Concentrated suspensions of very high phase fractions (>10%) significantly complicate hydrodynamic characteristics in multiphase reactors. The inline image method proposed recently provides the possibility to peer into dense particle swarm dynamics, previously considered an impossible mission. In this work, the method was further developed to determine the particle-resolved flow field and comprehensive datasets of particles within a swarm. Transient swarm microstructure demonstrated two aggregation states, that is, doublets and multiplets, accompanied by frequent collisions and friction. Statistical analysis indicated the damping effect on slip velocity and net force induced by the particle swarm became significant as solid holdup reached 13.2%, which markedly enhanced particle suspension. Through correlation analysis of dynamic datasets and relevant mechanisms, the viscous effect and hindrance effect exerted by the particle swarm were quantitatively elucidated for the first time. Accordingly, a correlation was proposed to predict the swarm effect on axial slip velocity, and good agreement was demonstrated across wide concentration ranges.

Liquid bridging cylinders are ubiquitous in nature and industrial processes. The morphology and capillary force of these bridges between cylinders are influenced by several factors, including inter-particle spacing, cylinder diameter, liquid volume and wettability. This study combines experimental and Surface Evolver (SE) simulations to systematically investigate the effects of these factors on bridge morphology and capillary force. The results indicate that capillary bridge force decreases as particle spacing increases. Conversely, increasing liquid volume enhances capillary bridge force and induces a reverse morphological transition. For cylinder diameters between 1 and 6 mm, the capillary force increases with increasing particle size. Based on experimental and numerical results, we propose a nonlinear regression model for accurate prediction of capillary force. It exhibits greater generality in predicting capillary bridge forces compared to the Princen model. These findings offer valuable theoretical insights for controlling liquid bridges in relevant engineering and industrial applications.

Considering the intrinsic difficulty of accurately determining the thermodynamic data of complex impurity‐containing systems in industrial crystallization processes, this study creatively establishes a predictive correlation between infrared spectra, operating conditions, and the thermodynamics of the racemic methionine ( dl ‐methionine) crystallization system in K 2 CO 3 aqueous systems. Using principal component analysis, random forest, and Gaussian process regression architecture, a concentration prediction machine learning sub‐model was constructed, yielding a coefficient of determination of 0.988 in the test set. Subsequently, a first‐principle sub‐model combining equilibrium conditions with the electrolyte activity coefficient model was established by developing a parameter optimization program fitted experimental data to obtain a predicting error of 2.09%. The solubilization effect of carbonate salt on methionine was quantitatively characterized. The influence of temperature, pH, and carbonate component on the distribution and activity coefficients of dl ‐methionine species was thoroughly investigated. The hybrid model can better serve for the analysis of industrial dl ‐methionine crystallization processes.

Ultrafine microdroplets are popular in many liquid-liquid heterogeneous systems. This study systematically examined droplet generation in an annular ejector at low-pressure of 0.05 MPa to address the structural complexity and high energy consumption associated with traditional liquid-liquid dispersion devices. The findings reveal that the Sauter mean diameter (d32) demonstrates a non-monotonic relationship with the continuous phase Reynolds number (Re), while it increases monotonically with the dispersed phase volume ratio (phi). A dynamic equilibrium was attained after a specific circulation period, resulting in a stabilized d32 2.0 & micro;m. The optimal structural parameters were determined as follows: A mixing chamber length (L = 80 mm) and diameter (D = 7.5 mm) yielding a d32 of 2.25 & micro;m, and a diffusion section angle of 30 degrees producing a d32 of 2.4 & micro;m. Therefore, this work provides empirical insights for optimizing annular ejectors, offering guidance for the development of efficient microdroplet production device.

Secondary nucleation critically governs crystal size distribution (CSD), yet its precise control remains challenging. Here, in situ process analytical technologies (PAT) were used to investigate the secondary nucleation of glyphosate. A method based on a defined nucleation core-interval was established to determine the average secondary nucleation rate, enabling quantitative assessment of seed size, seed loading, seed shape, cooling rate, and agitation rate. To clarify morphology effects, an additive-free approach combining isoelectric-point and cooling crystallization was developed to prepare spheroidized seeds. Under identical size conditions, spheroidized seeds markedly suppressed secondary nucleation relative to prismatic seeds, yielding larger and narrower crystals. Thus, a seed-shape factor was incorporated into the averaged secondary nucleation model, improving performance (R 2: 0.54 to 0.95; ARD: 49.94% to 18.51%) and yielding more physically consistent exponents. These results highlight seed shape as a key regulator of secondary nucleation kinetics and offer an effective pathway for tailoring CSD.

The nano barium titanate (BaTiO3) prepared by the traditional batch method faces some challenges, including inhomogeneous mixing and imprecise supersaturation control, resulting in a wide particle-size distribution. The tetragonal nano-BaTiO3 with a narrow size distribution was obtained using a continuous, stable preparation system combined with a micro-droplet generator via the oxalate co-precipitation method. Titanium tetrachloride and barium chloride were used as sources of titanium and barium, respectively, with varying concentrations of titanium tetrachloride tested. The optimal reactive conditions were determined to be a TiCl4 solution concentration of 3.0 mol/L, pH 2.50, and a feeding rate of 20 mL/min, followed by aging at 80 °C for 2 h. The synthesized BaTiO3 has an average particle size of 85 nm (Span = 0.64) and a c/a ratio of 1.0077. Additionally, the optimized post-processing calcination condition was conducted at 975 °C for 2 h. The obtained nano-BaTiO3 (88 nm) exhibited the highest c/a ratio (1.0082) and the narrowest particle-size distribution (Span = 0.62). This indicates that the prepared nano-BaTiO3 exhibits high ferroelectric performance, as its c/a ratio exceeds 1.0080. Some advances in the synthesis approach for developing nano-barium titanate powders with a high dielectric constant are provided by this work.

Dimethylphenols serve as important intermediates in synthesizing pharmaceuticals and agrochemicals, yet traditional distillation struggles to separate their isomers due to minimal boiling point differences, and the development of melt crystallization is hampered by lacking solid-liquid equilibrium (SLE) data for some isomers. Therefore, the SLE data of both binary and ternary mixtures of 2,3-dimethylphenol (2,3-DMP), 3,5-dimethylphenol (3,5-DMP), and 3,4-dimethylphenol (3,4-DMP) were determined by using differential scanning calorimetry in this work. Additionally, crystallographic analysis was conducted to investigate the thermodynamic characteristics of these mixtures. The experimental results indicated that all the systems investigated in this research exhibited eutectic behavior. The experimentally obtained SLE data were well correlated with the Wilson and non-random two-liquid models. The excess thermodynamic functions were calculated to analyze the types and intensities of the molecular interactions occurring in the mixtures. Furthermore, this study developed a model for the correlation between the theoretical crystallization yield and the actual cooling yield and final yield in melt crystallization. This study has furnished reliable data essential for developing and optimizing the melt crystallization process of mixtures of 2,3-DMP, 3,5-DMP, and 3,4-DMP. (c) 2025 The Chemical Industry and Engineering Society of China, and Chemical Industry Press Co., Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Considering the intrinsic difficulty of accurately determining the thermodynamic data of complex impurity-containing systems in industrial crystallization processes, this study creatively establishes a predictive correlation between infrared spectra, operating conditions, and the thermodynamics of the racemic methionine (dl-methionine) crystallization system in K2CO3 aqueous systems. Using principal component analysis, random forest, and Gaussian process regression architecture, a concentration prediction machine learning sub-model was constructed, yielding a coefficient of determination of 0.988 in the test set. Subsequently, a first-principle sub-model combining equilibrium conditions with the electrolyte activity coefficient model was established by developing a parameter optimization program fitted experimental data to obtain a predicting error of 2.09%. The solubilization effect of carbonate salt on methionine was quantitatively characterized. The influence of temperature, pH, and carbonate component on the distribution and activity coefficients of dl-methionine species was thoroughly investigated. The hybrid model can better serve for the analysis of industrial dl-methionine crystallization processes.

Achieving efficient and homogeneous mixing in highly concentrated solid-liquid systems remains a major challenge, since existing simulation methods cannot accurately capture the dynamic continuous mixing, thus failing in effective prediction and optimization. In this work, an Euler-Euler model coupled with the dynamic mesh technology and kinetic theory of granular flow was developed to simulate the transient continuous mixing of dense solid-liquid suspension. The liquid shear stress, dispersive mixing, distributive mixing and dispersivedistributive coupling mixing were resorted to elucidate the dense solid-liquid mixing mechanism. On this basis, novel screw elements were designed and associated mixing behaviors were comprehensively investigated. The results demonstrate that the novel simulation method provides high accuracy in predicting the mixing performance. Compared with the conventional K45/5/32 kneading element, the axial kneading slot element had increased the non-uniformity of tensile strength and shear strength by 16.5% and 64.7%, respectively, leading to a 48.7% improvement in the mixing uniformity of the dense solid-liquid system. Moreover, the axial kneading slot element increases the probability density of liquid shear stress in the range of 610-1300 Pa from 0.41 in K45/5/32 element to 0.56, increasing moderate shear stress zones by 15% while maintaining the high liquid shear stress region (>4200 Pa) to disrupt agglomerated particles.

This study systematically investigates the morphological evolution and adhesion mechanics behavior of capillary bridges between rough surfaces by combining precise force measurements with high-speed image morphological characterization. The investigation begins by measuring the advancing and receding contact angles of droplets on surfaces with varying roughness, the results show that rough surfaces promote apparent hydrophilicity and contact angle hysteresis, which is consistent with the Wenzel-like wetting tendency. The effects of separation distance and liquid volume on the quasi-static characteristics of the capillary bridges were then investigated. Experimental results reveal a pronounced dual effect of surface roughness. At small separation distances or larger liquid volumes, the Wenzel-like wetting tendency induced by rough topography reduces the apparent contact angle and enhances interfacial curvature, thereby generating greater total capillary forces. Conversely, at large separation distance, the strong pinning effect caused by rough topography impedes contact line retraction. This induces severe necking, leading to instability and rupture at reduced critical distances. A generalized empirical scaling law incorporating liquid volume, contact angle and asymmetric surface roughness was developed to predict rupture distance, achieving a high goodness of fit (R2 = 0.977). Following rupture, the liquid preferentially redistributes toward rougher surfaces, indicating that roughness induced wetting enhancement and pinning strongly affect the post-rupture liquid distribution.

When measuring the dynamic characteristics of bubbles using image-based methods, the images including complex overlapping of bubbles with irregular shape pose a huge challenge to current bubble reconstruction algorithms. Based on the Mask R-CNN architecture, a multitask model "Bubble Boundary R-CNN" is proposed for detection, segmentation and shape reconstruction of overlapping bubbles with high deformation at high gas velocities. By leveraging shared deep features from detection and segmentation, model achieves flexible bubble reconstruction and accurate size distribution extraction from high gas velocity bubble flows. To overcome the limitations of Mask R-CNN and address the challenges posed by high-deformation and overlapping bubbles, a semantic segmentation auxiliary head and the Boundary-preserving Mask Head (BMask Head) are introduced to enhance edge information and extract bubble boundary features. Additionally, an Overlapping Boundary Prediction Network (OBPN) is proposed to reconstruct boundary features that are lost due to bubble occlusion and predict the complete bubble shapes. The optimized model shows its ability to flexibly and accurately reconstruct overlapped and deformed bubbles by varying occlusion rates and circularity. The new model has also proven its reliability when applied to bubble images from a bubble column at gas velocities of up to 0.067 m3/s. In future work, more efforts are needed to further enhance the detection capability of the model and reduce missed and multiple detections at high gas velocity.

Phenethyl isothiocyanate (PEITC) derived from cruciferous vegetables has shown anticancer activities by modulating apoptosis, cell cycle arrest, drug-metabolizing enzymes and even preferentially restoring a ‘WT-like’ conformation to p53R175H. But its molecular anti-cancer mechanisms are not well understood. Evidence shows that switching YAP-binding partners from pro-tumorigenic to pro-apoptotic proteins might hold great potential for the treatment of human cancers harboring mtp53. In this study we investigated the impact of PEITC on mtp53-YAP-p73 interaction in cancers harboring a variety of p53 mutants, but not limited to structural mutations. We showed that breast cancer, colorectal and lung cancer cells harboring mtp53 (p53R280K, p53R273H) were more sensitive to PEITC than those cells harboring wtp53. We demonstrated that PEITC bound to YAP at its WW binding domain, and induced a conformational change, facilitated the dissociation of YAP-mtp53 complex and inhibited their pro-proliferative transcriptional activity in different cancer cells harboring mtp53. Concomitantly, PEITC acted as a molecular glue to enhance the association of YAP-p73 complex and induced apoptosis. These results provide insights into the anticancer activity of PEITC against a wide spectrum of cancers and highlight a unique mode of action for PEITC-based cancer therapy.

The characterization of flow regime transitions in gas-liquid stirred tanks is important for process optimization. Conventional studies, however, have primarily characterized steady-state flow regimes, overlooking the evolution of gas-phase polydispersity that significantly influences the transition dynamics. This work addresses this gap by introducing a deep-learning-based polydisperse particle detection system for efficient size-resolved bubble characterization. The results reveal that regime transitions are mechanistically controlled by a hydrodynamic decoupling between bubble classes, with large-bubble dynamics playing a key role. The flooding-to-loading transition is characterized by the radial dispersion of large bubbles, marked by a sharp surge in their local occurrence frequency, with its growth rate increasing more than fivefold. The transition to full recirculation coincides with axial entrainment of large bubbles below the impeller, causing a more than threefold increase in local gas holdup. This study establishes a mechanistically grounded framework linking microscopic bubble phenomena to macroscopic flow pattern transitions.

The purity of electronic-grade chemicals significantly impacts electronic components. Although crystallization has been used to purify cerium ammonium nitrate (CAN), the impurity removal mechanism underlying different crystallization parameters remains unclear. Traditional analytical methods of inductively coupled plasma mass spectrometry (ICP-MS) have problems in detecting trace Fe accurately, because of the high concentration of Ce and interference of polyatomic ions. Therefore, this study developed a new method integrating the standard addition and internal standard methods and explored the role of the kinetic energy discrimination mode. This new approach effectively overcomes Ce-related matrix interference and fills the gap in ultra-trace impurity detection. Furthermore, the study investigated the effects of cooling rate, seed mass loading and seed size on the removal of Fe impurity. The seed mass loading affects the average crystal size through regulating secondary nucleation and crystal growth. The removal of Fe in CAN is determined by surface adsorption and agglomeration. Under the condition of the cooling rate of 0.2 K center dot min-1, and addition of 0.5% (mass) 600-680 mm seeds, the Fe content is the lowest, at only 0.24 mg center dot L-1, and the Fe removal rate reaches 92.28%. (c) 2025 The Chemical Industry and Engineering Society of China, and Chemical Industry Press Co., Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

The formation of liquid bridge morphology is dominated by three key parameters, namely liquid volume, separation distance, and wettability, which synergistically modulate capillary bridge force. To unravel the underlying mechanisms, we performed experimental and computational studies using water and ethylene glycol across liquid volumes (0.2-10 mu L) and separation distances (0-2.5 mm). Our results indicate that liquid bridge morphology deviates from "concave" and transitions into "column" or "convex" for small separation distance (S* < 0.1) and large liquid volume (V* > 0.1). This morphological evolution introduces significant apparent contact angle variations. A nonlinear regression model was proposed to correct the apparent contact angle theta by geometric parameters of liquid bridge. This correction enhances the consistency between Surface Evolver simulations, theoretical predictions, and experimental data, achieving agreement within 10 % error. Finally, a regime map was constructed to classify liquid bridge morphologies based on S* and V*, providing a practical guide for selecting appropriate capillary force calculation.

OpenFOAM (Open Field Operation and Manipulation) is an open-source computational fluid dynamics (CFD) platform with significant extensibility, offering promise for the design and scale-up of multiphase reactors. When considering polydisperse multiphase flow, the source terms of the discrete population balance equation (PBE), associated with local turbulent energy dissipation and involving complex integrations, can significantly increase the computational demands on CFD-PBE coupled simulations. Consequently, only a limited number of studies have employed OpenFOAM for gas-liquid stirred tanks. This work develops a comprehensive CFD-PBE computational framework to enable efficient and stable simulations of such systems. By discretizing the turbulent flow fields, field-dependent coalescence and breakage rates, along with the daughter bubble size distribution, are pre-calculated and tabulated. These pre-calculated results are then utilized in the CFD-PBE simulations through a simple "database lookup" process and interpolation to assemble the source terms of the discrete PBE. This decoupling of the computation of source terms from the CFD-PBE simulation significantly enhances computational efficiency while maintaining accuracy. Additionally, this study employs degassing boundary conditions and specialized hexahedral mesh refinement techniques to further enhance computational speed and stability. Comparative analysis with existing PBE treatments shows that this innovative pre-calculation and interpolation approach achieves comparable precision with a nearly 35-fold increase in computational speed. Moreover, simulation results have been validated against a comprehensive experimental dataset obtained from the inline image method and literature, confirming the accuracy and generalizability of the proposed computational framework. This framework facilitates more precise and cost-effective simulations of industrial applications.

The solubility of polymorphic glycine in mixed solutions was determined, and the dissolution behavior of glycine was investigated by molecular simulation.

Ulcerative colitis (UC) is a chronic inflammatory bowel disease. The etiology of UC is multifaceted, and the underlying pathogenesis remains incompletely understood. Pyroptosis, programmed cell death mediated by the gasdermins, is a pivotal driver of UC pathology due to its dual role in epithelial barrier disruption and inflammatory amplification. We previously showed that phenethyl isothiocyanate (PEITC), an isothiocyanate derived from cruciferous vegetables, alleviated acute liver injury in mice by suppressing hepatocyte pyroptosis. In this study we evaluated the therapeutic potential of PEITC in the treatment of UC and the underlying mechanisms. UC mouse models were established by administration of 2.5

Static layer melt crystallization is an important separation and purification technique used in many fields, but it generally has a low growth rate of the crystal layer. This work aims to simulate the static layer melt crystallization process in a crystallizer with an inner cooling tube, and probe into the design and scale-up of the crystallizer from the perspective of production efficiency. Heat transfer by conduction and convection are coupled together with phase transition and fluid flow to establish a comprehensive model of static layer melt crystallization. CFD simulations were performed to analyze the influence of the size, structure and wall material of the crystallizer. The inner diameters of the crystallizer and the inner cooling tube should be designed to optimize the crystallization yield. The industrial-scale static layer crystallizer can be designed into multiple regions that are spaced in an optimized distance, and thinner polytetrafluoroethylene walls should be favored.

Continuous crystallization has recently attracted extensive attention in both industry and academia. In this work, a continuous MSMPR (mixed-suspension mixed-product removal) crystallization apparatus was established, and continuous antisolvent crystallization experiments of the L-histidine-water-ethanol system were first conducted. The effects of various process parameters, such as the feed concentration, the stirring rate, and the mean residence time, on the crystal form and volume-based mean crystal size of the product were investigated. Within the explored experimental conditions, changes in the feed concentration or the stirring speed can influence the crystal size but do not affect the crystal form of the product. However, modifying the mean residence time can affect the crystalline form of the product. These findings indicate that it is possible to control the crystal form and volume-based mean size of L-histidine by adjusting the process parameters of continuous MSMPR crystallization. Finally, the continuous crystallization kinetics of form B was studied, and it was found that the growth kinetics of form B was size-dependent, and the MJ3 model was more suitable. The total nucleation and growth kinetics of form B were also fitted in this study. The results will provide some meaningful guidance on the control of polymorph and size in continuous crystallization, which is a topic of great concern in the pharmaceutical industry.