During carbon capture from steel slag, large amounts of calcium and magnesium are utilized, whereas silica is largely unutilized. In this study, porous SiO₂ materials were synthesized from the silicon in steel slag for use in indirect carbon capturing. The porous SiO₂ materials were synthesized from γ-dicalcium silicate (γ-Ca₂SiO₄ or γ-C₂S), the main silicon-containing phase in steel slag. The phase exhibits moderate reactivity, and the synthesis of the materials was achieved by optimizing the synthesis parameters and minimizing the influence of other elements present in the slag. The effects of synthesis temperature and pH value of the porous SiO₂ materials on their morphologies, structural parameters, and CO₂ sorption performance were investigated. Using cetyltrimethylammonium bromide as a template agent, porous SiO₂ materials with controllable structural properties could be prepared by adjusting their synthesis temperature and pH values. Porous SiO₂ materials with outstanding properties, such as tunable pore diameters and high specific surfaces (~1000 m²/g) were synthesized in an acidic solution via different types of interactions between the organic template and inorganic silica species. The maximum CO₂ sorption capacity of the porous SiO₂ material samples exceeded 70 mg/g. A resource utilization mechanism for transforming silica sources in steel slag into porous SiO₂ materials is proposed in the study. Highly ordered, pure porous SiO₂ materials with large specific surface areas were successfully synthesized from steel slag. The results indicated that steel-slag-based porous SiO₂ materials would be promising candidates for the in situ CO₂ capture in the steel industry.

To address the fact that the mechanism of split grouting in deep weak rock formations lags far behind engineering practice, this study develops a theoretical model for splitting diffusion in a single fracture by integrating the fracture propagation morphology and slurry flow behavior. Using coupled theoretical equations within the numerical simulation software, a numerical model of grouting-induced splitting in weak rock formations was developed considering the rheological properties of the slurry viscosity. Based on the numerical simulations investigating the effects of the formation depth, slurry viscosity, and grouting rate on the effectiveness of split grouting diffusion, during split grouting operations in deep rock formations, a relatively high initial grouting rate and pressure is recommended to be adopted in the early stage to initiate or connect fractures. Following grouting pressure stabilization, the slurry viscosity should be appropriately increased to promote rapid gelation along the fracture surfaces, thereby effectively sealing the fractures. A grouting control strategy featuring initial high-pressure, high-flow injection followed by low-rate consolidation was systematically developed and successfully applied to the reinforcement of fractured surrounding rock in the −1000 m floor slab of the vehicle yard roadway in the eastern section of Guqiao Mine, achieving notable engineering results.

The destruction of backfill under strong disturbances can be regarded as the result of stress waves propagating in the filling medium; however, accurately quantifying the stress wave attenuation and energy dissipation conversion process in backfill materials remains at an exploratory stage. The split Hopkinson pressure bar (SHPB) experimental system is employed in this study to conduct dynamic compression tests on backfill materials with different media, encompassing various strain rates. The relative dissipation factor, defined as the ratio of the wave attenuation coefficient to the energy dissipation coefficient, is utilized to establish dimensionless empirical formulas incorporating influencing factors such as the strain rate through a dimensional analysis method. Furthermore, the transformation process of stress wave propagation and energy dissipation within the backfill is thoroughly analyzed. The experimental results demonstrate that the strain rate and wave impedance affect the stress wave propagation and dynamic mechanical properties of the material. At a constant strain rate, a smaller sample wave impedance leads to a larger reflected wave and a smaller transmitted wave. Additionally, the backfill does not affect the normalized spectra of the incident and transmitted waves along the propagation path. When the wave impedance is constant, both the peak stress and peak strain increase with increasing strain rate. Samples subjected to strain rates of 34.51 and 68.34 s⁻¹ exhibit “double peaks” in their stress–strain curves, indicating prolonged plastic flow behavior. Furthermore, a negative correlation is observed between the model data and original data with respect to the peak stress and density, whereas positive correlations are observed for the wave impedance and strain rate. This study provides a new method for analyzing the stress wave propagation and energy dissipation of cemented tailings backfill (CTB) materials under strong disturbances, which can provide a comprehensive understanding of the failure mechanism of CTB materials under impact loads.

In this study, to investigate the mechanism through which high-voltage electrical pulses (HVEPs) enhance coal permeability and improve coalbed methane (CBM) extraction efficiency, liquid nitrogen adsorption analysis, nuclear magnetic resonance, infrared spectroscopy, and scanning electron microscopy were performed on HVEP-treated coal samples. The mentioned techniques were used to analyze the crack structures, pore distribution patterns, and changes in the chemical functional groups in the coal samples. The permeability enhancement mechanism of HVEP in coal was explored from macroscopic, mesoscopic, and microscopic perspectives. The chemical modification of coal through the breakdown of its oxygen-containing functional groups reduced the gas adsorption capacity of the coal samples and enhanced their desorption abilities. Simultaneously, the number of pores within the bottleneck pore interval of the coal samples increased significantly. The closed pores transformed into semi-closed and open pores. The pore volume was 2.86 times the pore volume of the original coal, while the pore specific surface area growth rate was 48.67%. This pronounced pore expansion effect eliminated the bottleneck pore interval, which reduced CBM seepage efficiency and enabled cross-scale CBM transport. Extensive parallel fractures and fissures appeared throughout the coal body. The connectivity within the pore-fracture network was enhanced substantially. This improved connectivity provided efficient pathways for gas transport.

Glutamic acid (or glutamate) is one of the most promising amino acids shown to recover base metals from e-waste in terms of cost, selectivity, and bulk availability. This study integrates machine learning (ML) to optimize glutamate leaching of waste printed circuit boards (PCBs) and the subsequent sulfide precipitation for metal recovery. Among the evaluated ML models, the tree-based random forest (training R² = 0.99, testing R² = 0.77 for Cu and training R² = 0.98, testing R² = 0.88 for Zn) and boosted tree (training R² = 0.99, testing R² = 0.93 for Pb) models exhibited the best prediction accuracy for Cu, Zn, and Pb extraction as a function of experimental variables. Parameter contribution and SHapley Additive exPlanations analyses revealed that Cu and Zn extraction were mainly affected by leaching time, initial pH, and glutamate dosage, whereas Pb extraction was affected by multiple variables. Sulfide precipitation efficiently recovered 96.9% of Cu, 100% of Co, 96.0% of Pb, 46.2% of Sn, and 10%–17% of Zn, Al, and Ni as mixed sulfides from the leachates, with CuS purity reaching 85.2%. Moreover, the regenerated glutamate ligand released during precipitation enabled faster leaching kinetics and higher Cu extraction in a subsequent cycle. Thus, our results demonstrate a sustainable data-driven pathway for base metal recovery from e-waste based on amino acids. Moreover, the framework developed here can also be applied to other complex resources.

Owing to the lack of effective beneficiation technologies, valuable wolframite and cassiterite resources are lost in the tailings of tungsten–tin polymetallic mines. For Shizhuyuan polymetallic mine, the recovery efficiencies of wolframite and cassiterite were significantly lower than those of scheelite using existing process. In this study, the underlying causes were investigated based on the flotation kinetics, surface hydration characteristics of the three target minerals, and the adsorption behavior of the collector. To enhance the comprehensive recovery of wolframite and cassiterite from the tailings, a novel double-ligand collector, Pb–BHA–SDBS (lead–benzohydroxamic acid–sodium dodecyl benzene sulfonate), was designed and successfully implemented in the Shizhuyuan polymetallic mine. Accordingly, a novel beneficiation process combining flotation and gravity separation was developed, yielding an additional low-grade W–Sn mixed concentrate. This process not only generates economic value for enterprises but also provides a reference for the comprehensive utilization of tungsten–tin polymetallic ore in the Nanling metallogenic belt of China.

The non-ferrous metallurgical industry holds an important strategic position for national construction and the achievement of a strong manufacturing country. Green, low-carbon, and efficient production in non-ferrous metallurgical enterprises relies on process modeling, optimization, and control. This article reviews the process modeling techniques and optimal control methods for non-ferrous metallurgical processes and mineral processing. Modeling and optimal control techniques encompass both traditional methods and artificial intelligence (AI) approaches, including deep learning, intelligent control, and reinforcement learning. The application of AI in non-ferrous metallurgical industries is receiving increasing attention. Therefore, we highlight the challenges and prospects of process modeling and optimal control, including the challenges of data scarcity, model interpretability, and integration complexity. Big data modeling, model incremental learning, cloud-edge collaborative control, and digital twin system-based optimization control will play an important role in future plants.

To address resource waste and environmental pollution from electronic waste (e-waste), this study summarized the recycling technology for precious metals from e-waste based on bibliometrics and knowledge graph methods. The relevant literature from 2014 to 2024 was analyzed, and technological development trends and international cooperation characteristics were outlined. The results indicate that the recycling technology for precious metals from e-waste has evolved from traditional hydrometallurgy to green separation technology, and research on integrated circulation systems has gradually taken shape. China dominates the research in this field, with high-yield authors and institutions primarily situated in the country. International cooperation has continued to strengthen over time. However, technological bottlenecks remain in industrial applications. Future research should focus on biomaterial coupling technology and full lifecycle system optimization to promote the in-depth integration of resource recycling theory and sustainable development goals.

Manganese ore is a critical strategic resource, and its type and endowment characteristics determine its potential applications in the field of new energy materials. This paper reviews the resource characteristics of multisource ores and their research progress in the new energy sector. First, the status and main types of manganese ores are introduced, and their strategic significance and physicochemical properties of manganese oxides and carbonates are analyzed. Second, various processing methods for different types of manganese ores, including hydrometallurgy, pyrometallurgy, and other technologies, are discussed to optimize resource utilization. Subsequently, the preparation and applications of manganese-based materials are reviewed, including manganese sulfate, manganese dioxide, manganese tetroxide, and manganese alloys (such as electrolytic manganese, manganese–zinc ferrite, and manganese steel). Finally, an outlook on the comprehensive utilization of manganese resources and future development directions is presented. This study provides a valuable reference for researchers in minerals processing, metallurgy, materials science, and environmental science, promoting the efficient development of multisource manganese ores and the sustainable advancement of new energy materials.

Quartz is a vital raw material for advanced industries, with high-purity quartz (HPQ, greater than or equal to 99.99% SiO₂) being indispensable in semiconductor manufacturing, photovoltaic cells, optical fibers, and medical technologies. This review critically examines the evolution of HPQ purification technologies, highlighting their mechanisms, efficiencies, and sustainability challenges. Traditional chemical methods such as hydrofluoric acid leaching and chlorination remove impurities but pose significant environmental and safety risks. Thermal treatments, including high-temperature annealing (higher than 1000 °C), deliver improved crystallinity but require substantial energy input. Physical approaches, including flotation, grinding, and magnetic separation, are more environmentally friendly but insufficient for achieving semiconductor-grade purity. Recent innovations, particularly hybrid processes such as microwave-assisted leaching and bioleaching, have demonstrated notable improvements in impurity removal and resource optimization. However, challenges remain in managing complex mineralogy, high reagent consumption, and operational costs. Beyond purification, this review also evaluates the scalability, environmental impact, and potential of sustainable strategies to meet the growing global demand for HPQ. By consolidating applications, purification pathways, and associated challenges into a unified framework, this review provides a comprehensive foundation for advancing both technological innovation and sustainable practices in HPQ production.