International Journal of Minerals, Metallurgy, and Materials最新文献

The high temperature compression test of Be/2024Al composites with 62wt% Be was conducted at 500–575°C and strain rate of 0.003–0.1 s⁻¹. The strain-compensated Arrhenius model and modified Johnson–Cook model were introduced to predict the hot deformation behavior of Be/2024Al composites. The result shows that the activation energy of Be/2024Al composites was 363.364 kJ·mol⁻¹. Compared with composites reinforced with traditional ceramics, Be/2024Al composites can be deformed with ultra-high content of reinforcement, attributing to the deformable property of Be particles. The average relative error of the two models shows that modified Johnson–Cook model was more suitable for low temperature condition while strain-compensated Arrhenius model was more suitable for high temperature condition. The processing map was generated and a hot extrusion experiment was conducted according to the map. A comparation of the microstructure of Be/2024Al composites before and after extrusion shows that the Be particle deformed coordinately with the matrix and elongated at the extrusion direction.

With the development of automation and informatization in the steelmaking industry, the human brain gradually fails to cope with an increasing amount of data generated during the steelmaking process. Machine learning technology provides a new method other than production experience and metallurgical principles in dealing with large amounts of data. The application of machine learning in the steelmaking process has become a research hotspot in recent years. This paper provides an overview of the applications of machine learning in the steelmaking process modeling involving hot metal pretreatment, primary steelmaking, secondary refining, and some other aspects. The three most frequently used machine learning algorithms in steelmaking process modeling are the artificial neural network, support vector machine, and case-based reasoning, demonstrating proportions of 56%, 14%, and 10%, respectively. Collected data in the steelmaking plants are frequently faulty. Thus, data processing, especially data cleaning, is crucially important to the performance of machine learning models. The detection of variable importance can be used to optimize the process parameters and guide production. Machine learning is used in hot metal pretreatment modeling mainly for endpoint S content prediction. The predictions of the endpoints of element compositions and the process parameters are widely investigated in primary steelmaking. Machine learning is used in secondary refining modeling mainly for ladle furnaces, Ruhrstahl–Heraeus, vacuum degassing, argon oxygen decarburization, and vacuum oxygen decarburization processes. Further development of machine learning in the steelmaking process modeling can be realized through additional efforts in the construction of the data platform, the industrial transformation of the research achievements to the practical steelmaking process, and the improvement of the universality of the machine learning models.

Metal corrosion causes significant economic losses, safety issues, and environmental pollution. Hence, its prevention is of immense research interest. Carbon dots (CDs) are a new class of zero-dimensional carbon nanomaterials, which have been considered for corrosion protection applications in recent years due to their corrosion inhibition effect, fluorescence, low toxicity, facile chemical modification, and cost-effectiveness. This study provides a comprehensive overview of the synthesis, physical and chemical properties, and anticorrosion mechanisms of functionalized CDs. First, the corrosion inhibition performance of different types of CDs is introduced, followed by discussion on their application in the development of smart protective coatings with self-healing and/or self-reporting properties. The effective barrier formed by CDs in the coatings can inhibit the spread of local damage and achieve self-healing behavior. In addition, diverse functional groups on CDs can interact with Fe³⁺ and H⁺ ions generated during the corrosion process; this interaction changes their fluorescence, thereby demonstrating self-reporting behavior. Moreover, challenges and prospects for the development of CD-based corrosion protection systems are also presented.

The tensile properties of 2297-T87 Al–Li alloy thick plates at different thickness position and in different direction were analyzed via tensile testing, optical microscopy (OM), X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive spectrometry (EDS), and transmission electron microscopy (TEM). Results indicated that the ultimate tensile strength (UTS) and yield strength (YS) of the alloy decreased firstly and then increased from the 1/8T position to the 1/2T position, whereas elongation to failure (E_f) decreased gradually such that its value along the rolling direction (RD) was higher than those along the transverse direction (TD) at the same thickness position. From the 1/8T position to the 3/8T position of the alloy, the UTS and YS along the TD were higher than those along the RD. At the 1/2T position of the alloy, the UTS, YS, and E_f along the RD were the highest, whereas those along the normal direction (ND) were the lowest. Microstructural observations further revealed that the anisotropy of tensile properties was related to grain morphology, crystal texture, second-phase particles, and Li atom segregation.

Effectively strengthening the surface sulfidation is essential for recovering hemimorphite by froth flotation. In this work, inductively coupled plasma optical emission spectrometer (ICP-OES) measurements, Visual MINTEQ calculation, X-ray photoelectron spectroscopy (XPS) analysis, time of flight secondary ion mass spectrometry (ToF-SIMS) analysis, and micro-flotation experiments were explored to systematically investigate the effect of ammonium sulfate ((NH₄)₂SO₄) on the formation of zinc sulfide species on hemimorphite surface and its role in sulfidation flotation. The results showed that (NH₄)₂SO₄ exhibited a positive influence on hemimorphite sulfidation flotation. It was ascribed to the number of zinc components in the form of Zn²⁺ and [Zn(NH₃)_i]²⁺ (i = 1–4) increased in the flotation system after hemimorphite treatment with (NH₄)₂SO₄, which was beneficial to its interaction with sulfur species in solution, resulting in a dense and stable zinc sulfide layer generated on the hemimorphite surface. [Zn(NH₃)_i]²⁺ participated in the sulfidation reaction of hemimorphite as a transition state. In addition, the sulfidation reaction of hemimorphite was accelerated by (NH₄)₂SO₄. Thus, (NH₄)₂SO₄ presents a vital role in promoting the sulfidation of hemimorphite.

Waste heat recovery from hot steel slag was determined in a granular bed through the combination of numerical simulation and an industrial test method. First, the effective thermal conductivity of the granular bed was calculated. Then, the unsteady-state model was used to simulate the heat recovery under three different flow fields (O-type, S-type, and nonshielding type (Nontype)). Second, the simulation results were validated by in-situ industrial experiments. The two methods confirmed that the heat recovery efficiencies of the flow fields from high to low followed the order of Nontype, S-type, and O-type. Finally, heat recovery was carried out under the Nontype flow field in an industrial test. The heat recovery efficiency increased from ∼76% and ∼78% to ∼81% when the steel slag thickness decreased from 400 and 300 to 200 mm, corresponding to reductions in the steel slag mass from 3.96 and 2.97 to 1.98 t with a blower air volume of 14687 m³/h. Therefore, the research results showed that numerical simulation can not only guide experiments on waste heat recovery but also optimize the flow field. Most importantly, the method proposed in this paper has achieved higher waste heat recovery from hot steel slag in industrial scale.

Tensile and shear fractures are significant mechanisms for rock failure. Understanding the fractures that occur in rock can reveal rock failure mechanisms. Scanning electron microscopy (SEM) has been widely used to analyze tensile and shear fractures of rock on a mesoscopic scale. To quantify tensile and shear fractures, this study proposed an innovative method composed of SEM images and deep learning techniques to identify tensile and shear fractures in red sandstone. First, direct tensile and preset angle shear tests were performed for red sandstone to produce representative tensile and shear fracture surfaces, which were then observed by SEM. Second, these obtained SEM images were applied to develop deep learning models (AlexNet, VGG13, and SqueezeNet). Model evaluation showed that VGG13 was the best model, with a testing accuracy of 0.985. Third, the features of tensile and shear fractures of red sandstone learned by VGG13 were analyzed by the integrated gradient algorithm. VGG13 was then implemented to identify the distribution and proportion of tensile and shear fractures on the failure surfaces of rock fragments caused by uniaxial compression and Brazilian splitting tests. Results demonstrated the model feasibility and suggested that the proposed method can reveal rock failure mechanisms.

Anion exchange membrane (AEM) electrolysis is a promising membrane-based green hydrogen production technology. However, AEM electrolysis still remains in its infancy, and the performance of AEM electrolyzers is far behind that of well-developed alkaline and proton exchange membrane electrolyzers. Therefore, breaking through the technical barriers of AEM electrolyzers is critical. On the basis of the analysis of the electrochemical performance tested in a single cell, electrochemical impedance spectroscopy, and the number of active sites, we evaluated the main technical factors that affect AEM electrolyzers. These factors included catalyst layer manufacturing (e.g., catalyst, carbon black, and anionic ionomer) loadings, membrane electrode assembly, and testing conditions (e.g., the KOH concentration in the electrolyte, electrolyte feeding mode, and operating temperature). The underlying mechanisms of the effects of these factors on AEM electrolyzer performance were also revealed. The irreversible voltage loss in the AEM electrolyzer was concluded to be mainly associated with the kinetics of the electrode reaction and the transport of electrons, ions, and gas-phase products involved in electrolysis. Based on the study results, the performance and stability of AEM electrolyzers were significantly improved.

Solution and quenching heat treatments are generally carried out in a roller hearth furnace for large-scale thick aluminum alloy plates. However, the asymmetric or uneven spray water flow rate is inevitable under industrial production conditions, which leads to an asymmetric residual stress distribution. The spray quenching treatment was conducted on self-designed spray equipment, and the residual stress along the thickness direction was measured by a layer removal method based on deflections. Under the asymmetric spray quenching condition, the subsurface stress of the high-flow rate surface was lower than that of the low-flow rate surface, and the difference between the two subsurface stresses increased with the increase in the difference in water flow rates. The subsurface stress underneath the surface with a water flow rate of 0.60 m³/h was 15.38 MPa less than that of 0.15 m³/h. The simulated residual stress by finite element (FE) method of the high heat transfer coefficient (HTC) surface was less than that of the low HTC surface, which is consistent with the experimental results. The FE model can be used to analyze the strain and stress evolution and predict the quenched stress magnitude and distribution.

To realize the comprehensive utilization of ludwigite ore, an integrated and efficient route for the boron and iron separation was proposed in this work, which via soda-ash roasting under CO–CO_2–N₂ atmosphere followed by grind-leaching, magnetic separation, and CO₂ carbonation. The effects of roasting temperature, roasting time, CO/(CO+CO₂) composition, and Na₂CO₃ dosage on the boron and iron separation indices were primarily investigated. Under the optimized conditions of the roasting temperature of 850°C, roasting time of 60 min, soda ash dosage of 20wt%, and CO/(CO+CO₂) of 10vol%, 92% of boron was leached during wet grinding, and 88.6% of iron was recovered during the magnetic separation and magnetic concentrate with a total iron content of 61.51wt%. Raman spectra and ¹¹B NMR results indicated that boron exists as B(OH) ⁻₄ in the leachate, from which high-purity borax pentahydrate could be prepared by CO₂ carbonation.