Types Of Particles Research Articles

Discontinuous Shear Thickening of Suspensions of Magnetic Particles in Relation to the Polymer Coating on Their Surfaces

The phenomenon of discontinuous shear thickening (DST) is observed in suspensions of solid particles with a very high-volume fraction. It is characterized by an abrupt decrease in the shear rate for critical stress during a ramp of stress. This behavior can be reproduced in numerical simulations by introducing a local friction between two particles above a given local force. We present experimental results showing this DST behavior obtained with suspensions of magnetic (iron) and nonmagnetic (calcium carbonate) particles and different amounts of a superplasticizer molecule used in the cement industry. For both types of particles, the same behavior was observed with first an increase in critical stress with the amount of plasticizer followed by a decrease at higher concentrations but with a larger viscosity before critical stress was reached. At a low concentration of plasticizer, the low critical stress is interpreted by the local sliding of plasticizer molecules on the surface of particles. At higher concentrations, when total coverage is achieved, the critical stress is higher since it has to remove the molecules out of the surface. At still higher concentrations, the increase in viscosity is explained by the formation of multilayers of molecules on the surface of the particles. This interpretation is supported by the measurement of the adsorption isotherm of the plasticizer on the surface of the particles.

Nanoplastic Size and Surface Chemistry Dictate Decoration by Human Saliva Proteins.

Environmental pollution with plastic polymers has become a global problem, leaving no continent and habitat unaffected. Plastic waste is broken down into smaller parts by environmental factors, which generate micro- and nanoplastic particles (MNPPs), ultimately ending up in the human food chain. Before entering the human body, MNPPs make their first contact with saliva in the human mouth. However, it is unknown what proteins attach to plastic particles and whether such protein corona formation is affected by the particle's biophysical properties. To this end, we employed polystyrene MNPPs of two different sizes and three different charges and incubated them individually with saliva donated by healthy human volunteers. Particle zeta potential and size analyses were performed using dynamic light scattering complemented by nanoliquid chromatography high-resolution mass spectrometry (nLC/HRMS) to qualitatively and quantitatively reveal the protein soft and hard corona for each particle type. Notably, protein profiles and relative quantities were dictated by plastic particle size and charge, which in turn affected their hydrodynamic size, polydispersity, and zeta potential. Strikingly, we provide evidence of the latter to be dynamic processes depending on exposure times. Smaller particles seemed to be more reactive with the surrounding proteins, and cultures of the particles with five different cell lines (HeLa, HEK293, A549, HepG2, and HaCaT) indicated protein corona effects on cellular metabolic activity and genotoxicity. In summary, our data suggest nanoplastic size and surface chemistry dictate the decoration by human saliva proteins, with important implications for MNPP uptake in humans.

Ab Initio Design of Ni‐Rich Cathode Material with Assistance of Machine Learning for High Energy Lithium‐Ion Batteries

With the widespread use of lithium‐ion batteries in electric vehicles, energy storage, and mobile terminals, there is an urgent need to develop cathode materials with specific properties. However, existing material control synthesis routes based on repetitive experiments are often costly and inefficient, which is unsuitable for the broader application of novel materials. The development of machine learning and its combination with materials design offers a potential pathway for optimizing materials. Here, we present a design synthesis paradigm for developing high energy Ni‐rich cathodes with thermal/kinetic simulation and propose a coupled image‐morphology machine learning model. The paradigm can accurately predict the reaction conditions required for synthesizing cathode precursors with specific morphologies, helping to shorten the experimental duration and costs. After the model‐guided design synthesis, cathode materials with different morphological characteristics can be obtained, and the best shows a high discharge capacity of 206 mAh g−1 at 0.1C and 83% capacity retention after 200 cycles. This work provides guidance for designing cathode materials for lithium‐ion batteries, which may point the way to a fast and cost‐effective direction for controlling the morphology of all types of particles.

Evaluation and correction of laser scattering-based particle size measurements at high obscurations using the Monte Carlo method.

High turbidity and its associated multiple scattering phenomena can often lead to an underestimation of the particle size for the laser scattering method. To investigate the light scattering characteristics and evaluate the effect of high-obscuration particle systems, a Monte Carlo model has been developed based on Mie's theory. A compact setup was utilized to perform a series of experiments on three certified reference materials (CRMs) at different concentrations. Both the scattered light energy distribution and the obscuration were measured simultaneously. The inversion results of the particle size indicate a continuous increase in deviation from the nominal value as the obscuration rises. According to the conventional single scattering model, the inversion errors fall within 5% for obscuration levels ranging from 0.15 to 0.2. However, for a higher obscuration, the error can reach approximately 15%. Thus, a correction method has been proposed by introducing an improved model matrix that includes the multiple scattering contribution for the data inversion, which exhibits a significant enhancement in the accuracy of particle size measurements under high obscuration conditions. For all three types of particles being studied, the error was successfully reduced to within 5.0%.

Pulse shape discrimination technique for diffuse supernova neutrino background search with JUNO

Pulse shape discrimination (PSD) is widely used in particle and nuclear physics. Specifically in liquid scintillator detectors, PSD facilitates the classification of different particle types based on their energy deposition patterns. This technique is particularly valuable for studies of the diffuse supernova neutrino background (DSNB), nucleon decay, and dark matter searches. This paper presents a detailed investigation of the PSD technique, applied in the DSNB search performed with the Jiangmen Underground Neutrino Observatory (JUNO). Instead of using conventional cut-and-count methods, we employ methods based on boosted decision trees and neural networks and compare their capability to distinguish the DSNB signals from the atmospheric neutrino neutral-current background events. The two methods demonstrate comparable performance, resulting in a 50–80% improvement in signal efficiency compared to a previous study performed for JUNO (An et al. [JUNO] in J Phys G 43(3):030401, 2016). Moreover, we study the dependence of the PSD performance on the visible energy and final state composition of the events and find a significant dependence on the presence/absence of 11\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{11}$$\\end{document}C. Finally, we evaluate the impact of the detector effects (photon propagation, PMT dark noise, and waveform reconstruction) on the PSD performance.

Diagnostic Accuracy of Raman Spectroscopy Integrated With Polarized Light Microscopy for Calcium Pyrophosphate-Associated Arthritis.

We studied the performance of integrated Raman polarized light microscopy (iRPolM) for the identification of calcium pyrophosphate (CPP)-associated arthritis (CPPD). This is a diagnostic accuracy study including 400 consecutive synovial fluid samples from a single hospital in the Netherlands. Accuracy measures were calculated against polarized light microscopy (PLM) and the 2023 American College of Rheumatology (ACR)/EULAR criteria set for CPPD. The interrater reliability between iRPolM and the 2023 ACR/EULAR criteria set for CPPD was strong (κ = 0.88). The diagnostic performance of iRPolM compared to the 2023 ACR/EULAR criteria set was sensitivity 86.0% (95% confidence interval [CI] 73.3-94.2), specificity 99.1% (95% CI 97.5-99.8), positive likelihood ratio 100.33 (95% CI 32.3-311.3), negative likelihood ratio 0.14 (95% CI 0.07-0.28), positive predictive value 93.5% (95% CI 82.2-97.8), negative predictive value 98.0% (95% CI 82.2-97.8), and accuracy 97.5% (95% CI 95.5-98.8). We allowed rheumatologists to rate the certainty of their microscopic identification of CPP and found a large correspondence between iRPolM and a certain identification (κ = 0.87), whereas only 10% of the uncertain CPP identifications could be confirmed with iRPolM. We identified several novel particle types in synovial fluid analysis, including calcium carbonate crystals, deposited carotenoids, microplastics, and three types of Maltese cross birefringent objects. iRPolM can easily identify CPP crystals with a strong diagnostic performance. PLM alone is not specific enough to reliably resolve complicated cases, and the implementation of Raman spectroscopy in rheumatology practice can be of benefit to patients with suspected CPPD.

Spatially revealed roles for lncRNAs in Drosophila spermatogenesis, Y chromosome function and evolution

Unlike coding genes, the number of lncRNA genes in organism genomes is relatively proportional to organism complexity. From plants to humans, the tissues with highest numbers and levels of lncRNA gene expression are the male reproductive organs. To learn why, we initiated a genome-wide analysis of Drosophila lncRNA spatial expression patterns in these tissues. The numbers of genes and levels of expression observed greatly exceed those previously reported, due largely to a preponderance of non-polyadenylated transcripts. In stark contrast to coding genes, the highest numbers of lncRNAs expressed are in post-meiotic spermatids. Correlations between expression levels, localization and previously performed genetic analyses indicate high levels of function and requirement. More focused analyses indicate that lncRNAs play major roles in evolution by controlling transposable element activities, Y chromosome gene expression and sperm construction. A new type of lncRNA-based particle found in seminal fluid may also contribute to reproductive outcomes.

3D printed self-sensing cementitious composites using graphite and carbon microfibers

Cementitious materials possessing self-sensing properties conferred through the incorporation of conductive fillers have attracted significant interest in the research community due to their potential application to structural health monitoring of civil infrastructure. A key advantage of cementitious sensors is that they can be organically embedded within cementitious structures to provide extremely robust monitoring capabilities. Of interest to this paper, the integration of cementitious sensors could be critical in empowering the 3D printing of cementitious composites by enabling real-time monitoring of additive manufacturing. Such integration could be achieved by 3D printing self-sensing cementitious nodes during the fabrication process. 3D printed self-sensing cementitious sensors could also be topologically engineered to achieve new opportunities in sensing of existing and new concrete and other cementitious structures. This paper presents one of the earliest studies, if not the first, on the 3D printing of self-sensing functionalized composites. The self-sensing cementitious composite sensors are fabricated using a cementitious matrix doped with graphite (G) and carbon microfibers. Various mixtures are studied, in particular G-only, G with milled carbon microfibers (MCMF), G with chopped carbon microfibers (CCMF), and G with MCMF and CCMF. A study of the percolation curves reveals that it is possible to attain electrical percolation using these conductive particles to create strain-sensitive sensors by leveraging the boosted piezoresistive effect. A study of strain sensing performance conducted on various specimens fabricated using different mixture types reveals the optimal concentration of dopants under different types of conductive particles. It is found that the optimal mixture that yields optimal performance also improves the Young’s Modulus of the specimens by 42.2% compared to cement-only specimens. Overall, this optimal mixture composed of 10% weight-to-cement G (10 wt% G), 0.250 wt% MCMF, and 0.125 wt% CCMF yielded a static gauge factor of 622, a resolution of 167 με , an accuracy of 19.24 με , and a Young’s Modulus of 798 MPa.

Exploring secondary aerosol formation associated with elemental carbon in the lower free troposphere

The variability of element carbon (EC) mixed with secondary species significantly complicates the assessment of its environmental impact, reflecting the complexity and diversity of EC-containing particles' composition and morphology during their ascent and regional transport. While the catalytic role of EC in secondary aerosol formation is recognized, the effects of heterogeneous chemistry on secondary species formation within diverse EC particle types are not thoroughly understood, particularly in the troposphere. Alpine sites offer a prime environment to explore EC properties post-transport from the ground to the free troposphere. Consequently, we conducted a comprehensive study on the genesis of secondary aerosols in EC-containing particles at Mt. Hua (altitude: 2069 m) from 1 May to 10 July, using a single particle aerosol mass spectrometer (SPAMS). Our analysis identified six major EC particle types, with EC-K, EC-SN, and EC-NaK particles accounting for 27.6 %, 27.0 %, and 19.6 % of the EC particle population, respectively. The concentration-weighted trajectory (CWT) indicated that the lower free troposphere over Mt. Hua is significantly affected by anthropogenic emissions at ground-level, predominantly from northwestern and eastern China. Atmospheric interactions are crucial in generating high sulfate levels in EC-SN and EC-OC particles (> 70 %) and notable nitrate levels in EC-K, EC-BB, and EC-Fe particles (> 80 %). The observed high chloride content in EC-OC particles (56 ± 32 %) might enhance chlorine's reactivity with organic compounds via heterogeneous reactions within the troposphere. Distinct diurnal cycles for sulfate and nitrate are mainly driven by varying transport dynamics and formation processes, showing minimal dependency on EC particle types. Enhanced nocturnal oxalate conversion in EC-Fe particles is likely due to the aqueous oxidation of precursors, with Fe-catalyzed Fenton reactions enhancing OH radical production. This investigation provides critical insights into EC's role in secondary aerosol development during its transport in the lower free troposphere.

Regression Modeling of Daily PM2.5 Concentrations with a Multilayer Perceptron

Various types of energetic fuel combustion processes emit dangerous pollutants into the air, including aerosol particles, marked as PM10. Routine air quality monitoring includes determining the PM10 concentration as one of the basic measurements. At some air monitoring stations, the PM10 measurement is supplemented by the simultaneous determination of the concentration of PM2.5 as a finer fraction of suspended particles. Since the PM2.5 fraction has a significant share in the PM10 fraction, the concentrations of both types of particles should be strongly correlated, and the concentrations of one of these fractions can be used to model the concentrations of the other fraction. The aim of the study was to assess the error of predicting PM2.5 concentration using PM10 concentration as the main predictor. The analyzed daily concentrations were measured at 11 different monitoring stations in Poland and covered the period 2010–2021. MLP (multilayer perceptron) artificial neural networks were used to approximate the daily PM2.5 concentrations. PM10 concentrations and time variables were tested as predictors in neural networks. Several different prediction errors were taken as measures of modeling quality. Depending on the monitoring station, in models with one PM10 predictor, the RMSE error values were in the range of 2.31–6.86 μg/m3. After taking into account the second predictor D (date), the corresponding RMSE errors were lower and were in the range of 2.06–5.54 μg/m3. Our research aimed to find models that were as simple and universal as possible. In our models, the main predictor is the PM10 concentration; therefore, the only condition to be met is monitoring the measurement of PM10 concentrations. We showed that models trained at other air monitoring stations, so-called foreign models, can be successfully used to approximate PM2.5 concentrations at another station.

Modeling the optical properties of forsterite particles using different irregularly shaped particles

Light scattering by four different types of irregularly shaped particles with refractive index m = 1.6 + 0.0005i is investigated over a wide range of size parameter x = 1 – 48 calculated by means of the discrete dipole approximation. The packing density of the constituent materials varies by a factor of ∼4.6 between the densest morphology of the pocked spheres and the fluffiest morphology of the loose clusters. The modeling reveals a great deal of similarity in the light-scattering response in all four types of particles, despite their dramatically different morphology. The resemblance is further enhanced when the light-scattering response is averaged over all available sizes of particles. However, the influence of particles whose size parameter exceeds 32 appears to be hardly noticeable. All four types of particles are capable of reasonably good fits to the experimental optical measurements of the forsterite particles with a simple power-law size distribution. It is significant that the power index providing these best fits appears to be squeezed to a very narrow range of values n = 3.075 ± 075. This finding potentially has a great impact on remote sensing as it raises confidence in the retrievals obtained with any model of irregularly shaped particles whose shape attains a high level of disorder. Interestingly, when the size distribution of laboratory-measured forsterite particles is considered, only the agglomerated debris particles immediately yield a reasonably good fit to the optical measurements. This suggests the morphology may more closely resemble that of the forsterite particles.

The charged particle detection system of the SRD module onboard the China space station

The radiation field environment outside the Earth varies greatly with space location and the cycle of solar activity, and the radiation environment near the space station’s orbit is much more complex than that on the Earth’s surface. Various high-energy cosmic ray particles and secondary particles produced by them and the bulkhead of the space station greatly impact the health of astronauts and the working conditions of instruments. Each aircraft is always equipped with some dedicated radiation monitoring instruments to assess the exposure risk for astronauts, and to analyze the causes of instrument failures. The Space Radiation Detector Module (SRDM) is working in the China Space Station (CSS) to measure the radiation environment inside, including two parts: the Charged Particle Detection System (CPDS) and the Neutron Detection System (NDS). The CPDS, which is the main content of this paper, contains a detector unit, that consists of three silicon detectors a BGO calorimeter, and the corresponding readout electronics unit of the detector unit. Ground test results show that the detection system can detect various charged particles from hydrogen to nitrogen ions with an energy resolution of less than 15%. The actual measurement results for a period in orbit show that the main types of charged particles in the cabin are protons and α particles, with measured energies ranging from 0.8 to 265.2 MeV for protons and from 1.0 to 61.6 MeV/A for α particles(where A is the mass number), and linear energy density (LET) range mainly from 1.0 to 612.4 keV/μm. The radiation environment data measured in CSS can provide an important reference value for the exposure risk, life science experiments, and the status of instruments on board.

Switching particle systems for foraging ants showing phase transitions in path selections

Switching interacting particle systems studied in probability theory are the stochastic processes of hopping particles on a lattice made up of slow and fast particles, where the switching between these types of particles occurs randomly at a given transition rate. This paper explores how such stochastic processes involving multiple particles can model group behaviors of ants. Recent experimental research by the last author’s group has investigated how ants switch between two types of primarily relied cues to select foraging paths based on the current situation. Here, we propose a discrete-time interacting random walk model on a square lattice, incorporating two types of hopping rules. Numerical simulation results demonstrate global changes in selected homing paths, transitioning from trailing paths of the ‘pheromone road’ to nearly optimal paths depending on the switching parameters. By introducing two types of order parameters characterizing the dependence of homing duration distributions on switching parameters, we discuss these global changes as phase transitions in ant path selections. We also study critical phenomena associated with continuous phase transitions.

DEM investigation on sandy soil behaviors under the influence of particle shape

The mechanical properties of sandy soil, a prevalent material in engineering construction, are affected by several factors, including the particle shape. This study utilized a rigid block unit for particle simulation and established specimens reflecting the real particle appearance based on μCT scanning geometry. Four simple particle types with varying roundness were designed according to the roundness definition, and corresponding numerical test models were developed. These models were employed to examine the effect of particle roundness on the macroscopic mechanical properties of sandy soil using triaxial shearing simulation tests, disregarding other shape parameters. The results demonstrate that particle roundness significantly affects the mechanical properties of sandy soil. Within a roundness range of [0,1], samples with varying particle roundness displayed different deviatoric stress–strain curves. As the roundness increases, the peak deviatoric stress decreases, and both the cohesive strength and friction angle of the samples reduce.

Polymerization mechanism of the Candida albicans virulence factor candidalysin

Candida albicans is a commensal fungus that can cause epithelial infections and life-threatening invasive candidiasis. The fungus secretes candidalysin (CL), a peptide that causes cell damage and immune activation by permeation of epithelial membranes. The mechanism of CL action involves strong peptide assembly into polymers in solution. The free ends of linear CL polymers can join, forming loops that become pores upon binding to membranes. CL polymers constitute a therapeutic target for candidiasis, but little is known about CL self-assembly in solution. Here, we examine the assembly mechanism of CL in the absence of membranes using complementary biophysical tools, including a new fluorescence polymerization assay, mass photometry, and atomic force microscopy. We observed that CL assembly is slow, as tracked with the fluorescent marker C-laurdan. Single-molecule methods showed that CL polymerization involves a convolution of four processes. Self-assembly begins with the formation of a basic subunit, thought to be a CL octamer that is the polymer seed. Polymerization proceeds via addition of octamers, and as polymers grow they can curve and form loops. Alternatively, secondary polymerization can occur and cause branching. Interplay between the different rates determines the distribution of CL particle types, indicating a kinetic control mechanism. This work elucidates key physical attributes underlying CL self-assembly which may eventually evoke pharmaceutical development.

Three-dimensional reconstruction of wear particle surfaces using multi-focus image sequence

Wear particles hold morphological traits that are linked to wear rate and mechanisms. Wear particle analysis is vital in the context of wear monitoring. Current methods can be used in three-dimensional (3D) reconstruction for particle type recognition and wear mechanism identification. A 3D reconstruction method based on microscopic focusing and image processing is here proposed. A fixed-focus quasi-planar strategy is built using the defocusing principle. The stationary wavelet transform is employed to assess the degree of focus for the purpose of depth mapping, facilitating the construction of 3D particle models. The model is then optimized for surface morphology to address noise and unevenness. The reconstruction results of the proposed approach were compared with that of laser scanning confocal microscope with average errors of arithmetic mean height (Sa), root-mean-square height (Sq), and kurtosis (Sku) at 2.81%, 2.96%, and 10.06%, respectively. The algorithm is non-contact and adaptable, offering value across various fields.

Investigation of Mechanical Properties of Polymer-Infiltrated Tetrapodal Zinc Oxide in Different Variants.

The aim of this study was to evaluate the influence of weight ratio, the shape of the precursor particles, and the application of a phosphate-monomer-containing primer on the mechanical properties of polymer infiltrated ceramic networks (PICNs) using zinc oxide. Two different types of zinc oxide particles were used as precursors to produce zinc oxide networks by sintering, each with two different densities resulting in two different weight ratios of the PICNs. For each of these different networks, two subgroups were built: one involving the application of a phosphate-monomer-containing primer prior to the infiltration of Bis-GMA/TEGDMA and one without. Elastic modulus and flexural strength were determined by using the three-point bending test. Vertical substance loss determined by the chewing simulation was evaluated with a laser scanning microscope. There was a statistically significant influence of the type of precursor particles on the flexural strength and in some cases on the elastic modulus. The application of a primer lead to a significant increase in the flexural strength and in most cases also in the elastic modulus. A higher weight ratio of zinc oxide led to a significantly higher elastic modulus. Few statistically significant differences were found for the vertical substance loss. By varying the shape of the particles and the weight fraction of zinc oxide, the mechanical properties of the investigated PICN can be controlled. The use of a phosphate-monomer-containing primer strengthens the bond between the infiltrated polymer and the zinc oxide, thus increasing the strength of the composite.

Synthesis and Characterization of Briquette from Carbonized Pinus Patula Saw Dust as Alternative Energy Source

This study aimed at the synthesis and characterization of briquette from Pinus patula saw dust using different binders. The effects of particle size and type of binder on the fuel qualities of the briquette were investigated. The bulk density of biomass causes handling, storage, and transportation problems that limit its large application. Densification of biomass into briquettes can solve these problems. The experimental results indicate that briquette produced from waste paper, fruit waste, cow dung, and starch flour binders has higher calorific value, higher fixed carbon, and lower ash content. Whereas, molasses and wood ash binders decreased the calorific value and fixed carbon and increased the ash content of the briquette. As a result, a maximum calorific value of 6596 cal/g, fixed carbon content of 62.6%, and ash content of 3.66% are obtained using waste paper. The calorific value of 6232 cal/g, fixed carbon of 48.74%, and ash content of 3.33% was obtained using fruit waste while the calorific value of 6194 cal/g, fixed carbon content of 59.94 %, and ash content of 5.35% was observed when cow dung is used. When starch flour is used, a calorific value of 6170 cal/g, fixed carbon content of 54.63%, and ash content of 2.63 was obtained. Therefore, waste paper, fruit waste, cow dung, and starch flour are promising binders that improve the fuel qualities of biomass briquettes.

Automated SEM analysis of particles in Hanford tank waste

Multiple bench-scale filtration campaigns of Hanford tank waste supernatant on a backpulseable dead-end filtration skid have provided greater insight into the solids that cause fouling and reduce filter performance. The solids collected during each campaign were concentrated from the backpulse solutions and examined using automated particle analysis (APA) methods with scanning electron microscopy and X-ray energy dispersive spectroscopy to categorize particle types and their morphological characteristics. We show that with APA, thousands of particles can be analyzed to provide accurate insight into the phases that may be impacting filter performance.

A mixing index for uniformly and gap-graded cohesionless particles influenced by mixing rate and diameter of stirring rods

Soil mechanics, sometimes, deals with the mixing of soil such as in deep mixing of soil and mixing of soil in tunnel boring machines, i.e., earth pressure balance (EPB-TBM) shields. The geometry of mixing apparatus such as size of stirring rods plays an important role to achieve higher degree of mixing. Moreover, the effect of gap-graded particles being mixed on degree of mixture should also be evaluated. It is important to estimate the extent of mixing of the soil to confirm the required performance of such phenomenon.This study aims to propose a simple method to quantify the degree of mixture of solid particles stirred by rotation of rods with varying diameters at different mixing rates. Discrete element method (DEM) is used to simulate the stirring process and evaluate the degree of mixture at each rotation based on the spatial distribution of different types of particles. To reduce complexity in the mixing process of particles, this study focuses on monodispersed (i.e., uniformly graded) and bidispersed (i.e., narrowly gap-graded) spherical particles where inter-particle cohesion is not considered. The DEM results reveal spatial variation of the degree of mixture (dm) in the given volume; however, its mean (dm¯) increases with the rotation number of stirring rods. Besides, dm¯ depends on the size of stirring rods whereas the mixing rate is less sensitive to dm¯ for the non-cohesive sandy particles. The dm¯ of narrowly gap-graded particles cases is slightly on the lower side as compared to that of uniformly graded particles. The results of the DEM simulations are validated through the model test.