Lei Gao

To address the core industrial bottlenecks of conventional batch tannic acid-based germanium precipitation processes—high reagent consumption, long reaction cycles of several hours, severe impurity co-precipitation as well as the common mismatch between single-channel microreactor throughput and industrial production demands. This work combines numerical simulation with experimental validation to investigate microscale two-phase flow regulation, high-throughput microreactor optimization, and tannic acid precipitation intensification. Two-dimensional two-phase flow models are established for straight and zigzag microchannels, with the level set method applied to track interfacial evolution. The regulatory effects of inlet velocity and channel geometry on flow patterns, droplet behavior and mixing performance are clarified. Zigzag channels induce chaotic convection via periodic corners, achieving an order-of-magnitude improvement in mixing efficiency at low Reynolds numbers (Re < 400), which lays a fundamental basis for reaction intensification. Taking zigzag channels as core units, a bidirectional symmetric superposition scale-up strategy is proposed to break the throughput limitation of single-channel systems, and a 3D-printed high-throughput microreactor integrating 78 parallel zigzag channels is designed. 3D simulations reveal a three-stage mixing mechanism and uniform flow distribution among parallel channels, with total throughput two orders of magnitude higher than a single channel. Single-channel experiments with industrial germanium-bearing raffinate yield 91.81% precipitation efficiency under optimal conditions, reducing the reaction residence time from hours in conventional batch processes to the second scale. Staged reagent addition and two-stage serial configuration further raise the efficiency to ~98%, realizing deep germanium recovery with significantly improved reagent utilization and reduced impurity co-precipitation. This process achieves efficient intensification of the chelation precipitation process while balancing throughput and mixing performance, providing a novel and technically feasible approach for efficient low-consumption germanium recovery, and offering solid technical support for the industrial application of microreactors in the hydrometallurgy field.

Platinum group metals (PGMs)—comprising platinum (Pt), palladium (Pd), rhodium (Rh), iridium (Ir), ruthenium (Ru), and osmium (Os)—are indispensable strategic materials for key industries, including automotive manufacturing, petrochemical engineering, and the new energy sector. Given the uneven global distribution of primary PGM reserves and the widening supply–demand gap, recovering PGMs from secondary sources—primarily metallurgical by-products and spent catalysts—has become a strategic priority. synergistic smelting, leveraging “multi-feedstock complementarity” and “multi-technology coupling,” offers an efficient approach to overcoming challenges associated with secondary resources, such as low grades, complex matrices, and refractory separation. This paper systematically reviews the technological evolution of synergistic smelting for PGMs recovery, focusing on three aspects: the characteristics and processing bottlenecks of PGMs-bearing secondary resources, the development trajectory of traditional metallurgical technologies, and innovative breakthroughs in microwave-assisted synergistic smelting. A comparative analysis between traditional and microwave-based technologies is conducted across four dimensions: resource adaptability, technical performance, environmental sustainability, and industrial maturity. Finally, the core challenges currently confronting microwave-assisted synergistic smelting and future directions for industrial demonstration are elaborated on. This study serves as a comprehensive reference for the efficient and sustainable recovery of PGMs, with significant implications for the circular economy and strategic resource security.

During electron beam cold hearth melting (EBCHM) of Ti-6wt%Al-4wt%V titanium alloy, aluminum volatilization causes compositional segregation in the ingot, significantly degrading material performance. Traditional methods (e.g., the Langmuir equation) struggle to accurately predict aluminum diffusion and compensation behaviors, while computational fluid dynamics (CFD), although capable of resolving multiphysics fields in the molten pool, suffer from high computational costs and insufficient research on segregation control. To address these issues, this study proposes a CFD-machine learning (backpropagation neural network, CFD-ML(BP)) approach to achieve precise prediction and optimization of aluminum segregation. First, CFD simulations are performed to obtain the molten pool’s temperature field, flow field, and aluminum concentration distribution, with model reliability validated experimentally. Subsequently, a BP neural network is trained using large-scale CFD datasets to establish an aluminum concentration prediction model, capturing the nonlinear relationships between process parameters (e.g., casting speed, temperature) and compositional segregation. Finally, optimization algorithms are applied to determine optimal process parameters, which are validated via CFD multiphysics coupling simulations. The results demonstrate that this method predicts the average aluminum concentration in the ingot with an error of ≤3%, significantly reducing computational costs. It also elucidates the kinetic mechanisms of aluminum volatilization and diffusion, revealing that non-monotonic segregation trends arise from the dynamic balance of volatilization, diffusion, convection, and solidification. Moreover, the most uniform aluminum distribution (average 6.8 wt.%, R² = 0.002) is achieved in a double-overflow mold at a casting speed of 18 mm/min and a temperature of 2168 K.

As the pace of human development in the ocean accelerates, the demand for corrosion-resistant building materials in marine engineering projects is constantly increasing. The development of high-performance corrosion-resistant materials and their mechanism research has gradually become the latest hotspot in the field of metal materials. Through cyclic dry–wet testing, electrochemical techniques, morphological characterization, and compositional analysis, this study simulated the impact of varying manganese content on weathering steel corrosion resistance in chloride environments. The results indicated that, compared to low-manganese weathering steel, the average rust layer thickness of high-manganese weathering steel increased by 22.5%. Additionally, manganese was found in the rust as MnO/MnO₂, acting as a catalyst to promote electron transfer, which led to a decrease in the α/γ* ratio in the high-manganese weathering steel, thereby accelerating the corrosion reaction.

Titanium metal is primarily produced via the Kroll process, which is characterized by a semi-continuous production flow and a lengthy process cycle, resulting in high production costs. Researchers have explored alternative methods for titanium production, including molten salt electrolysis, such as the Fray–Farthing–Chen (FFC), Ono Suzuki (OS), and University of Science and Technology Beijing (USTB) processes, aiming to achieve more economical production. Among these, the USTB process, a representative of soluble anode electrolysis, has shown significant promise. However, controlling oxygen concentration in titanium produced by soluble anode electrolysis remains a challenge. This study proposes a novel approach to enhance deoxidation efficiency in soluble anode electrolysis for titanium production by introducing yttrium chloride (YCl₃) into the molten salt electrolyte. Thermodynamic analysis and experimental validation demonstrate that the theoretical deoxidation limit for titanium can reach below 100 ppm under Y/YOCl/YCl₃ equilibrium. We report the successful synthesis of titanium powder with an oxygen concentration of 6000 ppm from titanium-carbon-oxygen solid solution. Under optimized conditions, the purity of the titanium powder reached 99.42%, demonstrating a new approach for producing high-purity titanium. This method, based on soluble anode electrolysis, offers a potential alternative to the conventional Kroll process. The research elucidates the fabrication process and analytical methods for titanium-carbon-oxygen solid solution, and employs a combination of analytical techniques, including XRD, SEM-EDS, and ONH Analyzer, for characterization of the electrolytic product, encompassing phase analysis, microstructure, and oxygen concentration testing.

Dense lithium hydride (LiH) is widely used in neutron shielding applications for thermonuclear reactors and space systems due to its unique properties. However, traditional sintering methods often lead to cracking in LiH products. This study investigates the densification sintering of LiH using microwave technology. A multiphysics model was established based on the measured dielectric properties of LiH at different temperatures, allowing for a detailed analysis of the electromagnetic and thermal field distributions during the microwave heating of cylindrical LiH samples. The results indicate that the electric field distribution within the LiH is relatively uniform, with resistive losses concentrated primarily in the LiH region of the microwave cavity. LiH rapidly absorbs microwave energy, reaching the sintering temperature of 520 °C in just 415 s. Additionally, the temperature difference between the low- and high-temperature regions during the sintering process remains below 5 °C, demonstrating excellent uniform heating characteristics. The microwave sintering process enhances interface migration within the LiH samples, resulting in dense metallurgical bonding between grains. In summary, this research provides valuable insights and theoretical support for the rapid densification of LiH materials, highlighting the potential of microwave technology in improving material properties.