospect in accelerating the development of low-cost and widely applicable disease diagnosis in clinical medicine.

Early differential diagnosis of pancytopenia related diseases based on serum surface-enhanced Raman spectroscopy.

Pancytopenia is a common blood disorder defined as the decrease of red blood cells, white blood cells and platelets in the peripheral blood. Its genesis mechanism is typically complex and a variety of diseases have been found to be capable of causing pancytopenia, some of which are featured by their high mortality rates. Early judgement on the cause of pancytopenia can benefit timely and appropriate treatment to improve patient survival significantly. In this study, a serum surface-enhanced Raman spectroscopy (SERS) method was explored for the early differential diagnosis of three pancytopenia related diseases, i.e., aplastic anemia (AA), myelodysplastic syndrome (MDS) and spontaneous remission of pancytopenia (SRP), in which the patients with those pancytopenia related diseases at initial stage exhibited same pancytopenia symptom but cannot be conclusively diagnosed through conventional clinical examinations. The SERS spectral analysis results suggested that certain amino acids, protein substances and nucleic acids are expected to be potential biomarkers for their early differential diagnosis. In addition, a diagnostic model was established based on the joint use of partial least squares analysis and linear discriminant analysis (PLS-LDA), and an overall accuracy of 86.67 % was achieved to differentiate those pancytopenia related diseases, even at the time that confirmed diagnosis cannot be made by routine clinical examinations. Therefore, the proposed method has demonstrated great potential for the early differential diagnosis of pancytopenia related diseases, thus it has significant clinical importance for the timely and rational guidance on subsequent treatment to improve patient survival.

Quantitative detection of aflatoxin B1 in peanuts using Raman spectra and multivariate analysis methods.

Aflatoxin B1 (AFB1), among the identified aflatoxins, exhibits the highest content, possesses the most potent toxicity, and poses the gravest threat. It is commonly found in peanuts and their derivatives. This study employs Raman spectroscopy to monitor the AFB1 levels in moldy peanuts, providing a reliable theoretical basis for peanut storage management. Firstly, different degrees of moldy peanuts are spectrally characterized using a portable Raman spectrometer. Subsequently, a two-step hybrid strategy for feature selection is proposed, combining backward interval partial least squares (BiPLS) and variable combination population analysis (VCPA), aiming to simplify model complexity and enhance predictive accuracy. Finally, partial least squares (PLS) regression models are constructed based on different feature intervals and wavelength points. The research results reveal that the PLS regression model using the optimized feature intervals and wavelength points exhibits improved predictive capability and generalization performance. Notably, the BiPLS-VCPA-PLS model, established through the two-step optimization, selects nine wavelength variables, achieving a root mean square error of prediction (RMSEP) of 33.3147 µg∙kg-1, a correlation coefficient of the prediction set (RP) of 0.9558, and a relative percent deviation (RPD) of 3.4896. These findings demonstrate that the two-step feature optimization method, combining feature interval selection and feature wavelength selection, can more accurately identify optimal variables, thus enhancing detection efficiency and predictive precision.

Direct Observation of Particle-To-Particle Variability in Ambient Aerosol pH Using a Novel Analytical Approach Based on Surface-Enhanced Raman Spectroscopy.

The pH of atmospheric aerosols is a key characteristic that profoundly influences their impacts on climate change, human health, and ecosystems. Despite widely performed aerosol pH research, determining the pH levels of individual atmospheric aerosol particles has been a challenge. This study presents a novel analytical technique that utilizes surface-enhanced Raman spectroscopy to assess the pH of individual ambient PM2.5-10 aerosol particles in conjunction with examining their hygroscopic behavior, morphology, and elemental compositions. The results revealed a substantial pH variation among simultaneously collected aerosol particles, ranging from 3.3 to 5.7. This variability is likely related to each particle's unique reaction and aging states. The extensive particle-to-particle pH variability suggests that atmospheric aerosols present at the same time and location can exhibit diverse reactivities, reaction pathways, phase equilibria, and phase separation properties. This pioneering study paves the way for in-depth investigations into particle-to-particle variability, size dependency, and detailed spatial and temporal variations of aerosol pH, thus deepening our understanding of atmospheric chemistry and its environmental implications.

Mechanism of Interfacial Molecular Interactions Reveals the Intrinsic Factors for the Highly Enhanced Sensing Performance of Ag-Loaded Co3O4.

The noble metal-loaded strategy can effectively improve the gas-sensing performances of metal oxide sensors. However, the gas-solid interfacial interactions between noble metal-loaded sensing materials and gaseous species remain unclear, posing a significant challenge in correlating the physical and chemical processes during gas sensing. In this study, in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and in situ Raman spectroscopy were conducted to collaboratively investigate the interfacial interactions involved in the ethanol gas-sensing processes over Co3O4 and Ag-loaded Co3O4 sensors. In situ DRIFTS revealed differences in the compositions and quantities of sensing reaction products, as well as in the adsorption-desorption interactions of surface species, among Co3O4 and Ag-loaded Co3O4 materials. In parallel, in situ Raman spectroscopy demonstrated that the ethanol atmosphere can modulate the electron scattering of Ag-loaded Co3O4 materials but not of raw Co3O4. In situ experimental results revealed the intrinsic reason for the highly enhanced sensing performances of the Ag-loaded Co3O4 sensors toward ethanol gas, including a decreased optimal working temperature (from 250 to 150 °C), an improved gas response level (from 24 to 257), and accelerated gas recovery dynamics. This work provides an effective platform to investigate the interfacial interactions of sensing processes at the molecular level and further advances the development of high-performance gas sensors.

Se site targeted-two circles antioxidant in GPx4-like catalytic peroxide degradation by polyphenols (-)-epigallocatechin gallate and genistein using SERS.

A Se site targeted-two circles antioxidant of polyphenols EGCG and genistein in glutathione peroxidase 4 (GPx4)-like catalytic peroxide H2O2 and cumene hydroperoxide degradation was demonstrated by surface-enhanced Raman scattering (SERS). Se atom's active center is presenting a 'low-oxidation' and a 'high-oxidation' catalytic cycle. The former is oxidized to selenenic acid (SeO-) with a Raman bond at 619/ 610 cm-1 assigned to the νO - Se by the hydroperoxide substrate at 544/ 551 cm-1 assigned to ωHSeC decreased. Under oxidative stress, the enzyme shifted to 'high-oxidation' catalytic cycle, in which GPx4 shuttles between R-SeO- and R-SeOO- with a Raman intensity of bond at 840/ 860 cm-1 assigned to νO[bond, double bond]Se. EGCG could act as a reducing agent both in H2O2 and Cu-OOH degradation, while, genistein can only reduce Cu-OOH, because it binds more readily to the selenium site in GPx4 than EGCG with a closer proximity, therefore may affect its simultaneous binding to coenzymes.

Optical Nanobiosensor Based on Surface-Enhanced Raman Spectroscopy and Catalytic Hairpin Assembly for Early-Stage Lung Cancer Detection via Blood Circular RNA.

Lung cancer has become the leading cause of cancer-related deaths globally. However, early detection of lung cancer remains challenging, resulting in poor outcomes for the patients. Herein, we developed an optical biosensor integrating surface-enhanced Raman spectroscopy (SERS) with a catalyzed hairpin assembly (CHA) to detect circular RNA (circRNA) associated with tumor formation and progression (circSATB2). The signals of the Raman reporter were considerably enhanced by generating abundant SERS "hot spots" with a core-shell nanoprobe and 2D SERS substrate with calibration capabilities. This approach enabled the sensitive (limit of detection: 0.766 fM) and reliable quantitative detection of the target circRNA. Further, we used the developed biosensor to detect the circRNA in human serum samples, revealing that patients with lung cancer had higher circRNA concentrations than healthy subjects. Moreover, we characterized the unique circRNA concentration profiles of the early stages (IA and IB) and subtypes (IA1, IA2, and IA3) of lung cancer. These results demonstrate the potential of the proposed optical sensing nanoplatform as a liquid biopsy and prognostic tool for the early screening of lung cancer.

Ripening process in exocarps of scarlet eggplant (Solanum aethiopicum) and banana (Musa spp.) investigated by Raman spectroscopy.

In this work, we used Raman spectroscopy to identify compounds present at different maturation stages of the exocarp of scarlet eggplant and two banana cultivars, 'prata' and 'nanica'. Raman spectral analyses of both fruits showed bands attributed to phenolic acids, flavonoids, carotenoids, and fatty acids. During the scarlet eggplant's maturation process, Raman spectral profile changes are mainly observed in the carotenoid content rather than flavonoids. Furthermore, it is suggested that naringenin chalcone together with ß-carotene determines the orange-red color of the ripe stage. Variations in chemical composition among the maturation stages of bananas were observed predominantly in 'prata' when compared to 'nanica'. In contrast to scarlet eggplant changes in the spectral profile were more evident in the content of the flavonoid/phenolic acids. The in situ analysis was demonstrated to be useful as a guide in selecting bioactive compounds on demand from low-cost horticultural waste.

Optical characterization and defect-induced behavior in ZnAl1.999Ho0.001O4 spinel: Unraveling novel insights into structure, morphology, and spectroscopic features.

The ZnAl1.999Ho0.001O4 phosphor, prepared by the solid-state method, crystallizes in the cubic spinel structure. Morphology and chemical composition homogeneity were determined via Energy Dispersive X-ray and SEM analysis. The (Eg) optical band gap was evaluated from the UV/vis absorption spectrum, confirming direct transition behavior according to Tauc's law. The Urbach energy (Eu) in the ZnAl1.999Ho0.001O4 spinel was higher than that in the ZnAl2O4 spinel, indicating increased disorder and a higher concentration of defects due to Ho3+ ions. The penetration depth (δ(λ)), optical extinction (k(λ)), and refractive index (n(λ)) were assessed across wavelengths (λ). The room temperature absorption spectrum revealed several peaks corresponding to the 4f-4f transitions of Ho3+ ions.

Rapid discrimination of four Salmonella enterica serovars: A performance comparison between benchtop and handheld Raman spectrometers.

Foodborne illnesses, particularly those caused by Salmonella enterica with its extensive array of over 2600 serovars, present a significant public health challenge. Therefore, prompt and precise identification of S. enterica serovars is essential for clinical relevance, which facilitates the understanding of S. enterica transmission routes and the determination of outbreak sources. Classical serotyping methods via molecular subtyping and genomic markers currently suffer from various limitations, such as labour intensiveness, time consumption, etc. Therefore, there is a pressing need to develop new diagnostic techniques. Surface-enhanced Raman spectroscopy (SERS) is a non-invasive diagnostic technique that can generate Raman spectra, based on which rapid and accurate discrimination of bacterial pathogens could be achieved. To generate SERS spectra, a Raman spectrometer is needed to detect and collect signals, which are divided into two types: the expensive benchtop spectrometer and the inexpensive handheld spectrometer. In this study, we compared the performance of two Raman spectrometers to discriminate four closely associated S. enterica serovars, that is, S. enterica subsp. enterica serovar dublin, enteritidis, typhi and typhimurium. Six machine learning algorithms were applied to analyse these SERS spectra. The support vector machine (SVM) model showed the highest accuracy for both handheld (99.97%) and benchtop (99.38%) Raman spectrometers. This study demonstrated that handheld Raman spectrometers achieved similar prediction accuracy as benchtop spectrometers when combined with machine learning models, providing an effective solution for rapid, accurate and cost-effective identification of closely associated S. enterica serovars.

Charged Amino Acid Substitutions Affect Conformation of Neuroglobin and Cytochrome c Heme Groups.

Neuroglobin (Ngb) is a cytosolic heme protein that plays an important role in protecting cells from apoptosis through interaction with oxidized cytochrome c (Cyt c) released from mitochondria. The interaction of reduced Ngb and oxidized Cyt c is accompanied by electron transfer between them and the reduction in Cyt c. Despite the growing number of studies on Ngb, the mechanism of interaction between Ngb and Cyt c is still unclear. Using Raman spectroscopy, we studied the effect of charged amino acid substitutions in Ngb and Cyt c on the conformation of their hemes. It has been shown that Ngb mutants E60K, K67E, K95E and E60K/E87K demonstrate changed heme conformations with the lower probability of the heme planar conformation compared to wild-type Ngb. Moreover, oxidized Cyt c mutants K25E, K72E and K25E/K72E demonstrate the decrease in the probability of methyl-radicals vibrations, indicating the higher rigidity of the protein microenvironment. It is possible that these changes can affect electron transfer between Ngb and Cyt c.

Procalcitonin Detection Using Immunomagnetic Beads-Mediated Surface-Enhanced Raman Spectroscopy.

The early detection of procalcitonin (PCT) is crucial for diagnosing bacterial infections due to its high sensitivity and specificity. While colloidal gold colorimetric and immune-chemiluminescence methods are commonly employed in clinical detection, the former lacks sensitivity, and the latter faces challenges with a brief luminescence process and an elevated background. Here, we introduce a novel approach for the quantitative analysis of PCT using surface-enhanced Raman spectroscopy (SERS), leveraging the enhanced properties of metal nanoparticles. Simultaneously, we employed a magnetic nanoparticle coating and surface biofunctionalization modification to immobilize PCT-trapping antibodies, creating the required immune substrates. The resulting magnetic nanoparticles and antibody complexes, acting as carriers and recognition units, exhibited superparamagnetism and the specific recognition of biomarkers. Then, this complex efficiently underwent magnetic separation with an applied magnetic field, streamlining the cumbersome steps of traditional ELISA and significantly reducing the detection time. In conclusion, the exploration of immunomagnetic bead detection technology based on surface-enhanced Raman spectroscopy holds crucial practical significance for the sensitive detection of PCT.

Raman Flow Cytometry and Its Biomedical Applications.

Raman flow cytometry (RFC) uniquely integrates the "label-free" capability of Raman spectroscopy with the "high-throughput" attribute of traditional flow cytometry (FCM), offering exceptional performance in cell characterization and sorting. Unlike conventional FCM, RFC stands out for its elimination of the dependency on fluorescent labels, thereby reducing interference with the natural state of cells. Furthermore, it significantly enhances the detection information, providing a more comprehensive chemical fingerprint of cells. This review thoroughly discusses the fundamental principles and technological advantages of RFC and elaborates on its various applications in the biomedical field, from identifying and characterizing cancer cells for in vivo cancer detection and surveillance to sorting stem cells, paving the way for cell therapy, and identifying metabolic products of microbial cells, enabling the differentiation of microbial subgroups. Moreover, we delve into the current challenges and future directions regarding the improvement in sensitivity and throughput. This holds significant implications for the field of cell analysis, especially for the advancement of metabolomics.

Advances in Biosensors for the Rapid Detection of Marine Biotoxins: Current Status and Future Perspectives.

Marine biotoxins (MBs), harmful metabolites of marine organisms, pose a significant threat to marine ecosystems and human health due to their diverse composition and widespread occurrence. Consequently, rapid and efficient detection technology is crucial for maintaining marine ecosystem and human health. In recent years, rapid detection technology has garnered considerable attention for its pivotal role in identifying MBs, with advancements in sensitivity, specificity, and accuracy. These technologies offer attributes such as speed, high throughput, and automation, thereby meeting detection requirements across various scenarios. This review provides an overview of the classification and risks associated with MBs. It briefly outlines the current research status of marine biotoxin biosensors and introduces the fundamental principles, advantages, and limitations of optical, electrochemical, and piezoelectric biosensors. Additionally, the review explores the current applications in the detection of MBs and presents forward-looking perspectives on their development, which aims to be a comprehensive resource for the design and implementation of tailored biosensors for effective MB detection.

Binary Mixtures of Meloxicam and L-Tartaric Acid for Oral Bioavailability Modulation of Pharmaceutical Dosage Forms.

Binary mixtures of active pharmaceutical ingredients (API) are researched to improve the oral bioavailability of pharmaceutical dosage forms. The purpose of this study was to obtain mixtures of meloxicam and L-tartaric acid because tartaric acid improves intestinal absorption and meloxicam is more soluble in a weakly basic environment. The mixtures in the 0-1 molar fraction range, obtained from solvent-assisted mechanosynthesis, were investigated by differential scanning calorimetry (DSC), Fourier Transform Infrared (FTIR) spectroscopy, Fourier Transform Raman spectroscopy (FT-Raman), X-ray powder diffraction (XRD) and solubility tests. The physicochemical characteristics of the compounds obtained from DSC data reveal, for the first time, the formation of a co-crystal at meloxicam molar fraction of 0.5. FTIR spectroscopy data show the existence of hydrogen bonds between the co-crystal components meloxicam and L-tartaric acid. FT-Raman spectroscopy was used complementary with FT-IR spectroscopy to analyze the pure APIs and their mixtures, to emphasize the appearance/disappearance and the shifts of the position/intensity of vibrational bands, following the formation of hydrogen-bonded structures or van der Waals interactions, and to especially monitor the crystal lattice vibrations below 400 cm-1. The experimental results obtained by X-ray powder diffraction confirmed the formation of the co-crystal by the loss and, respectively, the apparition of peaks from the single components in the co-crystal diffractogram. The solubility tests showed that the co-crystal product has a lower aqueous solubility due to the acidic character of the other component, tartaric acid. However, when the solubility tests were performed in buffer solution of pH 7.4, the solubility of meloxicam from the co-crystal mixture was increased by 57% compared to that of pure meloxicam. In conclusion, the studied API mixtures may be considered potential biomaterials for improved drug release molecular solids.

Surface-Enhanced Raman Spectroscopy (SERS) for Identifying Traces of Adenine in Organic-Bearing Extraterrestrial Dust Analogs Captured in the Tanpopo Aerogel after Hypervelocity Impacts.

Raman spectroscopy is a non-destructive analytical technique for characterizing organic and inorganic materials with spatial resolution in the micrometer range. This makes it a method of choice for space-mission sample characterization, whether on return or in situ. To enhance its sensitivity, we use signal amplification via interaction with plasmonic silver-based colloids, which corresponds to surface-enhanced Raman scattering (SERS). In this study, we focus on the analysis of biomolecules of prebiotic interest on extraterrestrial dust trapped in silica aerogel, jointly with the Japanese Tanpopo mission. The aim is twofold: to prepare samples as close as possible to the real ones, and to optimize analysis by SERS for this specific context. Serpentinite was chosen as the inorganic matrix and adenine as the target biomolecule. The dust was projected at high velocity into the trapping aerogel and then mechanically extracted. A quantitative study shows effective detection even for adenine doping from a 5·10-9mol/L solution. After the dust has been expelled from the aerogel using a solvent, SERS mapping enables unambiguous adenine detection over the entire dust surface. This study shows the potential of SERS as a key technique not only for return samples, but also for upcoming new explorations.

Plasmonic Au@Ag Core-Shell Nanoisland Film for Photothermal Inactivation and Surface-Enhanced Raman Scattering Detection of Bacteria.

Plasmonic metal nanomaterials have been extensively investigated for their utilizations in biomedical sensing and treatment. In this study, plasmonic Au@Ag core-shell nanoisland films (Au@AgNIFs) were successfully grown onto a glass substrate using a seed-mediated growth procedure. The nanostructure of the Au@AgNIFs was confirmed through scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), and atomic force microscopy (AFM). The UV-Vis spectra of the Au@AgNIFs exhibited a broad absorption in the visible range from 300 to 800 nm because of the surface plasmon absorption. Under simulated sunlight exposure, the temperature of optimal Au@AgNIF was increased to be 66.9 °C to meet the requirement for photothermal bacterial eradication. Furthermore, the Au@AgNIFs demonstrated a consistent photothermal effect during the cyclic on/off exposure to light. For photothermal therapy, the Au@AgNIFs revealed superior efficiency in the photothermal eradication of Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus). With their unique nanoisland nanostructure, the Au@AgNIFs exhibited excellent growth efficiency of bacteria in comparison with that of the bare glass substrate. The Au@AgNIFs were also validated as a surface-enhanced Raman scattering (SERS) substrate to amplify the Raman signals of E. coli and S. aureus. By integrating photothermal therapy and SERS detection, the Au@AgNIFs were revealed to be a potential platform for bacterial theranostics.

Detection and Identification of Pesticides in Fruits Coupling to an Au-Au Nanorod Array SERS Substrate and RF-1D-CNN Model Analysis.

In this research, a method was developed for fabricating Au-Au nanorod array substrates through the deposition of large-area Au nanostructures on an Au nanorod array using a galvanic cell reaction. The incorporation of a granular structure enhanced both the number and intensity of surface-enhanced Raman scattering (SERS) hot spots on the substrate, thereby elevating the SERS performance beyond that of substrates composed solely of an Au nanorod. Calculations using the finite difference time domain method confirmed the generation of a strong electromagnetic field around the nanoparticles. Motivated by the electromotive force, Au ions in the chloroauric acid solution were reduced to form nanostructures on the nanorod array. The size and distribution density of these granular nanostructures could be modulated by varying the reaction time and the concentration of chloroauric acid. The resulting Au-Au nanorod array substrate exhibited an active, uniform, and reproducible SERS effect. With 1,2-bis(4-pyridyl)ethylene as the probe molecule, the detection sensitivity of the Au-Au nanorod array substrate was enhanced to 10-11 M, improving by five orders of magnitude over the substrate consisting only of an Au nanorod array. For a practical application, this substrate was utilized for the detection of pesticides, including thiram, thiabendazole, carbendazim, and phosmet, within the concentration range of 10-4 to 5 × 10-7 M. An analytical model combining a random forest and a one-dimensional convolutional neural network, referring to the important variable-one-dimensional convolutional neural network model, was developed for the precise identification of thiram. This approach demonstrated significant potential for biochemical sensing and rapid on-site identification.

Sampling of microplastics at a materials recovery facility.

Detecting, separating, and characterizing airborne microplastics from other airborne particulates is currently challenging due to the various instrumental constraints and related sample preparation hurdles that must be overcome. The ability to measure these real-world environments is needed to better assess the risks associated with microplastics. To that end, the current study focused on developing a methodology for sampling and characterizing airborne microplastics. Particulate sampling was carried out at a municipal materials recovery facility near a conveyer belt containing sorted plastic materials to collect airborne environmental particles on filters. Nucleopore filters were mounted on Teflon support rings, coated with 100 nm aluminum to reduce the background signal for micro-Raman spectroscopy, and marked with a fiducial pattern using a laser engraver. The fiducial pattern was crucial in identifying samples, relocating particles, and efficiently enabling orthogonal measurements on the same samples. Optimum sampling conditions of 2 h at 25 L/min were determined using light microscopy to evaluate the particle loadings. The filters were then cut into slices which were attached to sections of thin beryllium-copper sheeting for easy transfer of the filter between microscopy platforms. Scanning electron microscopy was used to identify carbon-rich particles. Light microscopy was used to identify colored particles which were also carbon-rich which were then analyzed using micro-Raman spectroscopy to identify specific polymers.