Identification Of Particles Research Articles

A Hybrid Fuzzy Evaluation Method for Quantitative Risk Classification of Barrier Lakes Based on an AHP Method Extended by D Numbers

The risk classification of barrier lakes is the key to conducting emergency treatment in a scientific manner. However, risk classification faces difficulties such as a short time for risk evaluation, complex evaluation indicators, difficulty in obtaining information quickly, and quantifying index weights. Based on this, this paper constructs a quantitative risk classification model for barrier lakes based on D-AHP. On the basis of studies on nearly 100 cases of barrier lakes, an eight-factor evaluation index system and quantitative classification are proposed. The methods of rapid calculation of reservoir capacity curve of barrier lakes and intelligent identification of particles on the surface of barrier bodies were developed, which realized the rapid acquisition of eight-factor evaluation index information in an emergency environment. The D-AHP method dealt with inconsistent weight assignment to evaluation factors by experts, which helped achieve weight quantification of eight factors. The risk assessment on 15 barrier lakes such as Tangjiashan barrier lake shows that the conclusions drawn for the risk classification method proposed in this paper are basically consistent with those of the traditional table-lookup method. However, the table-lookup method ignores cumulative loss impacts on the risk level of barrier lakes and considers the extremely severe loss of barrier lakes as a sufficient condition for the evaluation level to be grade I, and thus a deviation in the evaluation. The risk classification method proposed in this paper is more reasonable and reliable.

All black: a microplastic extraction combined with colour-based analysis allows identification and characterisation of tire wear particles (TWP) in soils

While tire wear particles (TWP) have been estimated to represent more than 90% of the total microplastic (MP) emitted in European countries and may have environmental health effects, only few data about TWP concentrations and characteristics are available today. The lack of data stems from the fact that no standardized, cost efficient or accessible extraction and identification method is available yet. We present a method allowing the extraction of TWP from soil, performing analysis with a conventional optical microscope and a machine learning approach to identify TWP in soil based on their colour. The lowest size of TWP which could be measured reliably with an acceptable recovery using our experimental set-up was 35 µm. Further improvements would be possible given more advanced technical infrastructure (higher optical magnification and image quality). Our method showed a mean recovery of 85% in the 35–2000 µm particle size range and no blank contamination. We tested for possible interference from charcoal (as another black soil component with similar properties) in the soils and found a reduction of the interference from charcoal by 92% during extraction. We applied our method to a highway adjacent soil at 1 m, 2 m, 5 m, and 10 m and detected TWP in all samples with a tendency to higher concentrations at 1 m and 2 m from the road compared to 10 m from the road. The observed TWP concentrations were in the same order of magnitude as what was previously reported in literature in highway adjacent soils. These results demonstrate the potential of the method to provide quantitative data on the occurrence and characteristics of TWP in the environment. The method can be easily implemented in many labs, and help to address our knowledge gap regarding TWP concentrations in soils.

Expanding sample volume for microscopical detection of nanoplastics

The extent of nanoplastic pollution has raised severe environmental and health concerns. While the means for microplastic detection are abundant, improved tools for nanoplastic detection are called-for. State-of-the-art microscopic techniques can detect nanoplastics down to tens of nanometers, however, only from small sample sizes (typically ∼10μl). In this work, we describe a method that enables sampling of 1 liter of seawater by the means of correlative Raman- and SEM-techniques. This is achieved by adapting common microplastic sample purification protocols to suit the nanoplastic study. In addition, we decorate a membrane filter with SERS-property to amplify the Raman signals. Together, the purification method combined with the use of the SERS-activated-membrane-filter enables identification and imaging of individual nanoplastic particles from significantly larger sample sizes than before. In the nanoscale the average recovery rate is 5 %. These results aim to provide useful tools for researchers in the fight against plastic pollution.

Analytical calculation of the kinetic q factor and resonant response of toroidally confined plasmas

Symmetry-breaking perturbations in axisymmetric toroidal plasma configurations have a drastic impact on particle, energy, and momentum transport in fusion devices, thereby affecting their confinement properties. The perturbative modes strongly affect particles with specific kinetic characteristics through resonant mode–particle interactions. In this work, we present an analytical calculation of the kinetic q factor, enabling the identification of particles with kinetic properties that meet the resonant conditions. This allows us to predict the locations and structures of the corresponding resonant island chains, as well as the existence of transport barriers in the particle phase space. The analytical results, derived for the case of a large aspect ratio configuration, are systematically compared to numerical simulations, and their domain of validity is thoroughly investigated and explained. Our findings demonstrate that calculating the kinetic q factor and its dependence on both particle and magnetic field characteristics provides a valuable tool for understanding and predicting the resonant plasma response to non-axisymmetric perturbations. Moreover, this approach can be semi-analytically applied to generic realistic experimental equilibria, offering a low-computational-cost method for scenario investigations under various multi-scale perturbative modes.

A Review of Conventional and Environmentally Sustainable Glitter

ABSTRACTPlastics offer several benefits, such as durability, versatility, and cost‐effectiveness, which have led to their pervasive use in nearly every aspect of modern life. However, their widespread use has led to significant environmental concerns. One of the major issues is their persistence in the environment, where they accumulate and cause contamination. This problem is particularly severe with plastic packaging and microparticles used in cosmetic products. As a result, the cosmetics industry started producing environmentally sustainable glitter. “Biodegradable” glitter is available in distinct compositions, mainly with a central base composed of a biodegradable material. This review focuses on academic research and patents related to glitter and biodegradable glitter, aiming to classify and compare commercially available options. Furthermore, this review analyzes scientific research on conventional glitter and its conclusions regarding environmental impact. For this, we used the keywords “glitter,” “biodegradable glitter,” “eco‐friendly glitter,” “natural glitter,” and “sustainable glitter” in the databases of Wiley, Science Direct, Springer, and Clarivate Analytics ISI—Web of Science. We also performed a bibliometric analysis using the keyword “glitter” in the Web of Science database to obtain a bibliometric map of publication trends in the field using VOSviewer software. The products identified in the search were analyzed for their components and biodegradation potential. These products were classified as “biodegradable” if they were composed entirely of biodegradable constituents and as “eco‐friendly” if only a portion of their constituents was biodegradable. We observed that several glitter products are incorrectly classified as biodegradable, demonstrating the need for standardized terminology that accurately reflects the composition of the glitter particles. Patent applications indicate a growing interest in developing environmentally safe glitter particles in recent years. Studies on conventional glitter mainly focus on its environmental impact or discuss analyzes for the identification and classification of glitter particles. The data gathered highlight the gaps in the field, mainly related to glitter production methods and the standardization of biodegradable glitter. Thus, studies in this direction are highly necessary.

Detection response of the active components of the SciBar Cosmic Ray Telescope at Sierra Negra

The Scibar Cosmic-Ray Telescope (SciCRT) is the most promising detector of the Sierra Negra Cosmic Rays Observatory (SN-CRO). At this location, being a target and a tracker of secondary cosmic rays, the SciCRT offers a high probability of observing solar energetic particles and lower energy galactic cosmic rays (LEGCR); also, it allows the identification of incoming particles by measuring their energy deposition. In this work we present a Geant4-based simulation of the energy deposited by neutrons, γ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\gamma $$\\end{document}-rays, protons, electrons and muons in the optimally running SciCRT components. We also calculated the detection efficiency of the SciCRT at its current state. Our simulation results provide new information about the SciCRT detection response that may be used as a basis to estimate and analyze the energy spectra of primary particles.

Detection of a biological aerosol using optical particle counters

So far, detection and quantification of bio-aerosols requires genotypic or phenotypic identification of every single particle. Successful use of optical particle counters, as a time- and cost-saving alternative, has not been reported to date, indicating the need for further method development. Previously, such studies have focused on pollen. Here we report on a laboratory test of commercially available low-cost optical particle counters from Alphasense (OPC-N3 and OPC-R2) for the quantification of a known fungal aerosol (Botrytis cinerea). Aerosols were quantified using a Grimm Portable Aerosol Spectrometer (11E). Our measurements reveal that the low-cost sensors almost correctly detect the relative particle size distribution of the spores, as compared with the Grimm reference instrument, and potentially can be used in environmental detection of fungal aerosols.

Spectral parametrization of random particle-packings

We present a novel method of particle-monolayer parametrization, based on least-squares fitting to the power spectral density of the monolayer image. An important advantage of this technique is avoiding the identification of individual particles. Using two images of random particle assemblies, found in the literature, we demonstrate the implementation of the method. Specifically, we determine the dimensionless particle radius and surface coverage, with their standard errors. For homogeneous monolayers of monodisperse particles, the errors are on the order of 1%. The errors increase with particle polydispersity and monolayer heterogeneity. We also show that the method is not sensitive to distortion in apparent particle size, frequently occurring in optical microscopy and AFM measurements.

Development of PIND system based on resonant sensors and research on material identification methods for redundant particles

Abstract Aiming at the problems of the existing particle impact noise detection (PIND) system, such as low detection accuracy, high rate of misjudgment and missed judgment, and the inability to judge the material of internal redundant particles in real time, a method for automatic detection and material identification of redundant particles based on neural network recognition is proposed, and an automatic detection system for redundant particles of sealed electronic devices is developed. The system is equipped with multi-channel piezoelectric resonant sensors, which convert the redundant particle impact noise signal into a voltage signal, and then transmit the signal to the host using a high-speed data acquisition system. The upper computer uses a short-term energy threshold detection method to extract signal pulses; Then, a variety of time and frequency domain features are extracted and combined with wavelet domain features; Finally, by training the multi output neural network prediction model, it can automatically judge whether there are redundant particles in the tested parts, and automatically give the material information of the redundant particles. Experiments show that the accuracy of the system for material recognition of redundant particles with a mass greater than 0.1 mg can reach 88.6%.

The Target Detection of Wear Particles in Ferrographic Images Based on the Improved YOLOv8

An enhanced YOLOv8 algorithm is proposed in the following paper to address challenging issues encountered in ferrographic image target detection, such as the identification of complex-shaped wear particles, overlapping and intersecting wear particles, and small and edge-wear particles. This aim is achieved by integrating the main body network with the improved Deformable Convolutional Network v3 to enhance feature extraction capabilities. Additionally, the Dysample method is employed to optimize the upsampling technique in the neck network, resulting in a clearer fused feature image and improved precision for detecting small and edge-wear particles. In the head network, parameter sharing simplifies the detection head while enhancing convergence speed and precision through improvements made to the loss function. The experimental results of the present study demonstrate that compared to the original algorithm, this enhanced approach achieves an average precision improvement of 5.6% without compromising the detection speed (111.6FPS), therefore providing valuable support for online monitoring device software foundations.

Diagnostic and prophylactic potential of a stabilized foot-and-mouth disease serotype Asia1 virus like particles designed through a structure guided approach

Intact capsids of foot-and-mouth disease virus (FMDV) play a vital role in eliciting a protective immune response. Any change in the physico-chemical environment of the capsids results in dissociation and poor immunogenicity. Structural bioinfomatics studies have been carried out to predict the amino acids at the interpentameric region that resulted in the identification of mutant virus-like particles(VLPs) of FMDV serotype Asia1/IND/63/1972. The insect cell expressed VLPs were evaluated for their stability by sandwich ELISA. Among 10 mutants, S93H showed maximum retention of antigenicity at different temperatures, indicating its higher thermal stability as revealed by the in-silico analysis and retained the antigenic sites of the virus demonstrated by Sandwich ELISA. The concordant results of the liquid phase blocking ELISA for estimation of antibody titre of known sera with stable mutant VLP as antigen in place of virus antigen demonstrate its diagnostic potential. The stable mutant VLP elicited a robust immune response with 85.6 % protection in guinea pigs against virus challenge. The stabilized VLP based antigen requires minimum biosafety and cold storage for production and transit besides, complying with differentiation of infected from vaccinated animals. It can effectively replace the conventional virus handling during antigen production for prophylactic and diagnostic use.

Bayesian approach to particles identification in the MPD experiment

Identification of particles generated by ion collisions in the NICA collider is one of the basic functions of the Multipurpose Detector (MPD). The main means of identification in MPD are the time-of-flight system (TOF) and the time-projection chamber (TPC). The article considers the optimization of the algorithms of particles identification by these systems. Under certain conditions, the use of the statistical Bayesian approach has made it possible to achieve an optimal ratio of the efficiency of particle identification and contamination by incorrectly defined particles.

Identification of microalgal particles using pixel feature analysis of Mueller matrix images

Fine sorting of aquatic particles is of great significance for water environment monitoring. Natural water contains aquatic particles that exhibit a high degree of diversity and complexity, and the identification of aquatic particles remains a persistent challenge in the field. In this article, we propose a new technique for identifying the target species of microalgal particles by using the pixel feature analysis of Mueller matrix images. This technique is independent of any prior knowledge or data about the existing particles in the environment, which is advantageous when applied to real-world situations. The pixel-level polarimetric features are fully leveraged to construct polarization feature templates, which can be used to characterize and filter specific microalgal particles in complex environments. This method could enable the accurate detection of harmful algal blooms species in natural water, which can facilitate early warning of algal blooms. The preliminary results show that the recall rate reached 97.2%, and the average accuracy is 98.9%, which demonstrate the effectiveness of this approach for identifying the target species of aquatic particles in natural water.

Characteristics of the prototype LAMPS TPC for low-energy nuclear collisions at RAON

The time projection chambers (TPCs) with triple GEM amplifiers are tested at HIMAC using 12C beams to investigate their nucleus track reconstruction capability. The two identical TPCs, each having an active volume of 96 × 64 × 144 mm3 and rectangular pixel pads, have been developed as prototypes of the LAMPS collaboration’s low-energy active target TPC (AT-TPC). The responses to light nuclei, such as particle identification and spatial resolution, have been investigated. The Si-CsI detectors located next to the TPCs further facilitate the identification of incident particles, as well as the matching tracks. This paper presents the technical details of the experimental setup, pulse shape analysis, tracking algorithm, and spatial resolution measurement. Comparisons of the experimental data with the calculations by the Monte Carlo (MC) simulation are also discussed.

Single-molecule detection of SARS-CoV-2 N protein on multilayered plasmonic nanotraps with surface-enhanced Raman spectroscopy

The spread of the SARS-CoV-2 virus has had an unprecedented impact, both by posing a serious risk to human health and by amplifying the burden on the global economy. The rapid identification of the SARS-CoV-2 virus has been crucial to preventing and controlling the spread of SARS-CoV-2 infections. In this study, we propose a multilayered plasmonic nanotrap (MPNT) device for the rapid identification of single particles of SARS-CoV-2 virus in ultra-high sensitivity by surface-enhanced Raman scattering (SERS). The MPNT device is composed of arrays of concentric cylindrical cavities with Ag/SiO2/Ag multilayers deposited on the top and at the bottom. By varying the diameter of the cylinders and the thickness of the multilayers, the resonant optical absorption and local electric field were optimized. The SERS enhancement factors of the proposed device are of the order of 108, which enable the rapid identification of SARS-CoV-2 N protein in concentrations as low as 1.25 × 10−15－12.5 × 10−15 g mL−1 within 1 min. The developed MPNT SERS device provides a label-free and rapid detection platform for SARS-CoV-2 virus. The general nature of the device makes it equally suitable to detect other infectious viruses.

Absence of microplastic bioaccumulation in cod fillets from plastic-polluted western Norwegian waters

Plastics are synthetic, persistent materials that are distributed worldwide. An important concern is whether microplastics (MP) can bioaccumulate in the food chain and pose a threat to human consumers. We studied MP in the fillet of resident coastal cod from a plastic-polluted area in Western Norway, where long-range transported marine litter accumulates and MP are generated on shore. We dissected the fish and processed the samples in an MP-free lab using gentle enzymatic treatment. Micro Fourier transform infrared spectroscopy (µFTIR) was used for identification of particles down to 10 µm. The fish were 40 to 74 cm, corresponding to 2-6 years old. 29 particles were observed in fillets from eight of the 23 individuals. The mean particle count was 1.55 ± 2.75 nMP/100 g, and the mean concentration was 2.81 ± 8.33 µg/kg wet weight. Six polymer groups were identified, where polypropylene (33%) and polyethylene (30%) were the most frequent. The majority (86%) of the particles were fragments ranging from 32-100 µm. Fibers and fragments over 200 µm were observed. The largest particle was a PP particle of 258.2 µm. Controls showed minimal contamination and the procedural blanks were negative. There was no significant correlation between age, body condition, time of capture, and MP concentrations, and no evidence of bioaccumulation of MP in the fillet of older fish after in situ exposure. MP in food is of concern for human consumption and emphasizes the importance of understanding MP distribution and fate as well as reducing and controlling plastic release into the environment.

Hyperspectral imaging for identification of irregular-shaped microplastics in water

In this article, we demonstrate detection and identification of ten microplastic types directly in a water sample using an identification table derived from microplastic hyperspectral images. We selected a total of fourteen wavelengths which can be used to distinguish these ten microplastic types. We enhanced the visibility of these wavelengths by computationally removing water and baseline correcting with reflectance at 1550 nm. This method avoids, prevents, and eases most of the laborious sample preparation mandatory prior to analysis with robust techniques such as Raman spectroscopy and Fourier transform infrared spectroscopy. The ten different plastics were studied in water, first separately and then in a mixture. The microplastic concentrations varied depending on microplastic type and were kept <12 mg/ml per type. Finally, detection and identification were confirmed pixel-wise in a hyperspectral image of a realistic water matrix simulant including mixtures of only a few microplastic particles. All measurements have been performed with microplastics of different sizes and irregular shapes made in-house by milling commercial pellets and sheets. It enabled the establishment of a procedure for the identification of these vicious particles in real water samples. The present measurement setup of hyperspectral imaging and method of data analysis of a mixture of microplastics directly from a water-based sample may open a path towards fast, reliable, and on-site detection.

Rapid and non-destructive identification of plastic particles through THz technology and machine learning

For fast and accurate non-destructive differentiation of plastic pellets with similar appearance during import and export inspections, as well as for quality control purposes. Terahertz technology offers a non-destructive analysis approach for samples from diverse fields. By integrating deep learning algorithms with terahertz time-domain spectroscopy (THz-TDS) and linear discriminant analysis (LDA), it is possible to establish a highly accurate terahertz spectroscopy qualitative identification model. Additionally, the incorporation of principal component analysis (PCA) enables the application of this model to higher dimensional data. Notable differences in absorption coefficient, phase difference, and refractive index are observed among different plastic particles. The implementation of this rapid, accurate, and non-destructive detection method can greatly facilitate the testing and identification of plastic particles in customs and quality inspection departments.

Studies on a hadronic calorimeter with MPGD technology for a future Muon Collider experiment

In the context of the European strategy for particle physics, the Multi-TeV Muon Collider has emerged as a compelling alternative for advancing our understanding of the Standard Model, after the full exploitation of the High-Luminosity LHC. The physics programme at the Muon Collider includes precise measurements in the Higgs boson sector and the search for new physics at the TeV scale. Achieving these goals relies on accurate full event reconstruction, including the identification and precise four-momentum estimation of various particles. The Particle Flow (PF) algorithm is one of the most suitable approach for this task, exploiting information from tracking, calorimeter, and muon detectors for particle identification and measurements of their momenta/energies. Tracking detectors measure charged particle momenta, while calorimeters provide energy measurements for photons and neutral hadrons. Therefore, a combination of an exceptional tracking system and high-granularity calorimeters is necessary. However, one of the biggest challenges for a future experiment at the Muon Collider is to discriminate the product of the μμ collisions from the intense beam-induced-background (BIB), due to the unstable nature of muons, whose decay products interact with the detector material. To address this, an innovative hadronic calorimeter (HCAL) based on Micro Pattern Gas Detectors (MPGDs) is proposed. MPGDs offer robust technology for high radiation environments and a high granularity for precise spatial measurements. Dedicated studies are needed to assess and optimize the performance of an MPGD-based HCAL, including the development of medium-scale prototypes for performance measurements. The response of HCAL to incoming particles is examined through Monte Carlo simulations using Geant4, comparing the performance of digital and semi-digital readouts, with energy resolution as the figure of merit. The simulated geometry will be integrated into the Muon Collider software to study its impact on jet reconstruction within the full apparatus and in the presence of BIB. The simulation will be also validated through the test of a small-size calorimeter cell equipped with advanced resistive MPGD technologies, namely resistive MicroMegas, resistive μRWELL and RP-WELL.

Identification and Visualization of Polystyrene Microplastics/Nanoplastics in Flavored Yogurt by Raman Imaging.

The contamination of food by microplastics has garnered widespread attention, particularly concerning the health risks associated with small-sized microplastics. However, detecting these smaller microplastics in food poses challenges attributed to the complexity of food matrices and instrumental and method limitations. Here, we employed Raman imaging for visualization and identification of polystyrene particles synthesized in polymerization reactions, ranging from 400 to 2600 nm. We successfully developed a quantitative model of particle size and concentration for polystyrene, exhibiting excellent fit (R2 of 0.9946). We established procedures for spiked flavored yogurt using synthesized polystyrene, providing fresh insights into microplastic extraction efficiency. Recovery rates calculated from models validated the method's feasibility. In practical applications, the assessment of the size, type, shape, and quantity of microplastics in unspiked flavored yogurt was conducted. The most common polymers found were polystyrene, polypropylene, and polyethylene, with the smallest polystyrene sizes ranging from 1 to 10 μm. Additionally, we conducted exposure assessments of microplastics in branded flavored yogurt. This study established a foundation for developing a universal method to quantify microplastics in food, covering synthesis of standards, method development, validation, and application.