Size Distribution Function Research Articles

Formation of catalyst particles for the CNT growth from thin films: Experiment and simulation

The formation of Ni nanoparticles for the growth of carbon nanotubes on a TiNbarrier layer obtained by atomic layer deposition was studied. The interaction of the catalyst with the barrier layer is revealed. It consists of diffusion of Ni into TiN with activation energy of 0.75 eV.Assuming the melting of a nanosized Ni film, we simulated the formation of catalyst particles in terms of wetting transition theory. Comparison of simulation results and experimental data on catalyst particle formation allowed us to estimate effective interaction potential of Ni film and barrier layer. A change of the interaction potential due to variation of the film or barrier layer properties (e.g., material, structure, etc.) is responsible for change of the dynamics of catalyst particle formation including dynamics of their average size, number density and the shape of the size distribution function.

DISSOLUTION OF A POLYDISPERSE SYSTEM OF PARTICLES IN A NO-FLOW APPARATUS

The processes of dissolution and crystallization are mass transfer phenomena that have a number of complex physicochemical features, and which are very difficult to theoretically describe in various mathematical models. During the dissolution process, the crystals may undergo various changes, such as grinding, combining and crystal growth. As a result of the process of mass dissolution of crystals, different speeds of movement of individual faces can be observed, which can lead to their disappearance. These phenomena are the result of complex dynamics of the dissolution process, which includes inter-actions between solvent molecules and the crystal surface. In this work, the process of mass dissolution was studied using the crystal size distribution density function. The task was set to obtain an analytical solution of the equation. The main object of the study was the diffusion law of movement of the radius of spherical crystals during a periodic process. As part of this study, analytical solutions were obtained for some interesting practical cases related to the process of mass dissolution of spherical crystals and the density function of crystal size distribution. These solutions were described in the article in the form of mathematical formulas and supplemented with graphical data characterizing the main parameters of the process. The use of these analytical solutions can be useful for a more accurate description and improvement of the mass dissolution process itself. Optimizing this process will improve its efficiency, provide more precise control and efficient use of reagents.

Numerical Reconstruction of Proton Exchange Membrane Fuel Cell Gas Diffusion Layers

Flooding and dehydration reduce stability and power performance in Proton Exchange Membrane Fuel Cells (PEMFCs). The Gas Diffusion Layer (GDL) plays a crucial role in facilitating reactant gas transport and removing product water from the electrode. To suit various PEMFCs, GDLs with different shapes have been commercialized. The impact of the GDL structure on the surface-tension-driven water transport behavior remains poorly understood. However, this is one important aspect that can be controlled by proper design. In this study, the GDL performance is investigated by comparing curved and straight carbon fibers within the region. Specifically, an image-processing method extracts porosity, domain size, and fiber diameter from an experimental image-based GDL reconstruction. These parameters are utilized by in-house developed computer codes to stochastically reconstruct curved and straight carbon fiber GDLs, respectively. The real and reconstructed GDLs are compared in terms of pore size distribution, tortuosity, and permeability. Liquid transport in these GDLs and corresponding gas channels is simulated using a volume of fluid method in OpenFOAM 7.0.Figure 1(a) presents the T-shaped simulation domain and top view of three GDLs. Figure 1(b) displays the Cumulative Density Function (CDF) of the pore size distribution for the three GDLs, revealing that the main difference between the three GDLs lies in the pore diameter range of 10-30 µm. Upon completion of the research program, we aim to identify the influence of fiber shape on the GDL transport properties as well as the water behavior inside them. Figure 1

Effect of contact inhibition locomotion on confined cellular organization

Experiments performed using micro-patterned one dimensional collision assays have allowed a precise quantitative analysis of the collective manifestation of contact inhibition locomotion (CIL) wherein, individual migrating cells reorient their direction of motion when they come in contact with other cells. Inspired by these experiments, we present a discrete, minimal 1D Active spin model that mimics the CIL interaction between cells in one dimensional channels. We analyze the emergent collective behaviour of migrating cells in such confined geometries, as well as the sensitivity of the emergent patterns to driving forces that couple to cell motion. In the absence of vacancies, akin to dense cell packing, the translation dynamics is arrested and the model reduces to an equilibrium spin model which can be solved exactly. In the presence of vacancies, the interplay of activity-driven translation, cell polarity switching, and CIL results in an exponential steady cluster size distribution. We define a dimensionless Péclet number Q—the ratio of the translation rate and directional switching rate of particles in the absence of CIL. While the average cluster size increases monotonically as a function of Q, it exhibits a non-monotonic dependence on CIL strength, when the Q is sufficiently high. In the high Q limit, an analytical form of average cluster size can be obtained approximately by effectively mapping the system to an equivalent equilibrium process involving clusters of different sizes wherein the cluster size distribution is obtained by minimizing an effective Helmholtz free energy for the system. The resultant prediction of exponential dependence on CIL strength of the average cluster size and Q1/2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Q^{1/2}$$\\end{document} dependence of the average cluster size is borne out to reasonable accuracy as long as the CIL strength is not very large. The consequent prediction of a single scaling function of Q, particle density and CIL interaction strength, characterizing the distribution function of the cluster sizes and resultant data collapse is observed for a range of parameters.

Improved predictions of permeability properties in cement-based materials: A comparative study of pore size distribution-based models

Permeability is a fundamental physical property of a cement-based material and is closely related to its pore structure. This study developed an improved model for permeability prediction of cementitious materials with a function of the pore size distribution (PSD), in which PSD is simulated from single and compound lognormal distribution functions, respectively. The efficiency of the improved model is demonstrated by calculating permeability for mortars and pastes available in the literature. It is found that, first, for cementitious materials with unimodal PSD, the single lognormal distribution function is adequate to describe the pore structures. In contrast, for cementing mixtures presenting multimodal PSD, the compounded lognormal distribution function is highly recommended to cover a wide range of pore components. Second, the permeability model based on the compound PSD can predict well permeability to gas intrusion, whereas the single PSD-based model is applicable for calculating permeability via water intrusion. Furthermore, slippage effect and water saturation degree are introduced in the PSD-based model. Results prove that the corrected gas permeability model from the compound PSD performs well in estimating the gas permeability in unsaturated states. Finally, the compound PSD-based model is extended by considering varying porosities and relative humidities, which can be used to quantitively describe permeability when cementitious materials undergoing pore structure degradation induced by durability issues.

Modeling the distribution characteristics of vapor bubbles in cavitating flows

Dispersed vapor bubbles are the dominant rheology in cloud cavitation, and their size distribution is directly associated with cavitation noise and erosion. However, the numerical resolution of large numbers of dispersed bubbles remains a challenge. In this work, we establish a new cavitation model based on the population balance equation (PBE) that can predict the size distribution and spatiotemporal evolution of bubbles within cloud cavitation under different cavitation numbers. An expression for the phase transition source term without empirical parameters is derived based on the bubble size distribution (BSD) function, enabling the coupling of mass transfer in the governing equations with the PBE cavitation model. The cavitation model is solved alongside the Eulerian homogeneous mixture flow. The mass transfer between water and vapor, and the bubble coalescence and breakup under turbulent flows, are modeled to determine the BSD. The numerical model is carefully validated through comparisons with experimental results for cavitation flows on a wedge-shaped flat plate, and good agreement is achieved with respect to the pressure distribution, void fraction, and BSD. This confirms that our proposed cavitation model can accurately predict the void fraction and BSD within the cloud cavitation region.

Size-refocusing fitting of small-angle X-ray scattering from polydisperse nanoparticles for shape determination

Small-angle X-ray scattering (SAXS) which records reciprocal-space signals with characteristic Bessel-type oscillations is a powerful technique for studying nanoparticles. However, the size polydispersity (or size distribution) of nanoparticles in an ensemble sample smears the oscillational peaks and valleys in the SAXS profile, making it difficult to extract accurate real-space information (e.g. three-dimensional geometry) on the nanoparticles. In this work, a method capable of eliminating the size-distribution-induced smearing effect from SAXS profiles by taking the known size-distribution function into consideration has been developed. The method employs a penalized iterative regression to fit the pair distance distribution function (PDDF) derived from a SAXS profile, recovering the representative PDDF of the nanoparticles. The method has been evaluated with a series of nanoparticle systems of various shapes and size distributions, showing their PDDF profiles to have high fidelity to the reference ideal PDDF profiles. Inverse Fourier transformation of the recovered PDDF profiles gives SAXS profiles presenting the characteristic Bessel-type oscillations, enabling reconstruction of the representative three-dimensional geometry of the nanoparticles. This method will help in the use of SAXS to image synthesized colloidal nanoparticles where size polydispersity is inevitable.

Acid-activated natural zeolite and montmorillonite as adsorbents decomposing metsulfuron-methyl herbicide

Modern technologies of intensive crop production demand a wide application of various types of pesticides. The adverse effect of cropping intensification is that toxic organic compounds, in particular herbicides of the sulfonylurea family, are accumulated in the soil. The herbicide metsulfuron-methyl is soluble in water and accumulates in soil moisture, causing a toxic effect on plant development. The work examines the issue of the chemical stability of metsulfuron-methyl in a model aqueous environment for the targeted selection of a mineral that adsorbs the herbicide and simultaneously catalyzes the process of herbicide hydrolysis. Degradation processes of sulfonylurea herbicide metsulfuron-methyl in aqueous media in the presence of natural zeolite, acid-activated natural zeolite, and montmorillonite К10 were investigated. Under conditions of static contact of the adsorbents with an herbicide aqueous solution, montmorillonite was more active in the sorption and degradation of metsulfuron-methyl. During the first 1–2 days the herbicide concentration decreased by a factor of 5. An almost complete degradation of metsulfuron-methyl in aqueous dispersions of the acid-activated zeolite and montmorillonite was attained within 20 days. Pore size distribution functions (Barrett-Joyner-Halenda and standard non-local density functional theory methods) were evaluated. The acid treatment of the natural zeolite changed its porous structure - its specific surface area grew by a factor of 5. Three fractions of mesopores 4–10, 10–20, 20–30 nm were revealed in the original zeolite, and one fraction of pores 2.0–3.5 nm was found in the acid-activated zeolite. In the montmorillonite, only one group of 3–15 nm pores was detected. Studies of structural changes in natural zeolite after acid modification were carried out using the methods of X-ray fluorescence and X-ray phase analysis, scanning electron microscopy and infrared spectroscopy. The acid-activated montmorillonite and natural zeolite show promise for land reclamation work on detoxification of soils contaminated by sulfonylurea herbicides.

Fluidized Bed Reactor Modeling and Simulation with Aspen Plus

Fluidized bed reactor is useful for heterogeneous (multiphase) reactions, separations, size enlargement, coating, and blending in chemical and pharmaceuticals industries. To develop a model and simulation of this reactor with advanced systems process engineering software (Aspen Plus). Aspen plus has the capability to simulate real factory performance, design improved plants, and intensify profitability in present plants. This modeling and simulation are applied Geldart B particle classification, GGC is used for particle size distribution function, Newton parameters for mass convergence, and Ergun equation is used for minimum fluidization velocity calculation and pressure drop through the fluidized bed reactor. Generally, most of the predictions model from Aspen plus is a reasonable agreement through the experimental results. Advantage of this investigation is used small quantity of bed particles and identified their melting point temperature is above 340°C. This fluidized bed reactor model and simulation is functional for aluminum hydroxide decomposition reaction. The feed 275 kg/hr of Al(OH)3is converted into 169.6 kg/hr of aluminum oxide (Al2O3) and 89.9 kg/hr of water. A total mass conversion percent is 94.4% of aluminum hydroxide.

CO2 storage: threshold capillary pressure estimate in a remoulded caprock specimen

CO2 storage: threshold capillary pressure estimate in a remoulded caprock specimen

Results of capillary studies digital processing and interpretation with the aim of a pore channel size distribution function constructing

Pore channels distribution in reservoir by size largely determines such filtration parameters as absolute and phase permeability, as well as the residual oil saturation of the productive reservoir. This paper presents a technique for capillary research data digital processing in order to obtain a function graph of reservoir pore channels by size distribution. In this case, a generalized mathematical model of capillary pressure curves is used. The generalized model makes it possible to increase the accuracy of capillary curves approximation in the medium and small pore channels area. In this regard, the proposed digital processing technique makes it possible to increase the accuracy and efficiency of constructing the pore channel size distribution function. Keywords: pore channels; distribution density; permeability; oil saturation; digital processing technique.

The MAMA Algorithm for Fast Computations of Upwelling Far- and Mid-Infrared Radiances in the Presence of Clouds

A methodology for the computation of spectrally resolved upwelling radiances in the presence of atmospheric diffusive layers is presented. The algorithm, called MAMA (Martinazzo–Maestri), provides fast simulations over the whole longwave spectrum, with high accuracy, particularly for optically thin scattering layers like cirrus clouds. The solution is obtained through a simplification of the multiple-scattering term in the general equation of the radiative transfer in a plane-parallel assumption. The scattering contribution is interpreted as a linear combination of the mean ambient radiances involved in the forward and back-scatter processes, which are multiplied by factors derived from the diffusive features of the layer. For this purpose, a fundamental property of the layer is introduced, named the angular back-scattering coefficient, which describes the fraction of radiation coming from a hemisphere and back-scattered into a specific direction (the observer in our case). This property, easily derived from the phase function of the particle size distribution, can be calculated from any generic single-scattering properties database, which allows for simple upgrades of the reference optical properties within the code. The paper discusses the solutions for mean upward and downward ambient radiances and their use in the simplification of the general radiative transfer equation for thermal infrared. To assess the algorithm performance, the results obtained with the MAMA code are compared with those derived with a discrete ordinate-based radiative transfer model for a large range of physical and optical properties of ice and liquid water clouds and for multiple atmospheric conditions. It is demonstrated that, for liquid water clouds, the MAMA code accuracy is mostly within 0.4 mW/(m2cm−1sr) with respect to the reference code both at far- and mid-infrared wavelengths. Ice cloud spectra are also accurately simulated at mid-infrared for all realistic cloud cases, which makes the MAMA code suitable for the analysis of any spectral measurements of current satellite infrared sounders. At far infrared, the MAMA accuracy is excellent when ice clouds with an optical depth of less than 2 are considered, which is particularly valuable since cirrus clouds are one of the main targets of the future mission FORUM (Far-infrared Outgoing Radiation Understanding and Monitoring) of the European Space Agency. In summary, the MAMA method allows computations of cloudy sky high-resolution radiances over the full longwave spectrum (4–100 μm) in less than a second (for pre-computed gas optical depths and on a standard personal computer). The algorithm exploits the fundamental properties of the scattering layers, and the code can be easily updated in relation to new scattering properties.

Effect of substrate roughness on dynamics of wetting and decay of an ultrathin liquid film

In this study, we analyze the dynamics of wetting and decay of an ultrathin liquid film on a rough substrate. The dependence of disjoining pressure on liquid film thickness is derived by the direct numerical calculation for the Lennard-Jones pair interaction potential and 3D rough substrate with a random relief. It is shown that disjoining pressure for a rough substrate can be approximated by a similar function for a smooth one with highly fluctuating parameters over the substrate surface. The equation describing wetting and decay of 3D liquid film is modified, accounting for the calculated coordinate dependence of disjoining pressure the general form of capillary pressure depending on substrate relief. The approach is used for the simulation of drop formation from a nanosized liquid film for substrates with various roughness parameters. It is demonstrated that substrate roughness is responsible for accelerated film rupture and formation of “detectable” drops, increase in their number density, and decrease in average size. At the later stage, liquid film transformation is described by the power-law dependences of number density, average height and size with the exponents depending on substrate roughness. The blocking point of the size distribution function shifts to a larger value for a substrate with a more fine-grained structure. These changes in the growth rate and size distribution function of drops are associated with the change in conditions of liquid flow, which becomes inhomogeneous due to substrate roughness.

Characterization of the Ejecta from the NASA/DART Impact on Dimorphos: Observations and Monte Carlo Models

The NASA Double Asteroid Redirection Test (DART) spacecraft successfully crashed on Dimorphos, the secondary component of the binary (65803) Didymos system. Following the impact, a large dust cloud was released, and a long-lasting dust tail developed. We have extensively monitored the dust tail from the ground and the Hubble Space Telescope. We provide a characterization of the ejecta dust properties, i.e., particle size distribution and ejection speeds, ejection geometric parameters, and mass, by combining both observational data sets and using Monte Carlo models of the observed dust tail. The size distribution function that best fits the imaging data is a broken power law having a power index of –2.5 for particles of r ≤ 3 mm and –3.7 for larger particles. The particles range in size from 1 μm up to 5 cm. The ejecta is characterized by two components, depending on velocity and ejection direction. The northern component of the double tail, observed since 2022 October 8, might be associated with a secondary ejection event from impacting debris on Didymos, although is also possible that this feature results from the binary system dynamics alone. The lower limit to the total dust mass ejected is estimated at ∼6 × 106 kg, half of this mass being ejected to interplanetary space.

Unravelling the structural and dynamical properties of concentrated aqueous ammonium nitrate solutions: MD simulation studies

ABSTRACT In this work, we present, the concentration-dependent solvation structure and dynamics of an ammonium nitrate (AN) ion pair in aqueous media by means of classical molecular dynamics simulations. Structural properties were investigated by computing the potentials of mean force (PMFs), radial distribution functions (RDFs), cluster size distribution functions, and hydrogen bond dynamics. The change in the depth of the contact ion pair (CIP) and solvent-separated ion pair minima (SSIP) is marginal, whereas for the solvent-assisted ion pair (SAIP) is more stable at lower salt concentrations. The marginal difference in the depth of the CIP minimum of the PMF is explained based on cluster size distribution and hydrogen bonding. The solvation structure of the ions is not affected by the salt concentration. Ab-initio and classical molecular dynamics simulations (both constrained and unconstrained) give similar results for the solvation structure around the cation and the anion. The spatial density distribution functions of the anions around the cations show that the anions occupy the space around the ammonium hydrogen atoms. Profound differences in dynamical properties are found in the case of concentrated solutions as compared to dilute solutions.

A Network Clustering Algorithm for Protein Complex Detection Fused with Power-Law Distribution Characteristic

Network clustering for mining protein complexes from protein–protein interaction (PPI) networks has emerged as a prominent research area in data mining and bioinformatics. Accurately identifying complexes plays a crucial role in comprehending cellular organization and functionality. Network characteristics are often useful in enhancing the performance of protein complex detection methods. Many protein complex detection algorithms have been proposed, primarily focusing on local micro-topological structure metrics while overlooking the potential power-law distribution characteristic of community sizes at the macro global level. The effective use of this distribution characteristic information may be beneficial for mining protein complexes. This paper proposes a network clustering algorithm for protein complex detection fused with power-law distribution characteristic. The clustering algorithm constructs a cluster generation model based on scale-free power-law distribution to generate a cluster with a dense center and relatively sparse periphery. Following the cluster generation model, a candidate cluster is obtained. From a global perspective, the number distribution of clusters of varying sizes is taken into account. If the candidate cluster aligns with the constraints defined by the power-law distribution function of community sizes, it is designated as the final cluster; otherwise, it is discarded. To assess the prediction performance of the proposed algorithm, the gold standard complex sets CYC2008 and MIPS are employed as benchmarks. The algorithm is compared to DPClus, IPCA, SEGC, Core, SR-MCL, and ELF-DPC in terms of F-measure and Accuracy on several widely used protein–protein interaction networks. The experimental results show that the algorithm can effectively detect protein complexes and is superior to other comparative algorithms. This study further enriches the connection between analyzing complex network topology features and mining network function modules, thereby significantly contributing to the improvement of protein complex detection performance.

Particle-size polydispersity analysis based on the unified exponential/power-law approach to small-angle scattering

Controlling particle dispersity is of huge importance for practical applications in nanoscience and technology. The analysis of small-angle scattering of X-rays and neutrons for strongly polydisperse particulate systems is considered from the point of view of describing the type of size distribution function without applying classical regularization approaches. This article presents the development of a method for determining the polydispersity parameters of nanoobjects, based on the analysis of the ratio of various moments of the size distribution function, which are proportional to different invariants of the scattering curve. The use of the unified exponential/power-law approximation to describe small-angle scattering data makes it possible to determine the type of distribution, the average size and the spread. The possibilities of the method were tested for several hydrosols of metallic nanoparticles.

Size distribution of clusters and nucleation preference of trimers during SiC (0001) surface epitaxial growth under low coverage

In order to better understand the microscopic nucleation behavior during the epitaxial growth of a SiC crystal, a lattice kinetic Monte Carlo model is developed, in which Si and C particles are set as the basic elements. The events list is built up to implement group search and binary search, which can improve the efficiency of the algorithm. In addition, the Hoshen–Kopelman algorithm is also used to calculate the statistics of the properties of clusters. Then, the cluster size distribution function and the nucleation preference of trimers are analyzed. The results show that the cluster size distribution function obeys the Gauss distribution, and the proportion of crystal nuclei close to the average size gradually increases. Moreover, the growth temperature mainly affects the trapping rate of adatoms by dimers, and the C/Si ratio affects the formation of different types of dimers and the concentration of adatoms.

A computationally efficient approach for soot modeling with discrete sectional method and FGM chemistry

A novel approach for the prediction of soot formation in combustion simulations within the framework of discrete sectional method (DSM) based univariate soot model and Flamelet Generated Manifold (FGM) chemistry, referred to as FGM-CDSM, is proposed in this study. The FGM-CDSM considers the clustering of soot sections derived from the original soot particle size distribution function (PSDF) to minimize the computational cost. Unlike conventional DSM, in FGM-CDSM, governing equations for soot mass fractions are solved for the clusters, by using a pre-computed lookup table with tabulated soot source terms from the flamelet manifold, while the original soot PSDF is re-constructed in a post-processing stage. The flamelets employed for the manifold are computed with detailed chemistry and the complete sectional soot model. A comparative assessment of FGM-CDSM is conducted in laminar diffusion flames for its accuracy and computational performance against the detailed kinetics-based classical sectional model. Numerical results reveal that the FGM-CDSM can favorably reproduce the global soot quantities and capture their dynamic response predicted by detailed kinetics with a good qualitative agreement. Furthermore, compared to detailed kinetics, FGM-CDSM is shown to substantially reduce the computational cost of the complete reacting flow simulation with soot particle transport. Primarily, the use of FGM reduces the overall calculation by about two orders of magnitude compared to detailed kinetics, which is advanced further with the clustering of sections at a low memory footprint. Therefore, the present work demonstrates the promising capabilities of FGM-CDSM in the context of computationally efficient soot calculations and provides an excellent framework for extending its application to the simulations of turbulent sooting flames.

HST Imaging of Star-forming Clumps in Six GASP Ram-pressure-stripped Galaxies

Exploiting broad- and narrowband images of the Hubble Space Telescope from the near-UV to I-band rest frame, we study the star-forming clumps of six galaxies of the GASP sample undergoing strong ram pressure stripping. Clumps are detected in Hα and near-UV, tracing star formation on different timescales. We consider clumps located in galaxy disks and stripped tails and formed in stripped gas but still close to the disk, called extraplanar. We detect 2406 Hα-selected clumps (1708 in disks, 375 in extraplanar regions, and 323 in tails) and 3745 UV-selected clumps (2021 disk, 825 extraplanar, and 899 tail clumps). Only ∼15% of star-forming clumps are spatially resolved, meaning that most are smaller than ∼140 pc. We study the luminosity and size distribution functions (LDFs and SDFs, respectively) and the luminosity–size relation. The average LDF slope is 1.79 ± 0.09, while the average SDF slope is 3.1 ± 0.5. The results suggest that the star formation is turbulence-driven and scale-free, as in main-sequence galaxies. All of the clumps, whether they are in the disks or tails, have an enhanced Hα luminosity at a given size, compared to the clumps in main-sequence galaxies. Indeed, their Hα luminosity is closer to that of clumps in starburst galaxies, indicating that ram pressure is able to enhance the luminosity. No striking differences are found among disk and tail clumps, suggesting that the different environments in which they are embedded play a minor role in influencing the star formation.