Shear-induced particle migration in the internal die flows of concentrated particulate suspensions during slot coating processes was investigated using a diffusive flux model implemented within three-dimensional computational fluid dynamics simulations. Comparative simulations demonstrated that particle migration markedly altered the internal flow characteristics and particle distributions inside the slot dies. Inhomogeneous particle distributions at the feed inlet were found to modify the flow behavior in the chamber region and profoundly affect the velocity and particle concentration fields in the slit region. These changes critically influenced the die exit velocity and concentration profiles, which governed the uniformity of the wet coating thickness. The insights obtained from this study offer practical guidance for die design and process control to achieve a uniform coating thickness and stable slot coating operation.

To enhance the calibration efficiency and accuracy of Discrete Element Method (DEM) parameters for cohesive bulk materials, a collaborative method integrating Particle Swarm Optimization (PSO) and Backpropagation (BP) neural networks is proposed. Key macroscopic indicators (steady-state shear stress, angle of repose) are obtained via Jenike shear and funnel tests across a 0-50% moisture range. Orthogonal experiments determine micro-parameters (e.g., static/rolling friction, surface energy) to build a macro-micro mapping database. The core of the PSO-BP dual-model lies in its collaborative mechanism: the forward BP model predicts macroscopic responses to replace time-consuming DEM simulations, while the PSO algorithm optimizes the inverse BP model to accurately infer optimal micro-parameters from experimental macro-indicators (steady-state shear stress, angle of repose). Validation shows low errors (1.14% for angle of repose, 1.63% for steady-state shear stress) and good chute flow velocity agreement. This method overcomes traditional limitations of arbitrariness and ignored parameter coupling, providing reliable support for DEM simulation and equipment design for cohesive bulk materials.

Hydrogel desorption shows promise for thermal management and passive cooling engineering applications, but challenges persist in understanding particle desorption and shrinkage. A Lattice Boltzmann-based phase-field model was developed by combining experiments and mesoscopic simulations to simulate conjugate heat transfer, desorption kinetics, and shrinkage of single hydrogel particles, achieving <5% deviation from experiments. Results show that raising temperature from 343 to 353 K increases 1-h desorption mass by ~12% of initial mass (vs. 1.3% for 313-323 K), driven by rising saturation vapor pressure and falling evaporation enthalpy. Shrinkage is more sensitive at high temperature and low humidity, with surface area reductions of ~17% (343-353 K) and~13% (20-30% RH), compared to ~5% under mild conditions. Reducing particle initial mass from 5.75 g to 0.16 g shortens the time to release 50% of the initial water from 516 min to 117 min, indicating greater efficiency for smaller particles. This work bridges the macro-pore scale gap and offers a numerical tool with design insights for passive cooling.

This manuscript mainly proposed an effective method to well disperse the composite conductive agent which is composed of carbon nanotubes (CNTs) and graphene (Gr) in lithium-ion battery (LIB) slurry. Electrochemical Impedance Spectroscopy (EIS), Scanning Electron Microscopy (SEM) and gravitational sedimentation (GS) are employed to characterize the electrochemical, morphological and stability characterizations of LIB slurry, respectively. Specifically, electrochemical characterizations of LIB electrode slurries are performed by fitting Nyquist plots with a 10-parameter EEC, and quantitative morphological analysis of SEM images is conducted using a Mask R-CNN instance segmentation algorithm, both of which were proposed in our prior published research works. Consequently, the dispersion characterizations of LIB slurry are able to be summarized as follows: LiCoO₂ particles are well dispersed in LIB slurry at φ_com2 = 0.5%, by contrast, the composite conductive agent achieves superior coating and networking of LiCoO₂ particles under the conditions of both φ_com2 = 0.5% and m_CNTs:m_Gr = 4:1, due to the maximized CNTs-Gr synergistic effect. Meanwhile, the formed three-dimensional "long-range" conductive network maintains the stability of its internal skeleton structure during the sedimentation of LIB slurry. This finding holds significant potential to advance the application of CNTs/Gr composite conductive agents in LIB slurry.

Polycaprolactone (PCL) microspheres are emerging as versatile biomaterials for minimally invasive soft tissue augmentation due to their tunable biodegradability and favorable biocompatibility. Nevertheless, the extent to which monodispersity governs the functional performance of PCL microspheres, particularly in modulating host tissue responses and regenerative efficacy, remains poorly understood. Herein, we propose a method to controllably prepare monodisperse oil-in-water (O/W) droplet templates using coaxial flow-focusing microfluidics, and then obtaining monodisperse PCL microspheres with programmable sizes via solvent evaporation. The droplet dimensions are inversely regulated by the outer-to-inner phase flow rate ratio, while PCL concentration in the organic phase exerts minimal influence on initial droplet size but markedly reduces the volume shrinkage during solidification, yielding microspheres that better retain their geometries. Notably, the resultant PCL microspheres can maintain structural integrity and size uniformity over two-month period under physiologically mimetic conditions. Comprehensive biocompatibility assessments reveal that PCL microspheres exhibit negligible hemolytic activity and cytotoxicity, demonstrating excellent hemocompatibility and cytocompatibility. Notably, in a rabbit soft tissue implantation model, monodisperse PCL microspheres with an average diameter of 42 μm elicit attenuated foreign body reactions and potentiate endogenous collagen deposition relative to the polydisperse microsphere control group. These results provide useful guidance for the application of PCL microspheres in soft tissue augmentation.

Supercritical water gasification (SCWG) is a highly promising technology. A fundamental aspect of SCWG involves the flow of supercritical water (SCW) around interactive particles, which is inherently complex due to the presence of the wake effect. This study numerically investigates particle wake characteristics and wake-particle interactions in high-viscosity supercritical water (SCW) via an adaptive lattice Boltzmann method (LBM, N/D = 30, coarse-fine ratio 0.025:0.060) to support supercritical water gasification (SCWG) reactor optimization. The adaptive LBM effectively balances accuracy and efficiency, resolving SCW's steep viscosity gradients and fine wake structures well. Interparticle distance (L/D) is the dominant factor for particle drag, affecting trailing particles far more significantly, with three interaction regimes (strong: L/D = 0-2, moderate: 2-4, weak: ≥4). SCW's high viscosity amplifies wake overlap at L/D ≤ 2, minimizing trailing particle pressure drag and suppressing vortex shedding; increasing L/D weakens shielding, elevates drag, and makes trailing particles behave like isolated ones. Interparticle angle raises drag ratios, inducing distinct vortex structures at 30°-60° and 60°-90°, with identical drag at 90°. SCW wake symmetry and vortex shedding show Re-dependent transitions, with critical Re = 92 corresponding to the minimum trailing particle drag ratio. A drag ratio correlation with L/D and Re is also established. This work provides a reliable numerical tool for SCW particle interactions and theoretical guidance for SCWG reactor optimization, with future work focusing on particle swarms and experimental validation.

Thermal energy storage (TES) is a key enabler for the large-scale utilization of intermittent renewable energy sources like solar and wind power, and it also offers an efficient pathway for industrial electrification and decarbonization. However, the widespread adoption of TES is hindered by the high cost of conventional TES materials, such as alumina, magnesium oxide, and other ceramics. This study investigates the feasibility of converting coal gangue, an abundant industrial waste with considerable environmental impact, into a low-cost, mechanically stable, and thermally efficient material for medium-to high-temperature sensible heat storage applications. Three representative coal gangue samples from the Ordos region in Inner Mongolia, China, were calcined at temperatures ranging from 1000 to 1300 ℃ to optimize their phase compositions, mechanical strengths, and thermophysical properties. Among the samples, Sample A from Jungar exhibited the best overall performance, achieving a volumetric heat storage density of approximately 555 kWh m⁻³ at 1200 ℃, which is comparable to typical sensible heat storage materials, while maintaining good mechanical integrity and satisfactory cyclic stability. With near-zero raw-material cost, a simple single-step preparation process, and potential policy incentives for solid-waste utilization, the coal-gangue-based composites developed here offer clear economic and environmental advantages and introduce a promising, low-cost material system for sustainable TES applications at medium-to high-temperature levels.

Numerical simulations were conducted to study the pyrolysis of polypropylene (PP) in a fluidized bed reactor (FBR). For that purpose, a Eulerian-Lagrangian solver was developed, incorporating the gas-solid hydrodynamics in FBR, particle-level heat transfer, and a five-lump pyrolysis reaction kinetic model. This framework captures the mutual interplay among these physicochemical processes and enables predicting the yields of permanent gas (G), light fraction (LF), and heavy fraction (HF). Analysis of the characteristic timescales confirms that the pyrolysis reaction is significantly slower than convective heat transfer. At 505 ℃, LF was the dominant product (67.4 wt%), followed by G (29.6 wt%) and HF (3 wt%), and the product distribution significantly shifted toward G formation with increasing reactor temperature. In contrast, variations in particle size (1.5-2.5 mm) and operation mode (batch-wise vs. continuous) affected the transient thermal behavior but had minor effects on product yields, as heat transfer is not rate-determining under the investigated conditions.

High-capacity silicon anodes hold great promise for safe and energy-dense all-solid-state lithium batteries (ASSLBs), yet their practical application is hindered by interfacial degradation and mechanical fracture, which severely limit their cycle life. Herein, we unravel the particle-size-dependent electro-chemo-mechanical failure mechanisms of Si anodes in sulfide-based ASSLBs. The micro-sized Si (μm-Si) anode exhibits favorable initial Coulombic efficiency (ICE, 79.15%) and reversible capacity (2260.5 mAh g⁻¹) but succumbs to progressive particle fracture under prolonged cycling due to cumulative mechanical stress from large volume swings. By contrast, the nano-sized Si (nm-Si) anode suffers from severe interfacial side reactions and irreversible volume expansion due to its larger specific area and dense electrode structure, resulting in lower initial performance (ICE of 72.21%, 1372.7 mAh g⁻¹). In subsequent cycles, the nm-Si anode experiences continuous interfacial side reactions, leading to substantial accumulation of interfacial decomposition byproducts and sustained capacity decay. These contrasting failure pathways establish electro-chemo-mechanical coupling as the governing principle and provide a particle-size-dependent design framework for high-performance Si-based ASSLBs.

Sinkholes, a common geotechnical failure, have been reported more frequently in recent years due to complex climate change and urbanisation. This study investigates mechanisms of sinkhole development, considering the influence of triggering depth and compaction degree through experiments integrated with Particle Image Velocimetry (PIV) technique and Discrete Element Method (DEM). Laboratory tests simulating the formation of sinkhole in subgrade soil are carried out under varying triggering depths and void ratios, where the soil displacement is recorded over time. DEM simulations are performed and validated against the experimental results analysed by PIV technique. The results show good agreement between DEM and experimental investigations, especially in capturing time-dependent stages and displacement fields of sinkhole development. Furthermore, the numerical investigation indicates that increasing the triggering depth results in later development of sinkhole with greater vertical displacement around the triggering point to promote its propagation towards the ground surface. Interestingly, dense soil exhibits highly localised mobilisation confined within well-defined displacement boundaries, whereas loose soil displays a wider displacement zone characterised by a funnel-shaped pattern. The interparticle contact behaviour further releases the insightful mechanism of sinkhole progression, giving considerable value to our understanding and prediction of this catastrophic failure.