Accurate quantification of drug concentration within the skin's interstitial fluid (ISF) remains a significant analytical challenge due to the limitations of invasive sampling and the inability of bulk measurements to resolve micro-scale distribution. Traditionally, predictive models have treated the skin as a static barrier, ignoring the dynamic matrix effects caused by ISF flow, which leads to substantial errors in estimating deep-tissue analyte concentrations. To address this, this study proposes a computational analytical strategy integrating Finite Element Method (FEM) with Computational Fluid Dynamics (CFD) to quantitatively profile drug transport under varying thermal conditions. By calibrating against HPLC-validated ex vivo permeation data at a reference temperature, diffusion coefficients and ISF flow velocities were extrapolated to predict behavior at other temperatures. This approach effectively decouples the influence of fluid dynamics from passive diffusion, allowing for the precise resolution of temperature-dependent permeation kinetics. The Flow-Field model demonstrated strong correlations with ex vivo skin permeation tests, achieving R2 values over 0.99 for various drugs and temperature conditions. This work establishes a robust in silico tool for the micro-scale profiling of analytes in complex biological tissues, offering a non-invasive alternative to estimate ISF concentrations where physical sampling is restricted.

Triboelectric charging, resulting from repeated particle-particle (P-P) and particle-wall (P-W) interactions, critically affects process safety and efficiency, yet remains insufficiently understood. This study presents a numerical investigation of tribocharging in a horizontal-bend-vertical pipe using our recent combined Computational Fluid Dynamics-Discrete Element Method (CFD-DEM) model, which is capable of modeling impact and frictional charging for various particle shapes. Dense- and dilute-phase conveying of non-spherical particles is simulated to assess charge evolution, wall erosion, gas-solid flow behavior, and P-P/W contact information. Charge mitigation strategy is explored, and pipe erosion under various particle shapes is also evaluated. Results reveal strong shape-dependent charging characteristics. Prolate particles achieve the highest equilibrium charge due to their elongated shape, which enhances P-P charge transfer, while oblate particles charge rapidly via extensive wall contact. Charge accumulation is amplified at bends, where secondary flows increase collision frequency and intensity. Shape-dependent drag forces and flow patterns show that particle layering and suspension govern triboelectric behaviors. Numerical results further show that strategically placing discharge points upstream of bends in the dense phase and downstream of bends in the dilute phase can substantially mitigate charge buildup. Additionally, pipe erosion intensifies with the presence of non-spherical particles. Maximum erosion occurs in deeper bend areas with non-spherical particles in the dense phase, while erosion distributions become similar in the dilute phase. A single charging cycle has a negligible impact on tribocharging-induced pipe erosion. This study offers insights into complex bend phenomena that can facilitate industrial applications.

High heat fluxes challenge electronic thermal management, as phase change materials (PCMs) suffer from low heat transfer efficiency. In this work, we present a convection enhanced strategy using molten phase transport in phase change composites (PCCs). We fabricate porous fibers with over 90 vol% interconnected core channels and a dense, conductive shell via nonsolvent induced phase separation (NIPS). Experimental and numerical analyses reveal that specific channel structures activate internal convection, contributing over 100% more than intrinsic thermal conduction. Optimized PCC fibers achieve a thermal conductivity of 1.05 W·m-1·K-1 with only 3 wt% additives, which increases to 2.48 W·m-1·K-1 upon melting, outperforming fibers with substantially higher intrinsic conductivity. Device-level demonstrations confirm the thermal buffering potential of our PCC fibers. Overall, this work highlights the vital role of thermal convection, which is often overlooked, in the design and evaluation of PCCs.

Predictive modeling of hydrocyclone flow field dynamicsenables tighter control, better separation, and lower energy use. However, the nonlinear spatiotemporal features of hydrocyclone turbulence complicate data processing and model training. Traditional single-model machine learning faces challenges with the balance between speed and accuracy. This study proposes a novel predictive model that combines a convolutional neural network (CNN) and long short-term memory network (LSTM) to forecast the dynamic evolution of the flow field. The CNN extracts spatial data's nonlinear patterns, while the LSTM captures temporal dependencies in time series data of the flow field. Comparative analysis shows that the combined CNN-LSTM model significantly enhances both the accuracy and reliability of predictions in complex flow fields, such as air core, turbulent region, and boundary layer. Compared to the CNN model and LSTM model, the CNN-LSTM model improves prediction speeds by 12.5% and 46.15%, respectively, while reducing training times by 76.34% and 12.68%. Overall, the study demonstrates the superior predictive capabilities of the CNN-LSTM model, offering valuable insights for flow field prediction in diverse industrial processes.

Hydrocyclone optimization is typically performed under fixed-condition assumptions, making its performance highly sensitive to changes in the feed particle-size distribution (PSD) and evolving process priorities. This study presents a prototype adaptive and preference-aware multi-objective optimization and control framework that adjusts inlet velocity (V) and feed solids concentration (C) in response to variations in PSD. The framework consists of four main steps: (1) Surrogate model development: A CFD-trained response-surface methodology predicts key performance objectives, including cut size (d50), separation sharpness (Ep), underflow water-split ratio (Rf), pressure drop (dP), and throughput (Q). (2) Multi-objective optimization: The NSGA-II algorithm is employed to identify Pareto-optimal trade-offs. (3) Decision-making: The TOPSIS method is used to select the optimal operating point based on user-defined weights. (4) Supervisory module: This module continuously monitors PSD and weight vectors, triggering re-optimization when predefined thresholds are exceeded, and adjusting (V, C) to align with updated priorities. PSDs are modeled using a modified Johnson-SB distribution, defined by median size (d50) and a dispersion/tail coefficient (6j, ranging from 0.40 to 1.00), where d50 determines location and 6j controls the distribution's width and tails. In 25 PSD scenarios, adaptive set-point updates resulted in a 17-27% reduction in d50, a 14-25% improvement in Ep, and a 38-95% increase in Q compared to a static baseline. Rf remained within acceptable bounds, while dP varied between 0 and 136%, depending on separation requirements. This framework provides an efficient approach for ensuring stable separation under fluctuating feed conditions and offers a practical solution for controlling hydrocyclone performance.

The packing of multi-sized wet spheres is highly intricate, shaped by the interplay of interparticle forces induced by the presence of liquid. This study presents a comprehensive and quantitative analysis of the microscopic particle arrangement within a multi-sized wet sphere packing. To achieve this, a multi-sized wet sphere packing is obtained experimentally and is then characterized by various analytical techniques, in terms of coordination number (CN), pair correlation function (PCF), topological and metric properties of the Voronoi-Delaunay tessellation. Through CN and PCF analysis, distinctive packing features such as agglomerates and particle chains are identified and characterized. Furthermore, the application of the Voronoi and Delaunay tessellation techniques uncovers the existence of heterogeneous clusters of particles in contact and non-contact states. These tessellation methods also shed light on the distorted pore structure that emerges within the packing. The insights gained from this study may serve to enhance the assessment and development of innovative simulation methods where capillary and liquid-related forces acting on wet particles with a size distribution are considered.

Hydrocyclones are widely used in industrial separation processes due to their compact design and high processing capacity. The structure of the overflow pipe plays a crucial role in determining separation performance. Although numerous studies have explored the effects of overflow pipe wall thickness on hydrocyclone performance, results remain inconsistent. This study combines numerical simulations and physical experiments to investigate the impact of the ratio of the overflow pipe's outer diameter to the cylinder section diameter (RODCD) on the performance and flow characteristics of hydrocyclones (FX75, FX25, and FX75r). The FX75 is a commonly used medium-scale model, while the FX25 is designed for finer particle separation. The FX75r, geometrically similar to the FX75 but scaled to the size of the FX25, is introduced to examine the effects of RODCD across different geometric designs. The results indicate that for the FX75, optimal separation occurs within an RODCD range of 0.48-0.64, while for the FX25, an RODCD of 0.72 improves performance by reducing pressure drop and enhancing separation efficiency. Increasing the RODCD to 0.72 stabilizes the air core, reduces turbulence, and optimizes the vortex structure, leading to improved separation efficiency and reduced energy consumption. However, at excessive RODCD values (e.g., 0.88), flow field destabilization occurs, impairing effective separation. Geometric similarity validation confirms that hydrodynamic stability at higher RODCD values is more strongly influenced by internal geometric proportions than by cylinder diameter. These findings offer valuable insights into optimizing hydrocyclone design and RODCD to enhance separation efficiency in industrial applications.

New hydrocyclone designs can significantly enhance separation efficiency in applications such as water treatment and particle classification. Therefore, various hydrocyclone geometries with different inlet and cone configurations are explored through a validated mechanistic model, leading to a new cyclone design. The proposed design features a laminar spiral inlet and a straight-to-convex cone, achieving reductions of 44.2 % in separation sharpness and 58.4 % in water split. Its double-cone configuration reduces tangential velocities in the upper conical section, while the convex lower cone broadens the separation region, maintaining relatively high tangential velocities near the spigot. These effects reduce particle accumulation near the spigot and improve separation performance. Moreover, the laminar spiral inlet mitigates short-circuit flow near the vortex finder. To further enhance separation efficiency, the novel conical section is optimized by integrating mechanistic and data-driven models. Compared to the initial novel design, the optimized version exhibits an 18.2 % reduction in separation sharpness and a 16.2 % reduction in water split by optimizing four geometric variables characterizing the conical section. Internal flow field analysis confirms that the optimized configuration establishes favorable tangential velocities in the conical section, guiding fine and coarse particles along optimal paths and improving overall performance.

This study examines particle separation in vertical pneumatic separators for lithium iron phosphate (LFP) battery recycling using a coupled computational fluid dynamics-discrete element method approach. It presents a quantitative analysis investigating the effect of drag on nonspherical particle flow behaviors in LFP recycling. The drag force acting on nonspherical particles is calculated using the Holzer-Sommerfeld drag model. Particle behavior is analyzed by varying the diameter (ds), height (hs), and aspect ratio (AR). The experiments are conducted on a lab-scale apparatus, namely, vertical pneumatic separator, distinguishing crushed LFP particles in aluminum, copper and plastics, within range of 1-10 mm. Results reveal that the superficial airflow velocity required for particle suspension increases with increasing ds and hs but exhibits diminishing growth rates. Within the designated size range, the optimal separation velocities (uopt) are identified as 1.9-2.0 m/s for plastic films (3-7 mm) and 3.3-3.5 m/s for metallic foils. Cylindrical particles exhibit AR-dependent behavior, with higher AR values reducing metal foil recovery owing to increased particle entrainment. Overall, the findings establish quantitative relationships between particle morphology and pneumatic separation parameters, offering practical guidance for optimizing LFP battery recycling through multi-parameter physical separation.

Invasive neural electrodes prepared from materials with a miniaturized geometrical size could improve the longevity of implants by reducing the chronic inflammatory response. Graphene-based microfibers with tunable porous structures have a large electrochemical surface area (ESA)/geometrical surface area (GSA) ratio that has been reported to possess low impedance and high charge injection capacity (CIC), yet control of the porous structure remains to be fully investigated. In this study, we introduce wet-spun graphene-based electrodes with pores tuned by sucrose concentrations in the coagulation bath. The electrochemical properties of thermally reduced rGO were optimized by adjusting the ratio of rGO to sucrose, resulting in significantly lower impedance, higher CIC, and higher charge storage capacity (CSC) in comparison to platinum microwires. Tensile and insertion tests confirmed that optimized electrodes had sufficient strength to ensure a 100% insertion success rate with a low angle shift, thus allowing precise implantation without the need for additional mechanical enhancement. Acute in vivo recordings from the auditory cortex found low impedance benefits from the recorded amplitude of spikes, leading to an increase in the signal-to-noise ratio (SNR). Ex vivo recordings from hippocampal brain slices demonstrate that it is possible to record and stimulate with graphene-based electrodes with good fidelity compared with conventional electrodes.

Screening is a complicated process for classifying granular materials according to size. Choking is a vital issue in screening. It may occur when the particle flow along a screen is too slow, but slow particle flow and long residence time are beneficial to sieving performance. Therefore, a model to judge whether choking happens is useful for finding optimal operating conditions. Here, a classification model to predict screen choking is proposed by combining DEM simulation and machine learning. The model can consider various key controlling variables for particle properties and operating conditions. Using the model, safe operation condition regions without choking can be identified. Then, combining the model with our previous machine learning based process model, we can design a screening process with the desired performance. The work also shows a way of using machine learning to predict critical phenomena in particle flow.

Particle shape complicates the tribocharging behavior in pneumatic conveying, and the resulting performance is complex and poorly understood. This study further develops our recent model, which combines computational fluid dynamic (CFD) and the discrete element method (DEM), to simulate particle tribocharging in horizontal pneumatic conveying of superquadric particles. Charge transfer is modeled using the condenser model, which incorporates impact and frictional charges. After validation, the CFD-DEM model is used to investigate the effect of ellipsoid aspect ratio (AR) on tribocharging, revealing the impacts of flow regime and the underlying particleparticle/wall (P-P/W) interactions on charge transfer across flat and elongated particle shapes. The results show that the specific charge increases with lower sphericities due to larger P-W contact areas. Frequent P-P/W interactions and plug formations for prolate particles lead to smooth charging curves, while induced Coulomb forces prevent oblate particles (AR = 0.455 and 0.526) from forming plugs, resulting in unsmooth charging curves. The synergic effects of tribocharging and particle shape on pressure drop, mass flow rate, and flow regime have also been unveiled. It is shown that non-spherical particle shapes lead to increased pressure drop and mass flow rate with higher fluctuations, while tribocharging decelerates particle velocity due to intensified P-W interactions caused by cohesive Coulomb force, resulting in a higher pressure drop and lower mass flow rate with reduced fluctuations. No plugs are observed for extreme oblate shapes (AR = 0.455 and 0.526) because of induced Coulomb forces, leading to decreased and stable pressure drop and mass flow rate.

The laser multiple melting strategy is commonly employed in the laser powder bed fusion (LPBF) process to reduce porosity levels and optimize mechanical properties. However, the influence of the temporal sequences of energy input has received limited attention, despite their potential to control defect generation and microstructure evolution. Therefore, in this work, two specific remelting sequences were investigated, referred to here as the preheating strategy (a low-energy first scan followed by a high-energy second scan) and the remelting (a high-energy first scan followed by a low-energy second scan) strategy. The findings indicated that defect generation and surface roughness are highly sensitive to variations in the remelting sequences, demonstrating that samples subjected to the remelting strategy exhibit significantly lower porosity levels. The simulations revealed that the defects in the preheating strategy originate from insufficient melting between layers and rough top surfaces caused by inadequate melt pool flow. Additionally, the samples subjected to the remelting strategy exhibited superior high-temperature mechanical properties, with an ultimate tensile strength of 959.7 MPa, yield strength of 792.0 MPa, and outstanding elongation of 23.1 % along the building direction after heat treatment. This enhancement was attributed to the increased geometrically necessary dislocation density induced by fine carbides measuring 0.9 mu m. This study offers valuable insights into the laser multiple melting process, providing a foundation for future research aimed at optimizing mechanical properties in the LPBF process.

Hydrocyclones are widely used in mineral processing, where inner states critically determine separation performance. However, as these states are difficult to measure directly, existing control methods cannot effectively adjust operating parameters in real time. To address this issue, this study proposes a machine learning approach to predict hydrocyclone three-dimensional inner states under varied operating conditions. The method first introduces a three-dimensional data preprocessing technique to mitigate prediction errors caused by steep data gradients. Second, a stacked ensemble learning architecture is developed to enhance prediction robustness across different physical fields. The results demonstrate that the proposed model not only preserves the resolution of inner states provided by the mechanistic model but also reliably captures the variation trends of critical variables. Specifically, the model improves inner state prediction accuracy, reducing the mean squared error of the pressure field from 82,300 to 58,300. For performance indicators, the predicted percentage errors for pressure drop, partition curve, and water split are all within 5%. Crucially, its computational efficiency dramatically surpasses mechanistic model. A full three-dimensional physical field prediction takes approximately 10 s, compared to nearly 50 h for a conventional numerical simulation. This model offers an effective solution for realtime prediction of hydrocyclone three-dimensional inner states, holding significant potential to enhance process control precision and further separation efficiency.

The separation space of hydrocyclone, including its cylindrical and conical sections, governs internal fluid dynamics and significantly affects classification performance. While the individual effects of these two sections are well-studied, the effects of cylinder-to-cone ratio (CCR) remain insufficiently explored. This study utilizes numerical simulations to assess the effects of different CCRs on hydrocyclone performance metrics, including classification performance, flow field characteristics, and volume fraction distributions across seven CCR configurations. The results show that as CCR increases from 1:9 to 9:1, the cut size increases from 16.4 mu m to 30.4 mu m, Ecart probable increases from 6.1 mu m to 9.5 mu m, the pressure drop decreases by 11 kPa, and the water split drops from 5.8% to 3.7%. Additionally, a smaller CCR enhances tangential velocity and pressure gradient, improves particle classification, stabilizes the air core, and reduces particle misplacement. These findings offer valuable insights into optimizing hydrocyclone design and classification performance to meet diverse application needs.

Metastability, disorder and jamming are the typical characteristics of amorphous systems, while the related structure changes remain unclear. Sphere packing is often used as a structure model for amorphous and crystalline states. In this article, sphere packing systems with packing densities ranging from 0.50 to 0.74 were simulated by using Discrete Element Method (DEM), and the obtained packing structures were assessed to investigate the densification process and jamming properties. An order parameter that can effectively distinguish the order and disorder of packing structures was proposed based on the distribution characteristics of jamming angles. Then the evolution of jamming characteristics during the transition from Random Loose Packing (RLP) to Random Close Packing (RCP) and the jamming-jamming relations of different packing structures were demonstrated. On this basis, a correlation between order-jamming-metastable states from the microscopic structural perspective was established, which is of valuable theoretical and practical implications for the characterization and synthesis of crystalline and amorphous materials. (c) 2025 Chinese Society of Particuology and Institute of Process Engineering, Chinese Academy of Sciences. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Battery recycling is crucial due to its environmental and economic benefits, with efficient separation of the crushed battery mixture forming the basis of the process. However, numerical studies on this process remain limited primarily due to the highly non-spherical shapes of the particles involved. Spherical particles are commonly used in limited numerical investigations with compromised accuracy and reliability. In this study, the separation of crushed battery mixture by vertical pneumatic separator is simulated using the coupled method of computational fluid dynamics (CFD) and discrete element method (DEM). The non-spherical particles in the crushed battery mixture is resembled by cylindrical film-like particles, and the Holzer and Sommerfeld drag coefficient is applied to enhance the accuracy of the drag force calculation. After model validation, simulations are conducted to evaluate the effects of the airflow velocity, separation zone height, and initial particle velocity on the separation efficiency, followed by parameter optimization. The results indicate that the optimal separation airflow velocity is 2.0 m/s for plastic films and 3.4 m/s for anode copper and cathode aluminum foils. The ideal separation zone height is 300-350 mm for plastic films, and 300 mm optimal for anode copper and cathode aluminum foils. Additionally, a slight increase in initial velocity improves plastic film separation, whereas maintaining 0.2 m/s ensures effective separation of anode copper and cathode aluminum foils. Optimizing such parameters enhances the efficiency of the separation process, improves operating strategies, reduces material losses, and increases the overall effectiveness of battery recycling for sustainability and economic gains.