Flow simulations of size disperse granular particles with realistic contact models.
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This article discusses a unique agglomeration modeling methodology developed based on binary collisions to combine the effects of heterogeneity in ash chemistry and granular physics. A simple population balance is defined to find changes in the particle size distribution (PSD) of a fluidized bed. Thermodynamic equilibrium calculations and a computational fluid dynamics (CFD) code, are used to obtain hydrodynamic parameters. A method to calculate and use a distribution of collision frequencies in fluidized beds, corresponding to a poly-disperse particle size distribution, was developed in order to incorporate the particle level heterogeneities. A distribution of collision frequencies obtained for poly-dispersed particles showed a three orders of magnitude higher collision frequency amongst the smaller particles than the coarser ones, at the initiation of agglomeration. Ash agglomeration occurred when the slag amount (binder) was less than 10 wt% at temperatures <850 °C for Pittsburgh No. 8 coal.
The close-packed mesocrystal structures from soft-matter assemblies have recently received attention due to their structural similarity to atomic crystals, displaying various sphere-packing Frank–Kasper (FK) and quasicrystal structures. Herein, diverse mesocrystal structures are explored in second-generation dendrons (G2-X) designed with identical wedges, in which the terminal functionalities X = CONH 2 and CH 2 NH 2 represent two levels of the strong and weak hydrogen-bonding apexes, respectively. The cohesive interactions at the core apex, referred to as the core interactions, are effectively modulated by forming heterogeneous hydrogen bonds between these two functional units. For the dendron assemblies compositionally close to each pure component of G2-CONH 2 and G2-CH 2 NH 2 , their own FK A15 and C14 phases dominate other phases, respectively. We show the existence of the wide-range FK σ including the dodecagonal quasicrystal (DDQC) phases from the dendron mixtures between G2-CONH 2 and G2-CH 2 NH 2 , providing an experimental phase sequence of A15–σ–DDQC–C14 as the core interactions are alleviated. Intriguingly, the temperature dependence of particle sizes shows that the high plateau values of particle sizes are maintained equivalently until each threshold temperature (T th ), followed by a prompt decrease above the T th . A decrease in T th by alleviating the core interactions and its composition dependence suggest that the more size-dispersed particles, the more susceptibility to chain exchange with increasing temperature. Here our results on the formation of supramolecular dendron assemblies provide a guide to understand the core-interaction-dependent mesocrystal structures toward the fundamental principle underlying the temperature dependence of their particle sizes.
Here, we use machine learning (ML) enhanced computational reverse engineering analysis of scattering experiments (CREASE) to interpret small-angle X-ray scattering (SAXS) data obtained from a system of nanoparticles without a priori knowledge of their exact shapes (e.g. spheres or ellipsoids), sizes (0.5–50 nm) and distributions. The SAXS measurements yielded three categories of scattering profiles exhibiting 'strong', 'weak' and 'no' features. Diminishing features (e.g. broadening or disappearing peaks) in scattering profiles have always been attributed to the presence of significant dispersity in the system. Such featureless SAXS data are not suitable for traditional analysis using analytical models. If one were to fit a relevant analytical model (e.g. the lmfit analytical model for polydisperse spheres) to these 'weak' and 'no' SAXS profiles from our nanoparticle systems, one would obtain non-unique interpretations of the data. Relying on electron microscopy to identify the distributions of nanoparticle shapes and sizes is also unfeasible, especially in high-throughput synthesis and characterization loops. In such situations, to identify the distributions of particle sizes and shapes that could be present in the sample, one must rely on methods like ML-CREASE to interpret the data quickly and output all relevant interpretations about the structure present in the system. The ML-CREASE optimization loop takes the experimental scattering profile as input and outputs multiple candidate solutions whose computed scattering profiles match the SAXS profile input. The ML-CREASE method outputs distributions of relevant structural features, such as the volume fraction of the nanoparticles in the system and the mean and standard deviation of the particle size and aspect ratio, assuming a type of distribution (e.g. normal, log-normal) for size and aspect ratio. We find that, for the SAXS profiles analyzed here, accounting for the shape dispersity along with size dispersity of the nanoparticles using ML-CREASE improved the match between the computed scattering profiles and input experimental profiles.
Structural properties of iron-oxide nanoparticles deeply affect their magnetic performance in many applications such as with superparamagnetic relaxometry, when employed as cell-targeted magnetic nanoparticles for in vivo diagnostics. In this work, we present a detailed characterization of model nanoparticles for this application, with an average size of ~25 nm and a narrow size dispersion (σ < 7%). Considering the intrinsic structural properties of these model nanoparticles, the study of temperature dependence and correlation between dc-magnetization and superconducting quantum interference detector-relaxometry are discussed based on known theoretical predictions and computer simulations of the magnetic dipole moment and characteristic decay constants. Furthermore, computer simulations provide support in clarifying how important the overall collective magnetization is affected by particle size dispersion, which has a direct role on sustaining the magnetic relaxation signal in the temperature range required in preclinical and clinical settings.
Through this collaborative effort, a new 3D printing platform for ceramics called laser-induced slip casting (LIS) was explored and developed for improving the processing and manufacture of silicon carbide (SiC), a high temperature ceramic material. This method prints layers of ceramic slips/slurries with subsequent selective-laser heating to dry each layer to build a 3D structure. The completed and dried part is then sintered. This manufacturing process is inherently lower cost than alternative ceramic printing methods such as binder jet technology or stereolithography when it comes to the feedstock material but is more expensive than robocasting or direct ink writing. However, it has the potential to make more controlled parts with less defects compared to robocasting. The largest cost is the heating source for the printer. With this technique, there is potential to make large ceramic parts in a near-net shape. Further, there is no known commercial manufacturing of 3D printed ceramics that creates a large range of different high-density ceramics at large scale. The goals and outcomes for the development of the new printing technology were: 1) assessing the technology with alumina by characterizing coupons, 2) printing and sintering of silicon carbide (SiC), and 3) characterization and properties testing of materials printed and a scale-up of SiC part(s). Through this collaborative project, it was sought to provide the best solution to achieving highly dense and near-net shaped ceramic parts at large scale and reduced cost. The goal was to produce high density sintered materials for applications in defense, energy generating systems, armor, and wear parts using 3D printing methods. Variables including powder particle sizes, dispersant molecular weights (MWs), binders, printing parameters, and post processing were explored as well as the characterization of the physical properties (add specifics here).
Airborne particles are a major route for transmission of COVID-19 and many other infectious diseases. When a person talks, sings, coughs, or sneezes, nasal and throat secretions are spewed into the air. After a short initial fragmentation stage, the expelled material is mostly composed of spherical particles of different sizes. While the dynamics of the largest droplets are dominated by gravitational effects, the smaller aerosol particles, mostly transported by means of hydrodynamic drag, form clouds that can remain afloat for long times. In subsaturated air environments, the dependence of pathogen-laden particle dispersion on their size is complicated due to evaporation of the aqueous fraction. Particle dynamics can significantly change when ambient conditions favor rapid evaporation rates that result in a transition from buoyancyto-drag dominated dispersion regimes. To investigate the effect of particle size and evaporation on pathogen-laden cloud evolution, a direct numerical simulation of a mild cough was coupled with an evaporative Lagrangian particle advection model. The results suggest that while the dispersion of cough particles in the tails of the size distribution are unlikely to be disrupted by evaporative effects, preferential aerosol diameters (30-40 μm) may exhibit significant increases in the residence time and horizontal range under typical ambient conditions. Using estimations of the viral concentration in the spewed fluid and the number of ejected particles in a typical respiratory event, we obtained a map of viral load per volume of air at the end of the cough and the number of virus copies per inhalation in the emitter vicinity.
We report that high-entropy alloy (HEA) nanoparticles (NPs) hold great promise in electrocatalysis because of their nearly unlimited compositions, tailorable active sites, and high durability. However, the synthesis of these compositionally complex structures as monodisperse NPs remains a challenge by colloidal routes because the different rates of metal precursor reduction lead to phase separation. Here, we report the conversion of core@shell NPs into HEA NPs through annealing, with conservation of sample monodispersity. This potentially general route for high-quality HEA NPs was demonstrated by preparing PdCu@PtNiCo NPs via seed-mediated co-reduction, wherein Pt, Ni, and Co were co-deposited on PdCu seeds in solution. These multimetallic NPs were then converted to single-crystalline and single-phase PdCuPtNiCo NPs through annealing. On account of their small particle size, highly dispersed Pt/Pd content, and low elemental diffusivity, these HEA NPs were found to be a highly efficient and durable catalyst for the oxygen reduction reaction. They were also highly selective for the four-electron transfer pathway. We expect that this new synthetic strategy will facilitate the synthesis of new HEA NPs for catalysis and other applications.
High-entropy crystalline materials are attracting more attention. In principle, high-entropy metal carbides (HMCs) that contain five or more metal ions, possess more negative free energy value during catalysis. But its preparation is challenging because of the immiscibility of multi metal cations in a single carbide solid solution. Here, a rational strategy for preparing HMC is proposed via a coordination-assisted crystallization process in the presence of Br-based poly(ionic liquids). Through this method, Mo 0.2 W 0.2 V 0.2 Cr 0.2 Nb 0.2 C nanoparticles, with a single cubic phase structure, incorporated on porous carbon, are obtained (HMC@NC). By combination of well dispersed small particle size (~4 nm), high surface area (~270 m 2 g –1 ), and high-entropy phase, HMC@NC can function as a promising catalyst for the dehydrogenation of ethylbenzene. Here, unexpected activity (EB conv.: 73%) and thermal stability (>100 h on steam) at 450 °C are observed. Such a facile synthetic strategy may inspire the fabrication of other types of HMCs for more specific tasks.
One dominant challenge facing the development of biorefineries is achieving consistent system throughput with highly variant biomass feedstock quality and handling performance. Current handling unit operations are adapted from other sectors (primarily agriculture), where some simplifying assumptions about granular mechanics and flow performance do not translate well to a highly compressible and anisotropic material with nonlinear time- and stress-dependent properties. This work explores the shear and frictional properties of loblolly pine at multiple experimental test apparatus and particle scales to elucidate a property window that defines the shear behavior over a range of material attributes (particle size, size distribution, moisture content, etc.). In general, it was observed that the bulk internal friction and apparent cohesion depend strongly on both the stress state of the sample in granular shear testers and the overall particle size and distribution span. For equipment designed to characterize the quasi-static shear stress failure of bulk materials ranging from 50 to 1,000 ml in test volume, similar test results were observed for finely milled particles (50% passing size of 1.4 mm) with a narrow size distribution (span between 10 and 90% passing size of 0.9 mm), while stress chaining and over-torque issues persisted for the bench-scale test apparatus for larger particle sizes or widely dispersed sample sizes. Measurement of the anisotropic particle–particle friction ranged from coefficients of approximately 0.20 to 0.45 and resulted in significantly higher and more variable friction measurements for larger particle sizes and in perpendicular alignment orientations. To supplement these laboratory-scale properties, this work explores the flow of loblolly pine and Douglas fir through a pilot-scale wedge-shaped hopper and a screw feeder. For the gravity-driven hopper flow, the critical arching distance and mass discharge rate ranged from approximately 10 to 30 mm and 2 to 16 tons/hour, respectively, for both materials, where the arching distance depends strongly on the overall particle size and depends less on the hopper inclination angle. Comparatively, the auger feeder was found to be much more impacted by the size of the particles, where smaller particles had a more consistent and stable flow while consuming less power.
Four ODS FeCrAl alloys with base alloy composition Fe-16Cr-4Al-2Mo-0.2Y (wt.%) were mechanically alloyed with minor additions of CeO 2 and Y 2 O 3 additions and were successfully extruded into rectangular-shaped bars. Two oxide compositions were explored, which were designated NC with oxide addition of 0.5CeO 2 plus 0.2Yin solution of the base alloy powder and PC with additions of 0.5CeO 2 and 0.7Y 2 O 3 to elevate the O level. The microstructure of both alloys revealed by SEM and EBSD to consist of very small grains with sizes of ~450-520 nm and weak texture components. Sub-size tensile specimens fabricated from both alloys were tensile tested from room temperature to 800ºC. The results for both alloys from room temperature to 400ºC showed very high strengths, but low ductilities, especially uniform strain. From 500ºC to 800ºC, the strengths decreased rapidly, and the ductilities increased significantly. The reason for the rapid decreases in tensile strengths above 500ºC for both alloys is not clear, but will require advanced characterization techniques including atom probe tomography, to determine the size and number density of the nano-size oxide particles, and TEM/EFTEM, for investigating the dispersion of nano-size oxide particles on grain boundaries to assess the Zener grain boundary pinning effect, which will hinder grain coarsening at high temperatures that may enhance the Hall Petch strengthening mechanism.
Industrially, ammonia is produced by Haber-Bosch process under high temperatures and pressures, consuming more than >2% of the world's energy production. This paper presents microwave-assisted catalytic synthesis of ammonia operated at atmospheric pressure and temperatures from 260 to 360 °C. A Cs-promoted Ru catalyst supported on cerium oxide with different metal loading (4–24 wt% Ru) and support particle size (25nm, 50nm and 5μm) was investigated. The small size cerium oxide support resulted in the highest activity while the large cerium oxide support was less favorable, leading to lower activity associated to large Ru particle size and lower dispersion.
Abstract not provided
The role of particle shape in evaporation-induced auto-stratification in polydisperse colloidal suspensions is explored with molecular dynamics simulations of mixtures of spheres and aspherical particles. A unified framework based on the competition between diffusion and diffusiophoresis is proposed to understand the effects of shape and size dispersity. In general, particles diffusing more slowly (e.g., larger particles) tend to accumulate more strongly at the evaporation front. However, larger particles have larger surface areas and therefore greater diffusiophoretic mobility. Hence, they are more likely to be driven away from the evaporation front via diffusiophoresis. For a rapidly dried bidisperse suspension containing small and large spheres, the competition leads to “small-on-top” stratification. Here, we employ a computational model in which the diffusion coefficient is inversely proportional to particle mass. For a mixture of spheres and aspherical particles with similar mass, the diffusion contrast is reduced, and the spheres are always enriched at the evaporation front as they have the smallest surface area for a given mass and, therefore, the lowest diffusiophoretic mobility. Furthermore, for a mixture of solid and hollow spheres that have the same outer radius and thus the same surface area, the diffusiophoretic contrast is suppressed, and the system is dominated by diffusion. Consequently, the solid spheres, which have a larger mass and diffuse more slowly, accumulate on top of the hollow spheres. Finally, for a mixture of thin disks and long rods that differ significantly in shape but have similar mass and surface area, both diffusion and diffusiophoresis contrasts are suppressed, and the mixture does not stratify.
Thermal maturity of source rocks indicated by vitrinite reflectance (R o ) is an important and reliable parameter to determine the petroleum potential of sedimentary basins. Originally, R o was used to characterize the degree of coalification of coals and thus works well for coal seams, but it has limitations when used to determine the thermal maturity of black shale successions. A suite of six coal-shale pairs from the Upper Devonian black shale formations in the eastern United States (New Albany Shale, Ohio Shale, Dunkirk Shale, and Rhinestreet Shale) was selected to study the applicability of R o in assessing the thermal maturity of black shale successions. The results show that vitrinite in the studied coal samples is dominated by collotelinite, whereas vitrinite in black shales occurs as small dispersed particles (~5 μm) in the mineral matrix. When comparing the size and morphology of dispersed vitrinite particles in shales and collotelinite fragments in coals, vitrinite in shales and adjacent coals should have the same origin. The measured mean random R o of vitrinite in coals ranges from 0.51–0.68%, and is 0.04–0.11% (average 0.07%) lower than that of dispersed vitrinite particles in enclosing shales. This observation contrasts with previously reported R o suppression in liptinite-rich black shales. A high standard deviation of R o measurements indicates a highly heterogeneous nature of vitrinite in black shales. Three mechanisms can be envisioned to contribute to the higher R o of dispersed vitrinite particles in black shales relative to enclosed coals. First, small vitrinite particles may become more oxidized because small particles are more frequently suspended during transport to the site of deposition and reworking of bottom sediments after deposition than large pieces of driftwood that will turn into enclosed coal lenses during burial diagenesis. Second, misidentification of zooclast (e.g., chitinozoan) fragments as vitrinite due to loss of diagnostic morphology. Third, various macerals in the vitrinite group (e.g., corpogelinite and collotelinite) may have had different original reflectance values. Based on the petrographic characteristics of vitrinite and vitrinite-like particles, caution should be applied when using the R o values of dispersed vitrinite and vitrinite-like particles in black shales as indicators of thermal maturity.
More efficient lean-burn, natural gas engines are limited by greenhouse gas emissions due to methane oxidation catalysts (MOC) that suffer from water inhibition and high temperature activation. Herein, we report that the addition of Mg to supported 1 wt.% Pd MOCs improved hydrothermal stability even after severe hydrothermal aging. The superior methane oxidation activity compared to the corresponding Mg-free catalyst was attributed to (1) influence of Mg during surface roughening and restructuring at 700 °C on metal-support interaction, (2) reducibility of PdOx sites and (3) preferential stabilization of active Pd (1 0 0) facets in the sample as was evidenced by H 2 TPR and CO TPD characterization experiments. Methane conversion under synthetic exhaust conditions relevant to natural gas, lean-burn engines were investigated. In conclusion, BET, TPR, CO pulse chemisorption followed by TPD provided valuable insights into the surface area, pore volume, reducibility, Pd dispersion and Pd particle size of the selected catalyst samples.
In this project, the scanning electron microscopy (SEM) sampling method used during the statistical design study (SDS) was investigated to determine if any sampling biases were present in the analyzed data. Using standard particle size distribution powders from the National Institute of Standards and Technology (NIST 1984 standard reference material) with the origin wet dispersion method, it was determined that a bias to smaller particles was present. This was supported by theoretical calculations using Stokes’ law to determine the settling rate of spherical particles of roughly the same size and mass as those found in the SDS. Based on the theoretical calculations, it was determined that the settling rate for each of the 76 powder sets in the SDS could be unique based on specific particle shape and mass distributions, making a universal correction factor/formula not applicable. Therefore, priority shifted to developing an improved wet dispersion method that significantly reduced the particle settling rate for all particle size and shapes. This was achieved by replacing the original solvent (isopropyl alcohol) with a heavy liquid (lithium heteropolytungstates), which dramatically slowed the settling rate and allowed for the capture of a suitable homogeneous aliquot. SEM imaging and Morphological Analysis for Material Attribution (MAMA) software analysis were conducted on the NIST standard, and the SEM/MAMA data were compared to data captured by a dynamic image analysis particle size analyzer. The resulting data confirmed that the new wet dispersion method does indeed deliver an improved representative aliquot to the SEM stub. For instance, in the NIST certificate, the average particle size is ~17.1 µm ± 2.2 µm with a normal distribution. The initial wet dispersion method resulted in a drastically reduced average particle size of 6.1 µm in addition to a non-representative heavy bi-modal distribution whereas the improved LST wet dispersion method resulting in an average particle size that was much closer to the NIST certificate (12.7 µm) with a similar normal distribution. Although the improved method was still short of the NIST certificate average, atomic force microscopy analysis determined that the resulting ~20-25% reduction in size was due to particles sinking into the carbon sticky tape used for SEM imaging. It is believed that that this bias can be calibrated in a much more predicable manner than the original settling rate bias. In addition, the matching normal distribution curves between the NIST certificate and the heavy liquid method indicate a much-improved representative aliquot has been sampled and imaged. A surrogate CeO 2 powder was used to reflect PuO 2 more accurately and to aid in implementing radiological controls and shielding. The resulting data sets from the SEM/MAMA method and the particle size analyzer give almost identical average particle sizes and particle distribution statistics. Future work will re-analyze several select runs from the SDS to determine if morphological signatures can be found with the improved sampling method.