Minimum Number Of Particles Research Articles

Numerical investigation on sample population requirement for mean particle diameter analysis

The determination of the minimum particle number needed to meet error specifications and confidence levels poses a fundamental challenge in particle size analysis. Conventional models, primarily designed for log-normal distributions, may yield inaccuracies when applied to other distribution functions. This study introduces a numerical approach to explore the necessary sample size for measuring different types of mean diameters. The methodology involves distribution conversion, sample generation, repeated sampling, and error estimation. Specifically, Gates–Gaudin–Schuhmann (GGS) and Rosin–Rammler (RR) distributions serve as representative models in this investigation. The impact of sample size, span ratio, and boundary sizes on relative errors is examined. Additionally, an empirical model is formulated, simplifying the calculation of the requisite sample size when the relative error and mass-based span ratio are provided. The proposed method is also verified using experimental particle size data that follow the RR distribution.

How many microplastics do you need to (sub)sample?

Analysis of microplastics in the environment requires polymer characterization as a confirmation step for suspected microplastic particles found in a sample. Material characterization is costly and can take a long time per particle. When microplastic particle counts are high, many researchers cannot characterize every particle in their sample due to time or monetary constraints. Moreover, characterizing every particle in samples with high plastic particle counts is unnecessary for describing the sample properties. We propose an a priori approach to determine the number of suspected microplastic particles in a sample that should be randomly subsampled for characterization to accurately assess the polymer distribution in the environmental sample. The proposed equation is well-founded in statistics literature and was validated using published microplastic data and simulations for typical microplastic subsampling routines. We report values from the whole equation but also derive a simple way to calculate the necessary particle count for samples or subsamples by taking the error to the power of negative two. Assuming an error of 0.05 (5 %) with a confidence interval of 95 %, an unknown expected proportion, and a sample with many particles (> 100k), the minimum number of particles in a subsample should be 386 particles to accurately characterize the polymer distribution of the sample, given the particles are randomly characterized from the full population of suspected particles. Extending this equation to simultaneously estimate polymer, color, size, and morphology distributions reveals more particles (620) would be needed in the subsample to achieve the same high absolute error threshold for all properties. The above proposal for minimum subsample size also applies to the minimum count that should be present in samples to accurately characterize particle type presence and diversity in a given environmental compartment.

Validation of OpenMC Code Criticality Value Calculation for GFR Reactor with UN-PuN Fuel

The use of OpenMC code needs to be validated with other code, so that an accurate and valid criticality value is generated. The validation value is affected by the minimum number of particles used. Determination of the minimum number of particles is carried out by varying the number of particles so that they have a convergent value, which is in the range of 100-50000 particles. The results of determining the minimum number of particles are then used to find the minimum number of cycles consisting of active and inactive batches. This study uses the criticality value parameter in the form of an effective multiplication factor (k-eff) as a benchmark for code accuracy. The k-eff values generated by OpenMC and SRAC are then compared and the validation error value is searched. The error value is found by calculating the k-eff (Δk-eff) difference between the two codes. OpenMC code can be said to be validated if it has an error value of less than 1% against the calculation results of the SRAC code. The calculation results of determining the minimum number of particles show k-eff values in the range of 35000 - 50000 minimal changes significantly or not fluctuating. The calculation of k-eff and entropy in cycle 500 shows a convergent value at the active batch value of >30. Based on the data cycle, researchers used 100 active batches and 30 inactive batches to perform validation calculations. The results of the validation calculation using UN-PuN fuel with a plutonium material composition of 10% showed a maximum k-eff error value of 0.819%. The Δk-eff obtained is 0.008.

A minority of final stacks yields superior amplitude in single-particle cryo-EM

Cryogenic electron microscopy (cryo-EM) is widely used to determine near-atomic resolution structures of biological macromolecules. Due to the low signal-to-noise ratio, cryo-EM relies on averaging many images. However, a crucial question in the field of cryo-EM remains unanswered: how close can we get to the minimum number of particles required to reach a specific resolution in practice? The absence of an answer to this question has impeded progress in understanding sample behavior and the performance of sample preparation methods. To address this issue, we develop an iterative particle sorting and/or sieving method called CryoSieve. Extensive experiments demonstrate that CryoSieve outperforms other cryo-EM particle sorting algorithms, revealing that most particles are unnecessary in final stacks. The minority of particles remaining in the final stacks yield superior high-resolution amplitude in reconstructed density maps. For some datasets, the size of the finest subset approaches the theoretical limit.

Convergence of halo statistics: code comparison between rockstar and compaso using scale-free simulations

ABSTRACT In this study, we perform a halo-finder code comparison between rockstar and compaso. Based on our previous analysis aiming at quantifying resolution of N-body simulations by exploiting large (up to N = 40963) simulations of scale-free cosmologies run using abacus, we focus on convergence of the halo mass function, two-point correlation function, and mean radial pairwise velocities of halo centres selected with the aforementioned two algorithms. We establish convergence, for both rockstar and compaso, of mass functions at the 1 per cent precision level and of the mean pairwise velocities (and also two-point correlation function) at the 2 per cent level. At small scales and masses, we find that rockstar exhibits greater self-similarity. We also highlight the role played by the merger-tree post-processing of compaso haloes on their convergence. Finally, we give resolution limits expressed as a minimum particle number per halo in a form that can be directly extrapolated to Lambda cold dark matter.

Portable digital holographic particle analyzer (DHPA) for pneumatically conveyed fuel monitoring: Design and validation

Efficient, clean, and intelligent operation of coal-fired power plants plays a crucial role in the pursuit of energy conservation and carbon reduction. Online monitoring of pneumatically conveyed fuel is essential to furnish fundamental data for combustion optimization. This paper proposes a digital holographic particle analyzer (DHPA) with a portable design for monitoring pulverized fuel fineness. DHPA consists of a particle sampling and recycling probe, a digital inline holographic system, a controlling module, and software. The probe extracts representative fuel particles from the pipe, which are subsequently measured in a three-dimensional field by pulsed digital inline holography before being returned to the pipe. The performance tests of DHPA for coal fineness measurement encompass investigations into the dispersion of particles within the measurement volume, the minimum particle number necessary for reliable results, a comparison with the laser particle analyzer and sieving method, as well as sensitivity to mill parameter changes. It is recommended to capture at least 4 × 104 particles to ensure a credible outcome. DHPA exhibits comparability with the traditional sieving method, displaying an absolute error of 1.48% for fineness R90 measurement. Moreover, DHPA has been successfully employed for measuring and optimizing the coal fineness at full-scale power plants. On-site measurements have unequivocally confirmed the feasibility and effectiveness of DHPA for monitoring pulverized fuel.

Message Passing-Based 9-D Cooperative Localization and Navigation With Embedded Particle Flow

Cooperative localization (CL) is an important technology for innovative services such as location-aware communication networks, modern convenience, and public safety. We consider wireless networks with mobile agents that aim to localize themselves by performing pairwise measurements amongst agents and exchanging their location information. Belief propagation (BP) is a state-of-the-art Bayesian method for CL. In CL, particle-based implementations of BP often are employed that can cope with non-linear measurement models and state dynamics. However, particle-based BP algorithms are known to suffer from particle degeneracy in large and dense networks of mobile agents with high-dimensional states. This paper derives the messages of BP for CL by means of particle flow, leading to the development of a distributed particle-based message-passing algorithm which avoids particle degeneracy. Our combined particle flow-based BP approach allows the calculation of highly accurate proposal distributions for agent states with a minimal number of particles. It outperforms conventional particle-based BP algorithms in terms of accuracy and runtime. Furthermore, we compare the proposed method to a centralized particle flow-based implementation, known as the exact Daum-Huang filter, and to sigma point BP in terms of position accuracy, runtime, and memory requirement versus the network size. We further contrast all methods to the theoretical performance limit provided by the posterior Cramér-Rao lower bound (PCRLB). Based on three different scenarios, we demonstrate the superiority of the proposed method.

Effects of torsional control on the optical and electronic properties of monolayer NbS2: A first-principles study

Based on the first principle calculation, the torsional deformation control of single-layer graphene transition metal disulfide NbS 2 at different angles is systematically studied in this paper. The electronic properties include the energy gap change of the electronic band structure, the composition of the density of States, the trends of the real and imaginary parts of the dielectric function, and the electronic and optical properties such as absorption and reflection spectra. Our analysis shows that distortion can change the bond length of NbS 2 . Under torsion, the initial key length is 2.492 A, the shortest is 2.240 A, and the longest extended key is 2.744 A. On the other hand, the study of electronic and optical properties shows that the band structure of NbS 2 will have a split band gap at the Fermi level. When the torsion angle is 12 °, we notice that the electronic and optical properties of NbS 2 will change significantly, the energy band spacing will increase, and the corresponding minimum particle number will decrease significantly. The optical properties show that the torsion system can improve the storage capacity and polarizability of the material, improve the local optical response, and strongly impact the properties of the ultraviolet region. At the same time, it can improve the optical response to visible light and photocatalytic activity and effectively improve the transmittance of the material itself.

Optimization in PIV algorithm for visualizing vortices in bubble wake

The PIV (Particle image velocimetry) is the most commonly used method for flow field observation because of its high efficiency and non-interference to the flow field. This study aims to clarify the optimal parameter conditions used in the cross-correlation algorithm of PIV for flow fields with vortices. The influence factors on the error of the cross-correlation algorithm are analyzed and discussed using a synthetic flow field, including the seeding conditions, the velocity gradient and vortex size. It is confirmed that the minimum particle number density per interrogation window is about 10, which generally limits the minimum size of the window. For a vortex, when the vortex size is fixed, the velocity gradient corresponding to the characteristic velocity both controls the lower and upper limitation of window size. For a relatively small vortex, generally a window not larger than the vortex size is asked. Then, a strategy to improve the observation based on the existing equipment is discussed and applied to visualize a rising bubble wake based on LIF (Laser-induced fluorescence) images.

Sampling and sub-sampling of granular waste: size of a representative sample in terms of number of particles

This paper elucidates the theoretical principles behind the calculation of the size of a representative sample of granular solid waste. The key concept is the number of particles that must be present in a sub-portion of matter to be representative of a larger portion of matter. This depends on the fraction of particles in the waste batch showing the properties of interest, which shall be measured. A representative sample must include a fraction of particles of interest reliably similar to that of the waste batch to be characterized, with a controlled variability. In this context, it is demonstrated that the number of particles of interest that must be collected in a representative sample is 100. From this requirement, the mass of a representative sample can be calculated based on the knowledge of the frequency of particles of interest of the waste lot to be characterized. Data on particles concentrations in different samples of WEEE plastic scraps exemplifies how the presence in the sample of enough rare particles showing the property of interest is key to ensure reliable measurements. Further, the assumptions made on the controlled degree of variability to determine the minimum number of particles are discussed based on data on achievable intra- and inter-laboratory variability of analytical standards for waste characterization. Accordingly, the mass of laboratory samples and test portions recommended in published sampling plans or analytical standards are assessed for the occurring number of particles.

Determining a representative element volume for DEM simulations of samples with non-circular particles

Numerical studies on the number of particles or system size required to attain a representative element volume (REV) for discrete element method (DEM) simulations of granular materials have almost always considered samples with spherical or circular particles. This study considers how many particles are needed to attain a REV for 2D samples of 2-disc cluster particles where the particle aspect ratio (AR) was systematically varied. Dense and loose assemblies of particles were simulated. The minimum REV was assessed both by considering the repeatability of static packing characteristics and the shearing behaviour in biaxial compression tests, and by investigating the effect of sample size on the measured characteristics and observed shearing behaviour. The repeatability of the data considered generally improved with increasing sample size. The packing characteristics of the dense samples were more repeatable suggesting that the minimum REV reduces with increasing packing density. The minimum REV was observed to be sensitive to the characteristic measured. Although the overall responses of the samples during shear deformation were similar irrespective of the sample sizes, the smaller the sample size, the higher the fluctuations observed in the responses. Analysis of the coefficient of variation of the fluctuations around the critical state stress ratio can provide insight as to whether a REV is attained. The particle AR influences the effect of sample size on shearing characteristics and thus the minimum number of particles required to attain a REV; this can be explained by the influence of AR on the number of contacts within the samples.

Background mode selection during asymptotic-de Sitter inflation

For the first time, we choose non-flat vacuum mode for background spacetime based on the minimum number of created particles during early non-de Sitter inflation. In conventional methods for calculating the number of created particles, the flat background is selected automatically and causes a negative number problem for created particles during asymptotic-de Sitter inflation. In a covariant approach to curved spacetime, both real and background spacetimes should be selected, curved and consequently the relation for particle creation should be modify. As an interesting finding from this research, flat space does not include minimum number of particles and there are some asymptotic de Sitter spacetimes with fewer number. Therefore, in the generalized formula for particle creation, we choose a non-flat background containing the minimum number of created particles.

Analytical method development for characterizing ingredient-specific particle size distributions of nasal spray suspension products.

Particle size characterization for active pharmaceutical ingredients (APIs) in nasal spray suspension products presents unique challenges because both the API and excipient particles are present in the final dosage form. Currently, an established method is lacking because traditional particle sizing technologies do not distinguish the chemical identity of the particles. In this study, a non-destructive, ingredient-specific particle sizing method was developed for characterization of mometasone furoate (MF) nasal spray suspensions using Morphology Directed Raman Spectroscopy (MDRS). A five-step method development procedure was used in this study: sample preparation, particle imaging and morphology analysis, particle Raman measurements and classification, morphology filter selection, and minimum number of particles determination. Wet dispersion sample preparation method was selected to ensure that the particles were measured in their original suspended state. A training set containing over 10,000 randomly-selected particles, including both the API and excipient particles, was used to gain a comprehensive understanding of particle size, shape, and chemical ID for the nasal spray suspension. Morphology and Raman measurements were performed on each particle in the training set. The measurement results suggested that the aspect ratio and intensity mean filter combination was an appropriate morphology filter setting to selectively target API particles and exclude most of excipient particles. With further optimization of the morphology filter cutoff values and determination of minimal number of particles to be measured, the total measurement time was reduced from 90 hours to 8 hours. The morphologically screening strategy ultimately allowed us to create a time-efficient practical API-specific particle size distribution (PSD) methods for nasal spray suspensions. This study shows that MDRS is a fit for purpose analytical technique for determining ingredient-specific PSDs of the pharmaceutical formulation studied in this work.

Structural features of metal powders obtained by plasma spraying

The development of additive technologies in the Russian Federation is constrained by the lack of indigenous base for the production of metal powders. SPbPU has developed, manufactured and introduced a plasma atomizer. It provides atomization of metal powders and alloys of various chemical compositions. The data on the main technological parameters of atomization of nickel alloy Inconel 718 and titanium alloy Ti-6A1-4V are presented in the paper. It is shown that by optimizing the spraying mode the possibility to obtain alloy powders with a high level of sphericity and a minimum number of particles with surface and internal defects. The chemical compositions of the obtained particles are almost identical to the composition of the initial atomized materials. Samples for the mechanical testing were made from the obtained powders by the SLM method. The results of mechanical properties tests show that the properties of samples made by 3D printing methods from powders sprayed by plasma atomization methods are identical to the properties of materials made and heat-treated by traditional methods.

Simultaneous automated image analysis and Raman spectroscopy of powders at an individual particle level

Solid form diversity of raw materials can be critical for the performance of the final drug product. In this study, Raman spectroscopy, image analysis and combined Raman and image analysis were utilized to characterize the solid form composition of a particulate raw material. Raman spectroscopy provides chemical information and is complementary to the physical information provided by image analysis. To demonstrate this approach, binary mixtures of two solid forms of carbamazepine with a distinct shape, an anhydrate (prism shaped) and a dihydrate (needle shaped), were characterized at an individual particle level. Partial least squares discriminant analysis classification models were developed and tested with known, gravimetrically mixed test samples, followed by analysis of unknown, commercially supplied carbamazepine raw material samples. Classification of several thousands of particles was performed, and it was observed that with the known binary mixtures, the minimum number of particles needed for the combined Raman spectroscopy – image analysis classification model was approximately 100 particles per solid form. The carbamazepine anhydrate and dihydrate particles were detected and classified with a classification error of 1 % using the combined model. Further, this approach allowed the identification of raw material solid form impurity in unknown raw material samples. Simultaneous automated image analysis and Raman spectroscopy of powders at an individual particle level has its potential in accurate detection of low amounts of unwanted solid forms in particulate raw material samples.

Eulerian–Lagrangian direct numerical simulation of preferential accumulation of inertial particles in a compressible turbulent boundary layer

Abstract

TUM-ParticleTyper: A detection and quantification tool for automated analysis of (Microplastic) particles and fibers.

TUM-ParticleTyper is a novel program for the automated detection, quantification and morphological characterization of fragments, including particles and fibers, in images from optical, fluorescence and electron microscopy (SEM). It can be used to automatically select targets for subsequent chemical analysis, e.g., Raman microscopy, or any other single particle identification method. The program was specifically developed and validated for the analysis of microplastic particles on gold coated polycarbonate filters. Our method development was supported by the design of a filter holder that minimizes filter roughness and facilitates enhanced focusing for better images and Raman measurements. The TUM-ParticleTyper software is tunable to the user’s specific sample demands and can extract the morphological characteristics of detected objects (coordinates, Feret’s diameter min / max, area and shape). Results are saved in csv-format and contours of detected objects are displayed as an overlay on the original image. Additionally, the program can stitch a set of images to create a full image out of several smaller ones. An additional useful feature is the inclusion of a statistical process to calculate the minimum number of particles that must be chemically identified to be representative of all particles localized on the substrate. The program performance was evaluated on genuine microplastic samples. The TUM-ParticleTyper software localizes particles using an adaptive threshold with results comparable to the “gold standard” method (manual localization by an expert) and surpasses the commonly used Otsu thresholding by doubling the rate of true positive localizations. This enables the analysis of a statistically significant number of particles on the filter selected by random sampling, measured via single point approach. This extreme reduction in measurement points was validated by comparison to chemical imaging, applying both procedures to the same area at comparable processing times. The single point approach was both faster and more accurate proving the applicability of the presented program.

MapperBot/iSCAN: open-source integrated robotic platform and algorithm for 2D mapping

Simultaneous localization and mapping (SLAM) of an unknown environment is a primary requirement for any autonomous robot. In this paper we present a cost-effective, robust, and integrated software/hardware SLAM solution for 2D mapping of large indoor environments. The presented SLAM solution consists of a robotic platform, referred as MapperBot, and an effective implementation of SLAM algorithm, referred as iSCAN. MapperBot is built using commercial of-the-shelf components with an open-source software/hardware design presented in this paper. The SLAM algorithm iSCAN is based on a highly robust laser-scan matching technique, incorporating Rao-Blackwellized particle filter as a mean to increase its accuracy. As a result, a minimal number of particles is required, and the computer processing time is dramatically reduced. MapperBot/iSCAN solution is fully integrated with the Matlab environment, thus providing researchers, students and developers a quick entry into the realm of the SLAM.

Electron Microscopic Detection of Single Membrane Proteins by a Specific Chemical Labeling.

SummaryElectron microscopy (EM) is a technology that enables visualization of single proteins at a nanometer resolution. However, current protein analysis by EM mainly relies on immunolabeling with gold-particle-conjugated antibodies, which is compromised by large size of antibody, precluding precise detection of protein location in biological samples. Here, we develop a specific chemical labeling method for EM detection of proteins at single-molecular level. Rational design of α-helical peptide tag and probe structure provided a complementary reaction pair that enabled specific cysteine conjugation of the tag. The developed chemical labeling with gold-nanoparticle-conjugated probe showed significantly higher labeling efficiency and detectability of high-density clusters of tag-fused G protein-coupled receptors in freeze-fracture replicas compared with immunogold labeling. Furthermore, in ultrathin sections, the spatial resolution of the chemical labeling was significantly higher than that of antibody-mediated labeling. These results demonstrate substantial advantages of the chemical labeling approach for single protein visualization by EM.

Filling Adeno-Associated Virus Capsids: Estimating Success by Cryo-Electron Microscopy.

Adeno-associated viruses (AAVs) have been employed successfully as gene therapy vectors in treating various genetic diseases for almost two decades. However, transgene packaging is usually imperfect, and developing a rapid and accurate method for measuring the proportion of DNA encapsidation is an important step for improving the downstream process of large scale vector production. In this study, we used two-dimensional class averages and three-dimensional classes, intermediate outputs in the single particle cryo-electron microscopy (cryo-EM) image reconstruction pipeline, to determine the proportion of DNA-packaged and empty capsid populations. Two different preparations of AAV3 were analyzed to estimate the minimum number of particles required to be sampled by cryo-EM in order for robust calculation of the proportion of the full versus empty capsids in any given sample. Cost analysis applied to the minimum amount of data required for a valid ratio suggests that cryo-EM is an effective approach to analyze vector preparations.