Identification Of Particles Research Articles

Identification and detection of microplastic particles in marine environment by using improved faster R–CNN model

Microplastics refer to plastic particles measuring less than 5 mm, which has led to serious environmental problem and the detection of these tiny particles is crucial for understanding the corresponding distribution and impact on the marine environment. In this paper, an improved faster region-based convolutional neural network (R–CNN) model was developed for the identification and detection of microplastic particles. In the proposed model, the residual network-50 (ResNet-50) is employed as the backbone with the replacement of the traditional one to enhance the feature extraction capability and the feature pyramid networks (FPN) module is introduced together for solving the multi-scale target detection. By using the improved Faster R–CNN model, the network model performance is enhanced where the average confidence of detecting unique microplastic particles in the marine environment reaches as high as 99%. Moreover, the microparticles mixture was bounded precisely via the predicted bounding boxes without missing detection and wrong detection. In this way, the successful identification of polystyrene microplastic particles from the particles suspension with similar shapes but various conditions of backgrounds, brightness, distributions and object sizes, was achieved by employing the proposed improved Faster R–CNN model, enabling the accurate detection of microplastic particles in marine environment.

Rapid identification of micro and nanoplastics by line scan Raman micro-spectroscopy

Microplastic pollution has become an environmental problem that cannot be ignored in our society. Raman spectroscopy technology has been widely used in the field of microplastics detection due to its non-contact, non-destructive chemical specificity. Traditional point confocal Raman micro-spectroscopy technology uses single-point detection, resulting in long measurement times to scan the large areas of interest of typical samples. In this paper, we present a line scan confocal Raman micro-spectroscopy tool for fast detection and identification of microplastic particles. We show size and composition identification of particles and imaging over large areas. Compared with point confocal Raman imaging, the line scan confocal Raman technology increases the imaging speed by 1–2 orders of magnitude.

Composition and structural identification of silicon particles in diamond wire saw silicon powder

In this study, the content and calculation equations of each substance in diamond wire saw silicon powder (DWSSP) were determined. Initially, the presence of amorphous SiO2 was proven by crystal transformation. Then, the SiO2 content was determined by SiO2-CaO phase diagram analysis, and the SiO x content was determined by using the oxygen conservation rule. The variation trend between the SiO2, SiO x , and Si contents and the O content was determined, according to the calculated SiO2 and SiO x content. In addition, linear fitting was performed on the SiO2, SiO x , and Si contents of five DWSSP raw materials, and a calculation equation for SiO2, SiO x , and Si was obtained through the total O content. The three-layer structure of DWSSP was revealed, the DWSSP consisting of a Si core, an intermediate SiO x layer, and a SiO2 shell. This study can accurately identify the content of SiO2, SiO x , and Si in DWSSP based on the detected oxygen content, and provide a better theoretical basis for the process selection and design for silicon recovery or high value materials preparation.

Elastic modulus identification of particles in particulate composite through nanoindentation

The characterization of the constitutive behavior of embedded particles is crucial for the mechanical analysis of functional particulate composites. However, current methods for identifying the elastic parameters through instrumented indentation tests lack robust theoretical foundations. In this study, a series-connection analytical model was proposed to determine the Young's modulus of the embedded particles based on the Oliver-Pharr (O-P) model. Additionally, a simplified regression function was derived from the complete analytical model.To validate the accuracy of these methods in estimating the Young's modulus, 2D indentation simulations were conducted. Furthermore, in order to facilitate practical engineering applications, a data processing strategy was developed and implemented to determine the statistical value of the Young's modulus for LiNixMnyCozO2 (NMC) particles in the cathode of a Lithium-ion battery (LIB). Moreover, data obtained from continuous stiffness measurement (CSM) of a single embedded NMC particle were utilized to verify the reliability of the simplified regression function. The simplified regression function was found to possess significant engineering potential in related studies, as it enables a quick and reliable approximation of the Young's modulus of embedded particles without requiring detailed experimental information.

Detection and identification of particles on silicon wafers based on light scattering and absorption spectroscopy and Machine learning

Modern semiconductor manufacturing technology have a high-quality requirement of the wafers, and therefore the wafer inspection technique becomes increasingly important. During the manufacturing processes, particles can attach on the surface of the wafer which is an important factor of the quality and can even make it impossible to use the wafer. In this research, we introduce a particle detection and identification method based on the scattering and absorption spectra of the particles. A machine learning algorithm was developed to capture the feature of the particles and is able to identify the particle material from the scattering spectrum. Three different particles (Al2O3, SiC, and Si) were used to test this system. The validation accuracy achieves higher than 98% after 5 iterations training. The system was tested by scattering these three particles on the same wafer in different regions without mixing with each other. The results shows that particle Al2O3 and Si were identified with a high accuracy, whereas it is still challenging for the system to correctly label SiC particles. This can be improved by a larger dataset to enhance the generalization ability of the machine learning model.

Detection and quantification of microplastics in commercially bottled edible oil

The presence of microplastics in the food system has raised increasing public attention due to their potential risk to human health. However, knowledge regarding the presence and quantity of microplastics in daily consumed food products is very limited. The objective of this study is to quantify microplastics in polyethylene terephthalate (PET) bottled edible oil and determine the impact of oil on microplastics released from plastic packages in an accelerated storage experiment. For microplastic recovery, hexane was used to dilute the oil and facilitate the filtration process, and 62.73–118.00% of microplastics were recovered using standard polystyrene microplastics. Microplastics present in the oil were identified based on their Raman characteristics. The most frequent polymers identified were Poly(Ethylacrylate:St:Acrylamide) and Poly(Ethylene:Propylene) The result shows particles ranging from 1.34 × 105 to 5.80 × 105 per liter of oil were detected in 4 commercial edible oil (olive oil, canola oil, sunflower oil, and coconut oil). Over 80% of detected plastics were smaller than 10 µm. The accelerated experiment (40 °C, 10 days) did not lead to an increased number of particles (p > 0.05) nor the identification of any PET particles, suggesting that no PET microplastics were released from the bottles during the storage. Our results fill in the knowledge gap regarding the microplastics in edible oil and facilitate exposure analysis of microplastics through food.

Imaging and identification of single nanoplastic particles and agglomerates

Pollution by nanoplastic is a growing environmental and health concern. Currently the extent of nanoplastic in the environment can only be cumbersomely and indirectly estimated but not measured. To be able to quantify the extent of the problem, detection methods that can identify nanoplastic particles that are smaller than 1 mum are critically needed. Here, we employ surface-enhanced Raman scattering (SERS) to image and identify single nanoplastic particles down to 100 nm in size. We can differentiate between single particles and agglomerates and our method allows an improvement in detection speed of 10^{7} compared to state-of-the art surface-enhanced Raman imaging. Being able to resolve single particles allows to measure the SERS enhancement factor on individual nanoplastic particles instead of averaging over a concentration without spatial information. Our results thus contribute to the better understanding and employment of SERS for nanoplastic detection and present an important step for the development of future sensors.

R & D of prototype iTOF-MRPC at CEE

The cooling storage ring (CSR) external-target experiment (CEE) is a spectrometer running at the Heavy Ion Research Facility (HIRFL) at Lanzhou. The CEE is the first large-scale nuclear physics experimental device by China to operate in the fixed-target mode with an energy of ∼1 GeV. The purpose of the CEE is to study the properties of dense nuclear matter. CEE uses a multi-gap resistive plate chamber (MRPC) as its internal time-of-flight (iTOF) detector for the identification of final-state particles. An iTOF-MRPC prototype with 24 gaps was designed to meet the requirements of CEE, and the readout electronics of the prototype use the FPGA-based time digitization technology. Using cosmic ray tests, the time resolution of the iTOF prototype was found to be approximately 30 ps. In order to further understand how to improve the time resolution of MRPC, ANSYS HFSS was used to simulate the signal transmission process in MRPC. The main factors affecting the timing performance of the MRPC and, accordingly, the optimization scheme are presented.

An automated system for polymer wear debris analysis in total disc arthroplasty using convolution neural network.

Introduction: Polymer wear debris is one of the major concerns in total joint replacements due to wear-induced biological reactions which can lead to osteolysis and joint failure. The wear-induced biological reactions depend on the wear volume, shape and size of the wear debris and their volumetric concentration. The study of wear particles is crucial in analysing the failure modes of the total joint replacements to ensure improved designs and materials are introduced for the next generation of devices. Existing methods of wear debris analysis follow a traditional approach of computer-aided manual identification and segmentation of wear debris which encounters problems such as significant manual effort, time consumption, low accuracy due to user errors and biases, and overall lack of insight into the wear regime. Methods: This study proposes an automatic particle segmentation algorithm using adaptive thresholding followed by classification using Convolution Neural Network (CNN) to classify ultra-high molecular weight polyethylene polymer wear debris generated from total disc replacements tested in a spine simulator. A CNN takes object pixels as numeric input and uses convolution operations to create feature maps which are used to classify objects. Results: Classification accuracies of up to 96.49% were achieved for the identification of wear particles. Particle characteristics such as shape, size and area were estimated to generate size and volumetric distribution graphs. Discussion: The use of computer algorithms and CNN facilitates the analysis of a wider range of wear debris with complex characteristics with significantly fewer resources which results in robust size and volume distribution graphs for the estimation of the osteolytic potential of devices using functional biological activity estimates.

Label-Free Identification of Carbonaceous Particles Using Nonlinear Optical Microscopy.

The adverse health effects of ambient carbonaceous particles (CPs) such as carbon black (CB), black carbon (BC), and brown carbon (BrC) are becoming more evident and depend on their composition and emission source. Therefore, identifying and quantifying these particles in biological samples are important to better understand their toxicity. Here, we report the development of a nonlinear optical approach for the identification of CPs such as CB and BrC using imaging conditions compatible with biomedical samples. The unique visible light fingerprint of CB and BrC nanoparticles (NPs) upon illumination with a femtosecond (fs) pulsed laser at 1300 nm excitation wavelength is an effective approach for their identification in their biological context. The emission from spectral features of these CPs was investigated with time-domain fluorescence lifetime imaging (FLIM) to further support their identification. This study is performed for different types of CPs embedded in agarose gel as well as in in vitro mammalian cells. The unique nonlinear emissive behavior of CP NPs used for their label-free identification is further complementary with fluorophores typically used for specific staining of biological samples thus providing the relevant bio-context.

Co-administration of Intravenous Drugs: Rapidly Troubleshooting the Solid Form Composition of a Precipitate in a Multi-drug Mixture Using On-Site Raman Spectroscopy.

Intravenous drugs are often co-administrated in the same intravenous catheter line due to which compatibility issues, such as complex precipitation processes in the catheter line, may occur. A well-known example that led to several neonatal deaths is the precipitation due to co-administration of ceftriaxone- and calcium-containing solutions. The current study is exploring the applicability of Raman spectroscopy for testing intravenous drug compatibility in hospital settings. The precipitation of ceftriaxone calcium was used as a model system and explored in several multi-drug mixtures containing both structurally similar and clinically relevant drugs for co-infusion. Equal molar concentrations of solutions containing ceftriaxone and calcium chloride dihydrate were mixed with solutions of cefotaxime, ampicillin, paracetamol, and metoclopramide. The precipitate formed was collected as an "unknown" material, dried, and analyzed. Several solid-state analytical methods, including X-ray powder diffraction, Raman spectroscopy, and thermogravimetric analysis, were used to characterize the precipitate. Raman microscopy was used to investigate the identity of single sub-visual particles precipitated from a mixture of ceftriaxone, cefotaxime, and calcium chloride. X-ray powder diffraction suggested that the precipitate was partially crystalline; however, the identity of the solid form of the precipitate could not be confirmed with this standard method. Raman spectroscopy combined with multi-variate analyses (principal component analysis and soft independent modelling class analogy) enabled the correct detection and identification of the precipitate as ceftriaxone calcium. Raman microscopy enabled the identification of ceftriaxone calcium single particles of sub-visual size (around 25 μm), which is in the size range that may occlude capillaries. This study indicates that Raman spectroscopy is a promising approach for supporting clinical decisions and especially for compatibility assessments of drug infusions in hospital settings.

Physicochemical characterization and quantification of nanoplastics: applicability, limitations and complementarity of batch and fractionation methods

A comprehensive physicochemical characterization of heterogeneous nanoplastic (NPL) samples remains an analytical challenge requiring a combination of orthogonal measurement techniques to improve the accuracy and robustness of the results. Here, batch methods, including dynamic light scattering (DLS), nanoparticle tracking analysis (NTA), tunable resistive pulse sensing (TRPS), transmission electron microscopy (TEM), and scanning electron microscopy (SEM), as well as separation/fractionation methods such as centrifugal liquid sedimentation (CLS) and field-flow fractionation (FFF)–multi-angle light scattering (MALS) combined with pyrolysis gas chromatography mass spectrometry (pyGC–MS) or Raman microspectroscopy (RM) were evaluated for NPL size, shape, and chemical composition measurements and for quantification. A set of representative/test particles of different chemical natures, including (i) polydisperse polyethylene (PE), (ii) (doped) polystyrene (PS) NPLs, (iii) titanium dioxide, and (iv) iron oxide nanoparticles (spherical and elongated), was used to assess the applicability and limitations of the selected methodologies. Particle sizes and number-based concentrations obtained by orthogonal batch methods (DLS, NTA, TRPS) were comparable for monodisperse spherical samples, while higher deviations were observed for polydisperse, agglomerated samples and for non-spherical particles, especially for light scattering methods. CLS and TRPS offer further insight with increased size resolution, while detailed morphological information can be derived by electron microscopy (EM)–based approaches. Combined techniques such as FFF coupled to MALS and RM can provide complementary information on physical and chemical properties by online measurements, while pyGC–MS analysis of FFF fractions can be used for the identification of polymer particles (vs. inorganic particles) and for their offline (semi)quantification. However, NPL analysis in complex samples will continue to present a serious challenge for the evaluated techniques without significant improvements in sample preparation.Graphical abstract

Human occupational exposure to microplastics: A cross-sectional study in a plastic products manufacturing plant

Microplastics are ubiquitous in the natural environment, and their potential impact on health is a key issue of concern. Investigating exposure routes in humans and other living organisms is among the major challenges of microplastics. This study aims to examine the exposure level of plastic factory staff to microplastic particles before and after work shifts through body receptors (hand and facial skin, saliva and hair) in Sirjan, southeast of Iran. Moreover, the effect of face masks, gloves, cosmetics (e.g: face powder cream, lipstick and eye makeup products) and appearance on the exposure level is investigated. In total, 19 individuals are selected during six working days. Then, the collected samples are transferred to the laboratory for filtration, extraction, identification and counting of microplastic particles. Moreover, 4802 microplastic particles (100–5000 μm in size) in strand, polyhedral and spherical shapes and color spectra of white/transparent, black, blue/green, red and purple are observed. The nature of most of the observed samples is fiber with a size ≥1000 μm. Analyzing the selected samples using micro-Raman spectroscopy indicate polyester and nylon are the main identified fibers. Hair and saliva samples have the highest and lowest number of microplastics, respectively. Using gloves and sunscreen among all the participants, wearing a scarf and hair size among women and having a beard and mustache among men could have an effective role in the exposure level to microplastics. Results of this study could reveal the exposure route to microplastic particles in the human body and highlight the importance of providing higher protection to reduce exposure.

Genetic Algorithm for Determination of the Event Collision Time and Particle Identification by Time-of-Flight at NICA SPD

Particle identification is an important feature of the future SPD (Spin Physics Detector) experiment at the NICA (Nuclotron-based Ion Collider fAcility) collider. In particular, the identification of particles with momenta up to a few GeV/c (with c the speed of light) by their time-of-flight facilitates the reconstruction of events of interest. The high time resolution of modern TOF (Time-Of-Flight) detectors demands the need to obtain the event collision time, t0, with comparable accuracy. While the determination of the collision time is feasible through the use of TOF signals supplemented by track reconstruction, it proves to be computationally expensive. In the presented study, a dedicated Genetic Algorithm is developed as a fast and accurate method to determine the proton–proton collision time by the measurements of the TOF detector at the SPD experiment. By using this reliable method for the t0 determination we compare different approaches for the particle identification procedure based on TOF signals.

Localisation and identification of polystyrene particles in tissue sections using Raman spectroscopic imaging

The uptake of microplastic particles (MPP) by organisms is frequently described and poses a potential risk for these organisms and ultimately for humans either through direct uptake or trophic transfer. Currently, the in-situ detection of MPP in organisms is typically based on histological examination of tissue sections after uptake of fluorescently-labelled MPP and is thus not feasible for environmental samples. The alternative approach is purification of MPP from whole organisms or organs by chemical digestion and subsequent spectroscopic detection (FT-IR or Raman). While this approach is feasible for un-labelled particles it goes along with loss of any spatial information related to the location in the tissue. In our study we aimed at providing a workflow for the localisation and identification of non-fluorescent and fluorescent polystyrene (PS) particles (fragments, size range 2–130 μm) in tissue sections of the model organism Eisenia fetida with Raman spectroscopic imaging (RSI). We provide methodological approaches for the preparation of the samples, technical parameters for the RSI measurements and data analysis for PS differentiation in tissue sections. The developed approaches were combined in a workflow for the in-situ analysis of MPP in tissue sections. The spectroscopic analysis requires differentiation of spectra of MPP and interfering compounds, which is challenging given the complexity of tissue. Therefore, a classification algorithm was developed to differentiate PS particles from haem, intestinal contents and surrounding tissue. It allows the differentiation of PS particles from protein in the tissue of E. fetida with an accuracy of 95%. The smallest PS particle detected in the tissue was 2 μm in diameter. We show that it is possible to localise and identify non-fluorescent and fluorescent ingested PS particles directly in tissue sections of E. fetida in the gut lumen and the adjacent tissue.

The recovery of aerosol-sized microplastics in highly refractory vegetal matrices for identification by automated Raman microspectroscopy

Ombrotrophic peatlands are fed uniquely by atmospheric inputs and therefore have much potential as temporal archives of atmospheric microplastic (MP) deposition, yet the recovery and detection of MP within an almost purely organic matrix is challenging. This study presents a novel peat digestion protocol using sodium hypochlorite (NaClO) as a reagent for biogenic matrix removal. NaClO is more efficient than hydrogen peroxide (H2O2). By using purged air-assisted digestion, NaClO (50 vol%) reached 99% matrix digestion compared with 28% and 75% by H2O2 (30 vol%) and Fenton's reagent, respectively. At a concentration of 50 vol% NaClO did however chemically disintegrate small amounts (<10 mass %) of polyethylene terephthalate (PET) and polyamide (PA) fragments in the millimeter size range. Observation of PA6 in natural peat samples, while not found in the procedural blanks, questions whether PA is fully disintegrated by NaClO. The protocol was applied to three commercial sphagnum moss test samples, in which MP particles in the range of 0.8–65.4 μm were detected by Raman microspectroscopy. The MP mass% was determined at 0.012% corresponding to 129 thousand MP particles/g, of which 62% were smaller than 5 μm and 80% were smaller than 10 μm, yet were accountable for only 0.4% (500 ng) and 3.2% (4 μg) of the total mass of MP, respectively. These findings underline the importance of the identification of particles Ø < 5 μm when investigating atmospheric MP deposition. The MP counts were corrected for MP recovery loss and procedural blank contamination. MP spike recovery following the full protocol was estimated at 60%. The protocol offers an efficient way of isolating and pre-concentrating most aerosol sized MPs in large quantities of refractory vegetal matrices and enables the automated μRaman scanning of thousands of particles at a spatial resolution on the order of 1 μm.

Characterization and quantification of oxidative stress induced particle debris from polypropylene surgical mesh

AbstractExplanted polypropylene (PP) surgical mesh has frequently been reported to show surface alterations, such as cracks and flaking. However, to date the consequence of PP mesh degradation is not clearly understood, particularly its potential to influence the biological host response of surrounding tissues. Of particular concern is a possible host reaction to polypropylene particles released through degradation of surgical mesh in vivo. This concern is driven by previous studies which have postulated that an oxidative stress environment has the potential to etch away particles from the surface of a PP fibers. The release of such particles is of considerable significance as particles in the nano‐ to micro range have been shown to have the capacity to irritate cells and stimulate the immune system. The authors are not aware of any previous studies that have attempted to characterize, quantify or identify any particles released from PP mesh after exposure to an oxidative stress environment. Characterization of the PP mesh, post oxidative stress exposure, including identification of particles was achieved through application of a range of techniques: low voltage‐scanning electron microscopy (LV‐SEM), pyrolysis gas chromatography mass spectrometry (Pyr‐GCMS), nano‐Fourier transform infrared spectroscopy (nano‐FTIR), scattering‐type, scanning near‐field optical microscopy (s‐SNOM), atomic force microscopy (AFM), attenuated total reflectance‐Fourier transform infrared spectroscopy (ATR‐FTIR) and uniaxial tensile testing. The findings of this study indicate that oxidative stress alone is a major factor in the production of PP particle debris. PP debris identified within solution, using Pyr‐GCMS, was shown to be in order of the micron scale.

Identification and quantification of tire wear particles by employing different cross-validation techniques: FTIR-ATR Micro-FTIR, Pyr-GC/MS, and SEM

Tire wear particles (TWPs) are one of the environment's most important emission sources of microplastics. In this work, chemical identification of these particles was carried out in highway stormwater runoff through cross-validation techniques for the first time. Optimization of a pre-treatment method (i.e., extraction and purification) was provided to extract TWPs, avoiding their degradation and denaturation, to prevent getting low recognizable identification and consequently underestimates in the quantification. Specific markers were used for TWPs identification comparing real stormwater samples and reference materials via FTIR-ATR, Micro-FTIR, and Pyrolysis–gas-chromatography–mass spectrometry (Pyr-GC/MS). Quantification of TWPs was carried out via Micro-FTIR (microscopic counting); the abundance ranged from 220,371 ± 651 TWPs/L to 358,915 ± 831 TWPs/L, while the higher mass was 39,6 ± 9 mg TWPs/L and the lowest 31,0 ± 8 mg TWPs/L. Most of the TWPs analyzed were less than 100 μm in size. The sizes were also confirmed using a scanning electron microscope (SEM), including the presence of potential nano TWPs in the samples. Elemental analysis via SEM supported that a complex mixture of heterogeneous composition characterizes these particles by agglomerating organic and inorganic particles that could derive from brake and road wear, road pavement, road dust, asphalts, and construction road work. Due to the analytical lack of knowledge about TWPs chemical identification and quantification in scientific literature, this study significantly contributes to providing a novel pre-treatment and analytical methodology for these emerging contaminants in highway stormwater runoff. The results of this study highlight the uttermost necessity to employ cross-validation techniques, i.e., FTIR-ATR, Micro-FTIR, Pyr-GC/MS, and SEM for the TWPs identification and quantification in the real environmental samples.

Research on the application of pulse shape discrimination method in (n, lcp) reaction cross-section measurements

The China Spallation Neutron Source Back-n white neutron source supplies neutrons with a wide energy range for nuclear data measurements and nuclear techniques. In cross-section measurements of neutron induced light charged-particle emission (n, lcp) reactions at Back-n, the identification of charged particles are usually achieved with the ΔE-E method or a two-dimensional spectrum of the signal amplitude of the particles deposited in the detector and the corresponding neutron energies derived from the time of flight, called amplitude-En method. Nevertheless, a deficiency still exists in the measurement range and the identification performance with these methods. The pulse shape discrimination (PSD) method with a silicon detector is supposed to be a supplement to the amplitude-En and ΔE-E methods. With silicon detectors, experiments have been performed to compare the performance of the amplitude-En and the PSD methods. The PSD method shows a good performance in identifying light charged particles in the MeV neutron energy region. Alphas and tritons emitted from 6Li(n, t) reaction can be identified with the low energy threshold around 2.5 MeV. This provides an effective method for the cross-section measurement of (n, lcp) reactions at Back-n.

Polarimetric imaging for the detection of synthetic models of SARS-CoV-2: A proof of concept

ObjectiveTo conduct a proof-of-concept study of the detection of two synthetic models of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using polarimetric imaging. ApproachTwo SARS-CoV-2 models were prepared as engineered lentiviruses pseudotyped with the G protein of the vesicular stomatitis virus, and with the characteristic Spike protein of SARS-CoV-2. Samples were prepared in two biofluids (saline solution and artificial saliva), in four concentrations, and deposited as 5-µL droplets on a supporting plate. The angles of maximal degree of linear polarization (DLP) of light diffusely scattered from dry residues were determined using Mueller polarimetry from87 samples at 405 nm and 514 nm. A polarimetric camera was used for imaging several samples under 380–420 nm illumination at angles similar to those of maximal DLP. Per-pixel image analysis included quantification and combination of polarization feature descriptors in 475 samples. Main resultsThe angles (from sample surface) of maximal DLP were 3° for 405 nm and 6° for 514 nm. Similar viral particles that differed only in the characteristic spike protein of the SARS-CoV-2, their corresponding negative controls, fluids, and the sample holder were discerned at 10-degree and 15-degree configurations. SignificancePolarimetric imaging in the visible spectrum may help improve fast, non-contact detection and identification of viral particles, and/or other microbes such as tuberculosis, in multiple dry fluid samples simultaneously, particularly when combined with other imaging modalities. Further analysis including realistic concentrations of real SARS-CoV-2 viral particles in relevant human fluids is required. Polarimetric imaging under visible light may contribute to a fast, cost-effective screening of SARS-CoV-2 and other pathogens when combined with other imaging modalities.