Normal Particle Size Distribution Research Articles

Green synthesis of nanocomposite based on magnetic hydroxyapatite using Falcaria vulgaris Bernh leaf extract to remove tartrazine dye from aqueous solution

To mitigate the adverse impacts of water pollution, the application of nanotechnology in dye removal and by-product reduction has the potential to establish a sustainable potable water supply. For these methodologies, the materials must involve exhibit qualities such as simplicity, high efficiency, eco-friendliness, reusability, and affordability. This study presents a novel porous magnetic hydroxyapatite nanocomposite coated with zinc oxide (Fe3O4@HAP@ZnO). Initially, zinc oxide nanoparticles were generated via a green synthesis method, where a zinc nitrate solution was combined with an aqueous extract from the Falcaria Vulgaris Bernh plant, serving as a reducing and stabilizing agent. These were then coated onto the magnetic hydroxyapatite.Verification of successful nanocomposite synthesis was obtained through X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and X-ray spectroscopy (EDS), vibrating-sample magnetometer (VSM), High-resolution transmission electron microscopy (HRTEM) and scanning electron microscopy (SEM) images further substantiated these results. The synthesized nanocomposite exhibited a normal particle size distribution of approximately 89 nm. To achieve maximal decolorization efficiency, pertinent parameters were scrutinized and optimized through a detailed experimental design. Under optimal conditions, the nanocomposite's ability to decolorize tartrazine dye in an aqueous solution was evaluated. Remarkably, the highest observed efficiency approached 98 %, employing 0.07 g of the nanocomposite at 90 degrees Celsius, pH 6.4, over a span of 30 min. Further exploration of the nanocomposite's reusability revealed it could sustain an efficiency exceeding 90 % across 11 successive cycles, boasting an impressive capacity of 1398 (mgg-1).

Numerical simulation of polydisperse particle segregation based on a bubble-based drag model

This work is devoted to the development of a novel polydisperse drag model based on particle type for bubbling fluidized beds. Traditional homogeneous drag models do not take into account the heterogeneous mesoscale structures, while this model is a mesoscale-structure-dependent drag model. The model combined with Energy Minimum Multiscale (EMMS) theory has been successfully applied to the simulation of polydisperse particle segregation. Four particle size distribution (PSD) systems (including a binary system with different sizes, a binary system with different sizes and densities, a normal PSD and a lognormal PSD) were selected to validate the model under various superficial gas velocities. The PSD was decomposed into several types of solids, each governed by its own set of hydrodynamic equations. The solution algorithm was able to find the heterogeneous index for each solid, which was dependent on both slip velocity and solid concentration. Experiments were subsequently conducted under identical conditions to compare the experimental data with the simulated data. The average relative error between the simulation data and the experimental data about the mean particle size distribution was less than 5%. These results indicated that the proposed model outperformed other drag models in accurately predicting particle segregation behavior.

CFD-PBM Simulation for Continuous Hydrothermal Flow Synthesis of Zirconia Nanoparticles in a Confined Impinging Jet Reactor.

Computational fluid dynamics (CFD) and population balance models (PBM) were coupled together for the first time to simulate the synthesis of zirconia nanoparticles in a continuous hydrothermal flow synthesis (CHFS) system with a self-designed confined impinging jet mixing (CJM) reactor. The hydrodynamic and thermodynamic behaviors within the CJM reactor strongly influenced the formation of the ZrO2 nanoparticles. Crucial parameters, such as velocities, temperatures, mixing conditions, and reaction rates, were analyzed under various supercritical conditions. Temperature and velocity measurements as functions of distance were also investigated. Normal particle size distribution (PSD) patterns were observed in all cases. The mean particle sizes in this study were calculated and compared using PBM aggregation analysis.

Evaluation of particle models of corn kernels for discrete element method simulation of shelled corn mass flow

Bulk handling behavior of grains can be studied experimentally, but large-scale investigations of grain flow especially at the commercial scale are expensive, time consuming, and are therefore limited in the treatment factors that can be evaluated in any one study. Recent research has demonstrated the potential of discrete element method (DEM) in simulating grain flow in handling operations. However, application of DEM for simulating grain flow, requires development of appropriate particle models for each grain type. In this study, particle models comprised of one to four overlapping spheres were developed for shelled corn and tested. With these models, measurement of bulk properties, namely bulk density and angle of repose, both involving bulk flow were simulated using EDEM™ software with published data of material and interaction properties of shelled corn as inputs associated with each particle shape. Predicted time for the complete outflow from the bulk density test hopper (hopper emptying time), designated as simulation time in the study, was also recorded as another discriminant for model selection. Variable inputs into the simulation modeling were material properties particle shape, particle size distribution, Poisson's ratio, shear modulus, and density and interaction properties particle coefficients of restitution, static friction, and rolling friction. Computation time is critical in DEM modeling so single sphere particle models were emphasized over multi-sphere particles in the research even though multi-sphere particles represent the corn kernel shape more precisely because their simplicity can provide markedly reduced computation times to complete simulations. Predicted results for hopper emptying time, bulk density, and angle of repose were compared to experimental results or published data to select the most appropriate particle models for simulating bulk behavior of corn kernels in free-flowing grain applications using DEM. From the study, the most appropriate particle model (particle shape/physical properties combination) for corn kernels involved a single-sphere shape particle shape with a particle coefficient of restitution of 0.30, particle coefficients of static friction of 0.30 for corn-corn contact and 0.20 for corn-steel contact, particle coefficient of rolling friction of 0.05, normal particle size distribution with a standard deviation factor of 0.4, and particle shear modulus of 20 MPa.

Preparation and Characterization of Cellulose and Microcrystalline Cellulose from Sugarcane Bagasse and Assessment of the Microcrystalline Cellulose as a Directly Compressible Excipient

Cellulose, the most abundant biomass material in nature finds wide applications in the pharmaceutical industry. Sugarcane bagasse (SCB) is one of the main agricultural lignocellulose byproducts. The objective of this study was to prepare and characterize native and microcrystalline cellulose (MCC) from SCB and evaluate the MCC as a directly compressible pharmaceutical excipient. Cellulose was extracted from SCB by chlorine-free methods with or without dewaxing. MCC was prepared from the cellulose by hydrolysis using hydrochloric acid, and subsequently oven-dried (OD) or spray-dried (SD). The as-obtained cellulose and MCC were characterized in terms of yield, degree of polymerization (DP), chemical functionality, crystallinity, morphology and thermal stability. The chemical composition, particle size, densities and direct compressibility of MCCs were also determined. Cellulose yields on a dry weight basis were found to be 42.8 ± 1.10% and 43.5 ± 0.5% from non-dewaxed and dewaxed SCB, respectively. Dewaxed SCB cellulose (DSCBC) provided higher yield of MCC (DSCB-MCCOD, 83 ± 0.74%) than non-dewaxed SCB cellulose (SCBC) (SCB-MCCOD; 78 ± 1.07%). The DP of SCBC and DSCBC were 580.56 and 592.75, respectively, while the DP of MCC ranged from 230.10 - 251.40. The FTIR spectra of both cellulose and MCC were similar with that of Avicel PH-101. The degree of crystallinity of the dewaxed cellulose (77.34%) and MCCOD (79.56%) and MCC-SD (81.87%) were higher than non-dewaxed cellulose (74.50%) and MCC-OD (78.11%) and MCC-SD (79.62%). Scanning electron micrographs (SEM) showed a fibrous structure for DSCBC and rod-shaped for DSCB-MCC. Thermogravimetric analysis (TGA) revealed dewaxed products exhibited better thermal stability than non-dewaxed products. All MCC samples exhibited monomodal normal particle size distributions. The Hausner ratio and Carr’s index of DSCB-MCCOD and Avicel PH-101 were not significantly different (p < 0.05) indicating similar flow property and compressibility of the materials, respectively. Also, plain tablets prepared from SCB-MCC showed reasonably high crushing strengths (MCC-SD > MCC-OD), although tablets of Avicel PH-101 showed the highest crushing strengths. Thus, SCB could be an alternative source of cellulose and MCC for pharmaceutical applications.

Isolation and Physico‐chemical Characterization of Triticum Decocum Starch

AbstractEthiopian oat (Triticum decocum) starch has been isolated and examined for its chemical composition and physicochemical properties. The proximate composition of the starch on a dry weight basis is found to be 0.23% ash, 0.26% protein, 1.54% fat, and 56.31% starch. The total amylose content is 28.4% with 20.11% apparent amylose and 29.19% lipid complexed amylose. SEM showed oval, spherical shaped granules. The particle size distributions of the starch showed monomodal normal particle size distribution with the average volumetric particle diameters being about 23.03 µm, and specific surface area 0.7186 m2 g−1. XRD showed a pattern that is typical of A‐type with a distinctive maximum peak at around 17° 2θ. At all relative humidities, its water sorption properties resemble that of maize starch. It has unique swelling power and solubility in water at different temperatures. Viscosity curves of 10% of starch paste showed a peak viscosity of 1926cP. Its cold paste viscosity and hot paste viscosity are 4236 and 1720 cP, respectively. The breakdown and set back viscosities of Ethiopian oat starch are 206 and 2647cP, respectively. The FTIR showed the characteristic features of prominent starch peaks. Physicochemical characteristics of the oat starch have shown that it has similar properties with maize starch which could make it fit for use in food and pharmaceutical industries, especially in the area where oat is produced as endogenous crop like in Ethiopia.

Al–Al2O3 powder composites obtained by hydrothermal oxidation method: Powders and sintered samples characterization

The aluminum powder composites based on Al–Al2O3 were obtained by the advanced in–situ partial hydrothermal oxidation method. The opportunity to obtain the controlled amount of alumina on the particle’s surfaces was studied. The water vapor temperature fixed at 90 °C and 120 °C gave the possibility to obtain two composites with the amount of Al2O3 at 9.90 wt % (OAP–1) and 18.75 wt % (OAP–2). Their median diameters were 41–42 μm with normal particle size distribution. The comparison with atomized 7068 alloy (AP–7068) powder showed their better particle sphericity. The OAP–1 sintered sample has the highest hardness of 471 MPa that provided by the highest density of 97.1% and the finest grain structure.

Fast dynamic nonparametric distribution tracking in electron microscopic data

In situ transmission electron microscope (TEM) adds a promising instrument to the exploration of the nanoscale world, allowing motion pictures to be taken while nano objects are initiating, crystalizing and morphing into different sizes and shapes. To enable in-process control of nanocrystal production, this technology innovation hinges upon a solution addressing a statistical problem, which is the capability of online tracking a dynamic, time-varying probability distribution reflecting the nanocrystal growth. Because no known parametric density functions can adequately describe the evolving distribution, a nonparametric approach is inevitable. Towards this objective, we propose to incorporate the dynamic evolution of the normalized particle size distribution into a state space model, in which the density function is represented by a linear combination of B-splines and the spline coefficients are treated as states. The closed-form algorithm runs online updates faster than the frame rate of the in situ TEM video, making it suitable for in-process control purpose. Imposing the constraints of curve smoothness and temporal continuity improves the accuracy and robustness while tracking the probability distribution. We test our method on three published TEM videos. For all of them, the proposed method is able to outperform several alternative approaches.

Evolution of DARDAR-CLOUD ice cloud retrievals: new parameters and impacts on the retrieved microphysical properties

Abstract. In this paper we present the latest refinements brought to the DARDAR-CLOUD product, which contains ice cloud microphysical properties retrieved from the cloud radar and lidar measurements from the A-Train mission. Based on a large dataset of in situ ice cloud measurements, the parameterizations used in the microphysical model of the algorithm – i.e. the normalized particle size distribution, the mass–size relationship, and the parameterization of the a priori value of the normalized number concentration as a function of temperature – were assessed and refined to better fit the measurements, keeping the same formalism as proposed in DARDAR basis papers. Additionally, in regions where lidar measurements are available, the lidar ratio retrieved for ice clouds is shown to be well constrained by the lidar–radar synergy. Using this information, the parameterization of the lidar ratio was also refined, and the new retrieval equals on average 35±10 sr in the temperature range between −60 and −20 ∘C. The impact of those changes on the retrieved ice cloud properties is presented in terms of ice water content (IWC) and effective radius. Overall, IWC values from the new DARDAR-CLOUD product are on average 16 % smaller than the previous version, leading to a 24 % reduction in the ice water path. In parallel, the retrieved effective radii increase by 5 % to 40 %, depending on temperature and the availability of the instruments, with an average difference of +15 %. Modifications of the microphysical model strongly affect the ice water content retrievals with differences that were found to range from −50 % to +40 %, depending on temperature and the availability of the instruments. The largest differences are found for the warmest temperatures (between −20 and 0 ∘C) in regions where the cloud microphysical processes are more complex and where the retrieval is almost exclusively based on radar-only measurements. The new lidar ratio values lead to a reduction of IWC at cold temperatures, the difference between the two versions increasing from around 0 % at −30 ∘C to 70 % below −80 ∘C, whereas effective radii are not impacted.

Polyacrylic acid-coated iron oxide magnetic nanoparticles: The polymer molecular weight influence

Magnetic nanoparticles (MNPs), in particular, magnetic iron oxide-based nanoparticles were found to be useful as catalysts and as devices for data storage, environmental remediation and several biomedical applications, due to their excellent properties, such as biocompatibility and high magnetic moment. Polyacrylic acid (PAA) is a weak polyelectrolyte that can be used to stabilize the MNPs. To the best of our knowledge, the influence of PAA molecular weight and PAA concentration over the magnetic and structural properties of iron oxide nanoparticles has not been previously reported. The aim of this paper is to describe the differences evidenced in the properties of different magnetic materials by using PAA for iron oxides stabilization by one-pot coprecipitation synthesis. Iron oxide-based magnetic nanoparticles stabilized by polyacrylic acid (PAA) polymers were efficiently prepared and exhaustively characterized. The influence on the employment of two different low PAA molecular weights, Mw 1800 g/mol and 5000 g/mol, in three different iron salts: PAA ratios was analyzed. In summary, the main results showed that: for a certain PAA reactor feed higher oligomer quantities are present in MNPs as higher is the involved molecular weight of the polymeric chain; when molecular weight raises the contribution of loops and tails also does it, allowing having higher polymer contents. For both PAA’s Mw employed as the adsorbed PAA increases particles hydrodynamic diameters decreases, and their distribution becomes narrower; the PAA adsorbs onto iron oxides by chemisorption (the most probable interaction is the bidentate bridging). For the studied cases z potential values depend much more on the PAA’s quantity adsorbed onto the iron oxides than on the PAA’s Mw. MNPs are superparamagnetic and choosing the right shape of particle distribution is not central for getting estimates of the magnetization saturation, the average particle diameter and its standard deviation, while better fits are found with Normal and Log-Normal particle size distributions.

INVESTIGATION OF THE FORMATION PROCESS OF HAZARDOUS AND HARMFUL PRODUCTION FACTORS WHEN CUTTING A STONE FOR CONSTRUCTION WORKS

Stone cutting for construction work is carried out by disk diamond wheels the rotation speed of which, and, consequently, the cutting speed is 35-50 m/s. In view of the high intensity of the cutting process and intensive microchip formation, the process of stone cutting is accompanied by considerable dust formation, which can be both harmful and dangerous in the work. The greatest danger is represented by dust particles, which dimensions are 5 μm or less. These particles have the greatest pathogenic effect on the respiratory system of the human body. In addition, the settling time of these particles is measured in hours. Thus, even after the termination of work, the risk of dust exposure to the human body remains. Insignificant time of inhalation of these particles can lead a person to disability and death. Insurance payments in case of disability and compensation in case of death of an employee for these reasons all over the world amount to multibillion sums, which, even for this reason, makes the issue of combating this phenomenon very urgent. In this paper, the process of generation of hazardous and harmful production factors arising as a result of dust formation during the stone cutting, the regularity of dust distribution by fractions, quantitative indicators of the process for improving equipment, as well as individual and collective means of personnel protection are determined. The shape and dimensions of the cutting grains of the cutting discs are studied in the paper, depending on the grain material and the grain size of the cutting disc. Thecurvature radii of the cutting grains depend on the grain material and the grain size of the cutting disc. The actual number of grains participating in cutting and, consequently, in dust formation is shown. The dimensions of the dust particles depend on the graininess of the cutting discs and the processing regimes. Using the law of normal particle size distribution, the percentage of dust particles is determined depending on processing conditions. The rate of particle settling depends on their size and mass. The formation of a dust-air mixture and its probable concentration and chemical composition of dust depend on the chemical composition of the cutting materials. Regularities are obtained, which can give an opportunity to improve the individual and collective protection of workers from this harmful production factor.

Quantitative Phase Field Simulation of α Particle Dissolution in Ti–6Al–4V Alloys Below β Transus Temperature

A quantitative phase field method of multi-component diffusion-controlled phase transformations coupled with the Kim–Kim–Suzuki model was applied to study the effect of initial particle size distribution (PSD) in 3D and space distribution in 2D on dissolution of α particles in Ti–6Al–4V alloy below β transus temperature in real time and length scale. The thermodynamic and mobility data were obtained from Thermo-Calc and DICTRA softwares, respectively. The results show that the volume fractions of α particles decay with time as: $$ f = f_{\text{eq}} + (f_{0} - f_{\text{eq}} )\exp ( - Kt^{n} ) $$ for four cases of PSD. The sequence of dissolution kinetics from fast to slow is: uniform PSD, normal PSD, lognormal PSD and bimodal PSD. The space distribution is found to be a major factor affecting the dissolution kinetics and the microstructures. When the distance of the particles is less than critical value, the dissolution rates reduce with the decrease in distance. The Al and V concentration fields around the particles appear more obvious soft impingement.

Identifying multi-stage nanocrystal growth using in situ TEM video data

ABSTRACTThe in situ transmission electron microscopy technique is receiving considerable attention in material science research, as its in situ nature makes possible discoveries that ex situ instruments are unable to make and provides the capability of directly observing nanocrystal growth processes. As incresing amounts of dynamic transmission electron microscopy (TEM) video data become available, one of the bottlenecks appears to be the lack of automated, quantitative, and dynamic analytic tools that can process the video data efficiently. The current processing is largely manual in nature and thus laborious, with existing tools focusing primarily on static TEM images. The absence of automated processing of TEM videos does not come as a surprise, as the growth of nanocrystals is highly stochastic and goes through multiple stages. We introduce a method in this article that is suitable for analyzing the in situ TEM videos in an automated and effective way. The method learns and tracks the normalized particle size distribution and identifies the phase-change points delineating the stages in nanocrystal growth. Using the outcome of the change-point detection process, we propose a hybrid multi-stage growth model and test it on an in situ TEM video, made available in 2009 by Science.

Surface roughness modelling for Double Disk Magnetic Abrasive Finishing process

Magnetic Abrasive Finishing (MAF) is a super finishing process having capability to produce surface finish in nano-meter level. The value of surface roughness obtained using MAF process depends upon the material properties of work piece and process factors. In the present work, a mathematical model has been proposed for Double Disk Magnetic Abrasive Finishing (DDMAF) process. DDMAF process is a process that can effectively finish even the flat paramagnetic work piece, which were considered ineffective to be finished by conventional MAF. In the present work, the surface roughness has been modelled as a function of workpiece material properties and process factors namely working gap, abrasive mesh number, percentage weight of abrasive, rotational speed and feed rate. The process model utilizes Lorentz force and Amperes law to estimate the finishing force experienced by an iron particle. The force so obtained has been used to calculate the finishing force transferred to the abrasive particle by using force equilibrium between iron and abrasive particle. The effect of normal distribution of abrasive particle size and the effect of frictional force on finishing forces have also been considered in this work. A MatLab code has been developed to include all the above aspects to determine the change in surface roughness. The model so obtained has been validated using experimental findings and thereafter used to study the effect of various process parameters.

LBM prediction of effective electric and species transport properties of lithium-ion battery graphite anode

Numerical models play a vital role in the developing and performance optimization of lithium-ion batteries. The key factor to the prediction accuracy of macro-scale models is the specification of effective transport properties. This study, based on the anisotropic microstructure of graphite anode reconstructed by an ellipsoid-based simulated annealing method (SAM), established a mesoscopic model of diffusion process to predict the effective electric and species transport properties of lithium-ion battery graphite anode via lattice-Boltzmann (LB) method. The effect of particle size on the transport properties of graphite anode was discussed in detail. In the electrode through-plane direction, if the ellipsoidal particles are thinner and flatter, both the effective electric and species transport properties decrease; in the other two directions, the effective electronic charge transport properties barely change with the change of particle size while the effective species transport properties increase along with the increase of the size of particles. In addition, to get a more accurate replica of the real graphite anode, we assumed the sizes of solid particles follow a normal distribution and reconstructed the microstructure of electrode. The LB calculation results reveal that the normal distribution of particle size increases the electronic charge conductivity in the electrode through-plane direction and decreases in the other two directions, compared to the electrode of constant-sized particles; the effective species diffusivities (or ionic charge conductivities) in electrode through-plane direction for different microstructures are closer.

Numerical study of different particle size distribution for modeling of solid-liquid extraction in randomly packed beds

Abstract In this paper, the Montan wax extraction as an industrial example for a solid-liquid extraction process is modeled and simulated using 2D and 3D CFD models. Carrying out various simulation studies, the applied particle size distribution of the solid phase is studied concerning its impact on the extraction kinetics and the extraction rates. In the modeling, the solid phase, which is a wax-containing brown coal, is described by establishing 2D models of random packing in cylindrically packed beds. In the study, the applicability of a 2D CFD model is validated against simulation result of a 3D model of a random packing. The random packing of the 2D model considers the same mean particle size with a uniform distribution and a normal distribution, respectively. The simulation result indicates that the extraction rate is greatly increased using the brown coal of a normal particle size distribution, which is a good match with experimental results.

Arrange and Average Algorithm for Microphysical Retrievals with A “3β+3α” Lidar Configuration

We present the results of a comparison study in which a simple, automated, and unsupervised algorithm, which we call the arrange and average algorithm, was used to infer microphysical parameters (complex refractive index (CRI), effective radius, total number, surface area, and volume concentrations) of atmospheric aerosol particles. The algorithm normally uses backscatter coefficients ( β ) at 355, 532, and 1064 nm and extinction coefficients ( α ) at 355 and 532 nm as input information. We compared the performance of the algorithm for the existing “3 β +α” and potential “3 β +3α” configurations of a multiwavelength aerosol Raman lidar or highspectral-resolution lidar (HSRL). The “3 β +3α” configuration uses an extra extinction coefficient at 1064 nm. Testing of the algorithm is based on synthetic optical data that are computed from prescribed CRIs and monomodal logarithmically normal particle size distributions that represent spherical, primarily fine mode aerosols. We investigated the degree to which the microphysical results retrieved by this algorithm benefits from the increased number of input extinction coefficients.

Structurization and phase formation in the course of preparing nanocomposite TiN–Ni ion-plasma vacuum–arc coatings and their thermal stability

Structure and phase formation in the course of fabricating composite ion–plasma vacuum-arc nanocrystalline TiN–Ni coatings are investigated in a broad range of nickel concentrations (from 0 to 26 at %). It is established that the introduction of Ni into the coating composition refines the nitride phase crystallites. The arithmetic mean size of TiN grains decreases from 100–120 to 15–18 nm upon varying the Ni concentration from 0 to 12 at % and normal particle-size distribution. The further increase in the Ni content is accompanied by the transition to the polymodal particle distribution with an increase in their arithmetic mean diameter to 27 nm for the third mode. Nickel in coatings at a concentration of 12–13 at % Ni is in the X-ray amorphous state. As the concentration increases above 13 at %, the TiNi intermetallic compound is formed in the composition of coatings. This phenomenon in turn causes the appearance of porosity in the structure of the deposited layer. The blocking role of nickel simultaneously weakens with the formation of the intermetallic compound, which manifests itself in the growth of separate TiN grains to 30–35 nm. The TiN–Ni coatings are characterized by the thermal stability of the structure and composition upon heating to 800°C.

An insight into the yielding and normal compression of sand with irregularly-shaped particles using DEM

The micro mechanics of one-dimensional and isotropic normal compression of granular soil have recently been revealed using the discrete element method. By modelling soil grains as spheres and implementing a new crushing model, the authors have previously investigated the influence of fracture mechanism, particle strengths (and distributions), and the size-hardening law on both the normal compression line and resultant particle size distribution; this resulted in a new compression law. In this work, irregular particle shape is introduced, using ‘clumps’ (groups of spherical particles), allowing different relative densities of the same material to be subjected to normal compression. An investigation into the mechanics of yielding is presented, in which the onset of crushing is related to the average particle octahedral shear stress and ‘yield’ is seen to be a function of the available void space. Beyond yield, the normal compression lines for the clumps at different initial densities are examined and compared to that for spheres. The effects of coordination number and particle shape on the normal compression are studied, and in particular the micro mechanics behind the evolution of a fractal particle size distribution are revealed.

Microstructure and micromechanics of polydisperse granular materials: Effect of the shape of particle size distribution

The uniaxial compression of polydisperse spheres with continuous: normal, log-normal, arbitrary and discrete uniform particle size distribution was modelled with the discrete element method (DEM). The evolution of solid fraction, coordination number and fabric tensor with increasing compressive stress was investigated in granular packings of equal mean particle diameter and standard deviation of particle mean diameter. The study of the relationship between the shape of particle size distribution and the micromechanical properties of granular packings included the determination of the contact forces and the degree of mobilisation of friction in contacts between particles. Slight influence of the shape of continuous particle size distribution on the solid fraction and coordination number in polydisperse packings was observed. The discrete uniform distribution provided the number of contacts lower by 7% as compared to continuous distribution. Concerning the mobilisation of friction in contacts between spheres, the average ratio of the tangential: normal contact forces in packing with discrete distribution was 25% higher than the one calculated for normal particle size distribution.