Particuology最新文献

A novel Positron Emission Projection Imaging (PEPI) algorithm designed to compute the plane-projected spatial distribution of radiolabelled materials without the need for collimation is introduced. By leveraging improved data efficiency, we have achieved a technique with enhanced spatial resolution and temporal resolution compared to previous PEPI algorithms. Validation of this algorithm was conducted using synthetic data generated from a digital twin of a PET scanner, demonstrating its accuracy for practical applications. The industrial advantage of this novel algorithm was applied in the imaging of laminar flow mixing within a ploughshare mixer, with the experimental results compared against those obtained from validated computational fluid dynamics (CFD) models. This comparison highlights an important use case for PEPI as a robust validation tool for CFD simulations, crucial for enhancing industrial processes. PEPI, which uses deeply penetrating gamma-photons, is now capable of imaging opaque fluids and solids in industrial casing. Future directions for this work include further algorithmic refinements and expanding its application across various industrial systems, establishing PEPI as a robust tool for in-depth industrial process analysis. The advancements presented here allow for optimized mixer design and enhanced process efficiency, extending the frontiers of tomographic imaging in industrial applications.

This review presents a summary of the research conducted thus far on the recovery of various types of valuable metals from red mud. The composition, properties, environmental hazards, and current status of comprehensive utilization of red mud were studied. A number of studies have been conducted on the use of red mud as a modifying additive for cement, the development of various catalysts based on red mud, and the recovery of various valuable metals from red mud. Furthermore, we examine several techniques for extracting various types of valuable metals from red mud, including pyrometallurgical recovery, wet leaching recovery, and emerging biobased technology recovery. We investigate the underlying principles, processes, research progress, and the potential for industrial application of these methods, and assess the advantages and disadvantages of each from the perspectives of economic and environmental benefits. Although these methods have certain disadvantages, in general, the recovery of various types of valuable metals from red mud is an effective way to solve the problem of red mud and the supply of metal raw materials. In conclusion, this paper presents an overview of the current state of red mud development and utilization, as well as the various methods employed for the recovery of valuable metals from red mud.

In this study, the particle morphology, and size changes in copper and iron powders were investigated by using a high-energy ball mill under the same experimental conditions. The particle sizes and morphologies of the powders were examined using a particle size analyzer and scanning electron microscopy (SEM). We also used the discrete element method (DEM) to analyze the ball impaction and the shear energy during the ball milling process. It was discovered that the quantity of milling balls and rotational speed had a major impact on energy dissipation through heat and plastic deformation as well as milling efficiency. The characteristic (particle morphology and size) of the iron and copper powder were differently changed under the same experimental conditions, shear energy, and ball impaction. When the particle sizes of copper and iron powder were compared under the same experimental conditions, the copper particle size increased from 21.6 μm to 280 μm, resulting in particle agglomeration, while the iron powder particle size decreased from 154 μm to 4.35 μm.

To improve room-temperature hydrogen storage, palladium (Pd) nanoparticles were innovatively decorated by carbon bridge onto the amino-group functioned Zr-terephthalate metal-organic framework (MOF) UiO-66 to reduce the diffusion energy barrier and then improve the hydrogen spillover effect. Powder X-ray diffraction shows broad Pd peak and retained UiO-66-NH₂ integrity after Pd decoration. The hydrogen uptake capacity show that UiO-66-NH₂-Pd exhibits best hydrogen storage performance than UiO-66-NH₂ and pristine UiO-66. The hydrogen up taken in Pd decorated UiO-66 (UiO-66-NH₂-1Pd) was close to 4 wt% under 20 MPa at room temperature. Density functional theory (DFT) calculations show that hydrogen adsorption energy of UiO-66-NH₂-Pd was −0.5897 eV, which was much lower than that of UiO-66-NH₂ (−0.3716 eV) and UiO-66 (−0.2975 eV). Ultimately, Pd decorated NH₂ group functioned UiO-66 enable improve storage capacities through hydrogen spillover under ambient conditions which could satisfy the demand for sustainable energy, especially for the long-term storage energy media.

Complex segregation occurs in a binary particle system with differing particle sizes and densities, particularly when the larger particles are heavier (S–D system, i.e., size minus density system). Predicting the segregation pattern driven by multiple mechanisms simultaneously is often challenging. This study explores the segregation mechanisms in a quasi-2D circular drum containing a S–D system, realizing a transition between the S-core and Core-and-band patterns by adjusting the drum rotation speed. During the transition of the segregation pattern, only the S-core pattern chiefly driven by the percolation mechanism is initially observed. As the rotation speed increases, the buoyancy mechanism and particle diffusion gradually strengthen, jointly driving the formation of the Core-and-band pattern. A dimensionless strength ratio, $\lambda =H/h$, where H and h respectively represent the diffusion and buoyancy strengths at length scales, is introduced to elucidate this transition. The Core-and-band pattern emerges when $\lambda$ reached 1.4.

The development of deep learning has inspired some new methods to solve the 3D reconstruction problem for Tomographic Particle Image Velocimetry (Tomo-PIV). However, the supervised learning method requires a large number of data with ground truth as training information, which is very difficult to gather from experiments. Although synthetic datasets can be used as alternatives, they are still not exactly the same with the real-world experimental data. In this paper, an Unsupervised Reconstruction Technique based on U-net (UnRTU) is proposed to reconstruct volume particle distribution explicitly. Instead of using ground truth data, a projection function is used as an unsupervised loss function for network training to reconstruct particle distribution. The UnRTU was compared with some traditional algebraic reconstruction algorithms and supervised learning method using synthetic data under different particle density and noise level. The results indicate that UnRTU outperforms these traditional approaches in both reconstruction quality and noise robustness, and is comparable to the supervised learning methods AI-PR. For experimental tests, particles dispersed in cured epoxy resin are moved by an electric rail with a certain speed to obtain the ground truth data of particle velocity. Compared with other algorithms, the reconstructed particle distribution by UnRTU has the best reconstruction fidelity. And the accuracy of the 3D velocity field estimated by UnRTU is 12.9% higher than that from the traditional MLOS-MART algorithm. It demonstrates significant potential and advantages for UnRTU in 3D reconstruction of particle distribution. Finally, UnRTU was successfully applied to the high-speed planar cascade airflow field, demonstrating its applicability for measuring complex fluid flow fields at higher particle density.

This article conducts first-principles calculations to initially explore the construction of two configurations, NaFeO₂ (NFO) and NaMnO₂ (NMO), and studies the mixing enthalpies under different Fe–Mn ratios. The results indicate that NaFe_3/8Mn_5/8O₂ (NFMO) exhibits the most thermodynamically stable structure. Subsequent calculations on the mixing enthalpies and volume changes during the sodium extraction process for NFO, NMO, and NFMO configurations are presented, along with the partial density of states (PDOS) and Bader charges of transition metals (TM) and oxygen. These calculations reveal the synergistic mechanism of Fe and Mn. Fe and Mn can engage in more complex electron exchanges during sodium extraction, optimizing the internal electron density distribution and overall charge balance, thereby stabilizing the crystal structure and reducing the migration of Fe³⁺ to the sodium layers during deep sodium extraction. The interaction between Fe’s 3d electrons and Mn’s 3d electrons through the shared oxygen atoms’ 2p orbitals occurs in the Fe–Mn–O network. This interaction can lead to a rebalancing of the electron density around Mn³⁺ atoms, mitigating the asymmetric electron density distribution caused by the d₄ configuration of the lone Mn³⁺ and suppressing the Jahn-Teller effect of Mn³⁺. Moreover, the synergistic effects between Fe and Mn can provide a more balanced charge distribution, reducing extreme changes to the charge state of oxygen atoms and decreasing the irreversible oxygen release caused by anionic redox reactions during deep sodium extraction, thereby enhancing the material’s stability. This in-depth study of the interaction mechanism at the microscopic level when co-doping Fe and Mn offers valuable insights for the rational design and development of high-performance cathode materials.

This research focuses on modeling a multi-zone circulating reactor (MZCR) in the polypropylene production process. In these reactors, designed for polyolefin production, small catalyst particles (20–300 μm) initiate polymerization in the presence of monomer gas. The reactor consists of two main regions: the riser and the downer. The riser operates in the fast fluidization and the downer is in the moving bed regime. Employing the two-fluid model with the Eulerian-Eulerian approach, the dynamics of both solid and gas phases were modeled by applying Newton's laws of motion and assuming spherical particles. The population balance of particles within the reactor was also coupled with the equations of motion. The simultaneous solution of these equations provides valuable insights into particle and fluid behavior, revealing trends such as the growth of polymer particles. Furthermore, the impact of various operating conditions was explored. This study also examined the effects of design parameters (gas inlet velocity, average inlet diameter, and temperature) on the system performance. For instance, it was shown that in the case where the solid circulation flux is 30 kg/(m² s) the velocity of particles in the bed increases from 0.4 at the inlet to 1.1 m/s in the fully developed zone, when it is 43 kg/(m² s) the velocity of particles increases from 0.3 to 1.4 m/s, and when it is 55 kg/(m² s), it is increased from 0.22 to 1.5 m/s. Additionally, trends in particle size distribution based on temperature adjustments were revealed. This study showed that higher temperatures accelerate the polymerization reaction rate, promoting faster growth kinetics and the formation of larger particles.

Use of cerium oxide nanoparticles (CeO₂ NPs) to optimize management of resistant microorganisms has received increasing attention due to non-specific activity of inorganic antibacterial agents. Understanding the mechanism of action is essential to elucidating the antibacterial activity of CeO₂ NPs against bacteria. Therefore, this review aims to summarize the antibacterial mechanisms of CeO₂ NPs and correlate the structural and physicochemical properties of CeO₂ NPs to their antibacterial activity. We further summarize the strategies for the improvement of the antibacterial performance of CeO₂ NPs and provide our opinions for future challenges as a conclusion.

We treat the accurate simulation of the calcination reaction in particles, where the particles are large and, thus, the inner-particle processes must be resolved. Because these processes need to be described with coupled partial differential equations (PDEs) that must be solved numerically, the computation times for a single particle are too high for use in simulations that involve many particles. Simulations of this type arise when the Discrete Element Method (DEM) is combined with Computational Fluid Dynamics (CFD) to investigate industrial systems such as quicklime production in lime shaft kilns.

We show that, based on proper orthogonal decomposition and Galerkin projection, reduced models can be derived for single particles that provide the same spatial and temporal resolution as the original PDE models at a considerably reduced computational cost. Replacing the finite volume particle models with the reduced models results in an overall reduction of the reactor simulation time by about 40% for the sample system treated here.