The rapid advancement of modern science and technology, coupled with the recent surge in new-energy electric vehicles, has significantly boosted the demand for lithium. This has promoted the development and efficient utilization of lepidolite as a lithium source. Therefore, the processes for the flotation of lepidolite have been studied in depth, particularly the development and use of lepidolite flotation collectors and the action mechanism of the collectors on the lepidolite surface. Based on the crystal-structure characteristics of lepidolite minerals, this review focuses on the application of anionic collectors, amine cationic collectors (primary amines, quaternary ammonium salts, ether amines, and Gemini amines), and combined collectors to the flotation behavior of lepidolite as well as the adsorption mechanisms. New directions and technologies for the controllable flotation of lepidolite are proposed, including process improvement, reagent synthesis, and mechanistic research. This analysis demonstrates the need for the further study of the complex environment inside lepidolite and pulp. By using modern analytical detection methods and quantum chemical calculations, research on reagents for the flotation of lepidolite has expanded, providing new concepts and references for the efficient flotation recovery and utilization of lepidolite.

Titanium exhibits outstanding properties, particularly, high specific strength and resistance to both high and low temperatures, earning it a reputation as the metal of the future. However, because of the highly reactive nature of titanium, metallic titanium production involves extensive procedures and high costs. Considering its advantages and limitations, the European Union has classified titanium metal as a critical raw material (CRM) of low category. The Kroll process is predominantly used to produce titanium; however, molten salt electrolysis (MSE) is currently being explored for producing metallic titanium at a low cost. Since 2000, electrolytic titanium production has undergone a wave of technological advancements. However, because of the intermediate and disproportionation reactions in the electrolytic titanium production process, the process efficiency and titanium purity according to industrial standards could not be achieved. Consequently, metallic titanium production has gradually diversified into employing technologies such as thermal reduction, MSE, and titanium alloy preparation. This study provides a comprehensive review of research advances in titanium metal preparation technologies over the past two decades, highlighting the challenges faced by the existing methods and proposing potential solutions. It offers useful insights into the development of low-cost titanium preparation technologies.

The large-scale production of high-Ti steels is limited by the formation of Ti-containing oxides or nitrides in steel-slag reactions during continuous casting. These processes degrade mold flux properties, clog submerged entry nozzles, form floaters in the molds, and produce various surface defects on the cast slabs. This review summarizes the effects of nonmetallic inclusions on traditional CaO–SiO₂-based (CS) mold fluxes and novel CaO–Al₂O₃-based (CA) low- or non-reactive fluxes containing TiO₂, BaO, and B₂O₃ additives to avoid undesirable steel, slag, and inclusion reactions, with the aim of providing a new perspective for research and practice related to balancing the lubrication and heat transfer of mold fluxes to promote smooth operation and reduce surface defects on cast slabs. For traditional CS mold flux, although the addition of solvents such as Na₂O, Li₂O, and B₂O₃ can enhance flowability, steel–slag reactions persist, limiting the effectiveness of CS mold fluxes in high-Ti steel casting. Low- or non-reactive CA mold fluxes with reduced SiO₂ content are a research focus, where adding other components can significantly change flux characteristics. Replacing CaO with BaO can lower the melting point and inhibit crystallization, allowing the flux to maintain good flowability at low temperatures. Replacing SiO₂ with TiO₂ can stabilize the viscosity and enhance heat transfer. To reduce the environmental impact, fluorides are replaced with components such as TiO₂, B₂O₃, BaO, Li₂O, and Na₂O for F-free mold fluxes with similar lubrication, crystallization, and heat-transfer effects. When TiO₂ replaces CaF₂, it stabilizes the viscosity and enhances the heat conductivity, forming CaTiO₃ and CaSiTiO₅ phases instead of cuspidine to control crystallization. B₂O₃ lowers the melting point and suppresses crystallization, forming phases such as Ca₃B₂O₆ and Ca₁₁Si₄B₂O₂₂. BaO introduces non-bridging oxygen to reduce viscosity and ensure flux flowability at low temperatures. However, further studies are required to determine the optimal mold flux compositions corresponding to the steel grades and the interactions between the various components of the mold flux. In the future, the practical application of new mold fluxes for high-Ti steel will become the focus of further verification to achieve a balance between lubrication and heat transfer, which is expected to minimize the occurrence of casting problems and slab defects.

High-performance alloys are indispensable in modern engineering because of their exceptional strength, ductility, corrosion resistance, fatigue resistance, and thermal stability, which are all significantly influenced by the alloy interface structures. Despite substantial efforts, a comprehensive overview of interface engineering of high-performance alloys has not been presented so far. In this study, the interfaces in high-performance alloys, particularly grain and phase boundaries, were systematically examined, with emphasis on their crystallographic characteristics and chemical element segregations. The effects of the interfaces on the electrical conductivity, mechanical strength, toughness, hydrogen embrittlement resistance, and thermal stability of the alloys were elucidated. Moreover, correlations among various types of interfaces and advanced experimental and computational techniques were examined using big data analytics, enabling robust design strategies. Challenges currently faced in the field of interface engineering and emerging opportunities in the field are also discussed. The study results would guide the development of next-generation high-performance alloys.

This review provides a comprehensive overview of recent advancements in aluminum-based conductor alloys engineered to achieve superior mechanical strength and thermal stability without sacrificing electrical conductivity. Particular emphasis is placed on the role of microalloying elements—particularly Sc and Zr—in promoting the formation of coherent nanoscale precipitates such as Al₃Zr, Al₃Sc, and core–shell Al₃(Sc,Zr) with metastable L1₂ crystal structures. These precipitates contribute significantly to high-temperature performance by enabling precipitation strengthening and stabilizing grain boundaries. The review also explores the emerging role of other rare earth elements (REEs), such as erbium (Er), in accelerating precipitation kinetics and improving thermal stability by retarding coarsening. Additionally, recent advancements in thermomechanical processing strategies are examined, with a focus on scalable approaches to optimize the strength–conductivity balance. These approaches involve multi-step heat treatments and carefully controlled manufacturing sequences, particularly the combination of cold drawing and aging treatment to promote uniform and effective precipitation. This review offers valuable insights to guide the development of cost-effective, high-strength, heat-resistant aluminum alloys beyond conductor applications, particularly those strengthened through microalloying with Sc and Zr.

The rapid advancements in computer vision (CV) technology have transformed the traditional approaches to material microstructure analysis. This review outlines the history of CV and explores the applications of deep-learning (DL)-driven CV in four key areas of materials science: microstructure-based performance prediction, microstructure information generation, microstructure defect detection, and crystal structure-based property prediction. The CV has significantly reduced the cost of traditional experimental methods used in material performance prediction. Moreover, recent progress made in generating microstructure images and detecting microstructural defects using CV has led to increased efficiency and reliability in material performance assessments. The DL-driven CV models can accelerate the design of new materials with optimized performance by integrating predictions based on both crystal and microstructural data, thereby allowing for the discovery and innovation of next-generation materials. Finally, the review provides insights into the rapid interdisciplinary developments in the field of materials science and future prospects.

Cemented paste backfill (CPB) is a technology that achieves safe mining by filling the goaf with waste rocks, tailings, and other materials. It is an inevitable choice to deal with the development of deep and highly difficult mines and meet the requirements of environmental protection and safety regulations. It promotes the development of a circular economy in mines through the development of low-grade resources and the resource utilization of waste, and extends the service life of mines. The mass concentration of solid content (abbreviated as “concentration”) is a critical parameter for CPB. However, discrepancies often arise between the on-site measurements and the pre-designed values due to factors such as groundwater inflow and segregation within the goaf, which cannot be evaluated after the solidification of CPB. This paper innovatively provides an in-situ non-destructive approach to identify the real concentration of CPB after curing for certain days using hyperspectral imaging (HSI) technology. Initially, the spectral variation patterns under different concentration conditions were investigated through hyperspectral scanning experiments on CPB samples. The results demonstrate that as the CPB concentration increases from 61wt% to 73wt%, the overall spectral reflectance gradually increases, with two distinct absorption peaks observed at 1407 and 1917 nm. Notably, the reflectance at 1407 nm exhibited a strong linear relationship with the concentration. Subsequently, the K-nearest neighbors (KNN) and support vector machine (SVM) algorithms were employed to classify and identify different concentrations. The study revealed that, with the KNN algorithm, the highest accuracy was achieved when K (number of nearest neighbors) was 1, although this resulted in overfitting. When K = 3, the model displayed the optimal balance between accuracy and stability, with an accuracy of 95.03%. In the SVM algorithm, the highest accuracy of 98.24% was attained with parameters C (regularization parameter) = 200 and Gamma (kernel coefficient) = 10. A comparative analysis of precision, accuracy, and recall further highlighted that the SVM provided superior stability and precision for identifying CPB concentration. Thus, HSI technology offers an effective solution for the in-situ, non-destructive monitoring of CPB concentration, presenting a promising approach for optimizing and controlling CPB characteristic parameters.

Mine filling materials urgently need to improve mechanical properties and achieve low-carbon transformation. This study explores the mechanism of the synergistic effect of optimizing aggregate fractal grading and introducing CO₂ nanobubble technology to improve the performance of cement–fly ash-based backfill materials (CFB). The properties including fluidity, setting time, uniaxial compressive strength, elastic modulus, porosity, microstructure and CO₂ storage performance were systematically studied through methods such as fluidity evaluation, time test, uniaxial compression test, mercury intrusion porosimetry (MIP), scanning electron microscopy-energy dispersive spectroscopy analysis (SEM-EDS), and thermogravimetric-differential thermogravimetric analysis (TG-DTG). The experimental results show that the density and strength of the material are significantly improved under the synergistic effect of fractal dimension and CO₂ nanobubbles. When the fractal dimension reaches 2.65, the mass ratio of coarse and fine aggregates reaches the optimal balance, and the structural density is greatly improved at the same time. At this time, the uniaxial compressive strength and elastic modulus reach their peak values, with increases of up to 13.46% and 27.47%, respectively. CO₂ nanobubbles enhance the material properties by promoting hydration reaction and carbonization. At the microscopic level, CO₂ nanobubble water promotes the formation of C–S–H (hydrated calcium silicate), C–A–S–H (hydrated calcium aluminium silicate) gel and CaCO₃, which is the main way to enhance the performance. Thermogravimetric studies have shown that when the fractal dimension is 2.65, the dehydration of hydration products and the decarbonization process of CaCO₃ are most obvious, and CO₂ nanobubble water promotes the carbonization reaction, making it surpass the natural state. The CO₂ sequestration quality of cement–fly ash-based materials treated with CO₂ nanobubble water at different fractal dimensions increased by 12.4wt% to 99.8wt%. The results not only provide scientific insights for the design and implementation of low-carbon filling materials, but also provide a solid theoretical basis for strengthening green mining practices and promoting sustainable resource utilization.

An image processing and deep learning method for identifying different types of rock images was proposed. Preprocessing, such as rock image acquisition, gray scaling, Gaussian blurring, and feature dimensionality reduction, was conducted to extract useful feature information and recognize and classify rock images using TensorFlow-based convolutional neural network (CNN) and PyQt5. A rock image dataset was established and separated into workouts, confirmation sets, and test sets. The framework was subsequently compiled and trained. The categorization approach was evaluated using image data from the validation and test datasets, and key metrics, such as accuracy, precision, and recall, were analyzed. Finally, the classification model conducted a probabilistic analysis of the measured data to determine the equivalent lithological type for each image. The experimental results indicated that the method combining deep learning, TensorFlow-based CNN, and PyQt5 to recognize and classify rock images has an accuracy rate of up to 98.8%, and can be successfully utilized for rock image recognition. The system can be extended to geological exploration, mine engineering, and other rock and mineral resource development to more efficiently and accurately recognize rock samples. Moreover, it can match them with the intelligent support design system to effectively improve the reliability and economy of the support scheme. The system can serve as a reference for supporting the design of other mining and underground space projects.

Interfacial interactions between rough mineral particles have garnered considerable attention as they directly determine particle agglomeration and floatability. This study comprehensively investigates the agglomeration characteristics of siderite particles after argon (Ar) plasma surface modification through settling tests, flocs size measurements, and fractal dimension calculations. Ar plasma surface modification promotes the agglomeration of siderite particles, as evidenced by increased floc size and density. The agglomeration mechanism induced by Ar plasma surface modification is evaluated using a theoretical model combining the surface element integration (SEI) approach, differential geometry, and the composite Simpson’s rule. Changes in surface roughness, wettability, and charge are considered in this model. Compared to the unpretreated siderite particles, the energy barrier for interaction of the 30-min Ar plasma-pretreated siderite particles decreases from 2.3 × 10⁻¹⁷ J to 1.6 × 10⁻¹⁷ J. This reduction provides strong evidence for the agglomeration behavior of siderite particles. Furthermore, flotation experiments confirm that Ar plasma surface modification is conducive to the aggregation flotation of siderite. These findings offer crucial insights into particle aggregation and dispersion behaviors, with notable application in mineral flotation.

It is difficult to recover chrysocolla from sulfidation flotation, which is closely related to the mineral surface composition. In this study, the effects of fluoride roasting on the surface composition of chrysocolla were investigated, its impact on sulfidation flotation was explored, and the mechanisms involved in both fluoride roasting and sulfidation flotation were discussed. With CaF₂ as the roasting reagent, Na₂S·9H₂O as the sulfidation reagent, and sodium butyl xanthate (NaBX) as the collector, the results of the flotation experiments showed that fluoride roasting improved the floatability of chrysocolla, and the recovery rate increased from 16.87% to 82.74%. X-ray diffraction analysis revealed that after fluoride roasting, approximately all the Cu on the chrysocolla surface was exposed in the form of CuO, which could provide a basis for subsequent sulfidation flotation. The microscopy and elemental analyses revealed that large quantities of “pagoda-like” grains were observed on the sulfidation surface of the fluoride-roasted chrysocolla, indicating high crystallinity particles of copper sulfide. This suggests that the effect of sulfide formation on the chrysocolla surface was more pronounced. X-ray photoelectron spectroscopy revealed that fluoride roasting increased the relative contents of sulfur and copper on the surface and that both the Cu⁺ and polysulfide fractions on the surface of the minerals increased. This enhances the effect of sulfidation, which is conducive to flotation recovery. Therefore, fluoride roasting improved the effect of copper species transformation and sulfidation on the surface of chrysocolla, promoted the adsorption of collectors, and improved the recovery of chrysocolla from sulfidation flotation.

Bentonite is a necessary binder in producing pellets. Its excessive use reduces the iron grade of pellets and increases production costs. Minimizing bentonite dosage is essential for producing high-quality iron ore pellets. Addressing the gap in the application of organically-intercalated modified bentonite in the pelletizing field, this study introduces an innovative modification process for bentonite that employs the synergistic effect of mechanical force and dimethyl sulfoxide to enhance the intercalation of organic compounds within bentonite, thus significantly enhancing its binding performance. The colloid value and swell capacity of modified bentonite (98.5 mL/3g and 55.0 mL/g) were much higher than the original bentonite (90.5 mL/3g and 17.5 mL/g). With the decrease of bentonite dosage from 1.5wt% to 1.0wt%, the drop number of green pellets from a height of 0.5 m and the compressive strengths of roasted pellets using the modified bentonite (6.0 times and 2916 N per pellet) were significantly higher than those of the original bentonite (4.0 times and 2739 N per pellet). This study provides a comprehensive analysis of the intercalation modification mechanism of bentonite, offering crucial technical insights for the development of high-performance modified bentonite as iron ore pellet binders.

The novel process of hydrogen-based shaft furnaces (HSFs) has attracted considerable attention because of their significant reduction of CO₂ emissions. In this study, the interaction of H₂ and CO with Fe_tet1- and Fe_oct2-terminated Fe₃O₄(111) surfaces under HSF conditions, including their adsorption and reduction behaviors, was investigated using the density functional theory method. The results indicated that the H₂ molecule adsorbed onto the Fe_tet1-terminated surface with an adsorption energy (AE) of −1.36 eV, whereas the CO molecule preferentially adsorbed on the Fe_oct2-terminated surface with an AE of −1.56 eV. Both H₂ and CO can readily undergo reduction on the Fe_tet1-terminated surface (corresponding to energy barriers of 0.83 eV and 2.23 eV, respectively), but kinetically the reaction of H₂ is more favorable than that of CO. With regard to the thermodynamics at 400–1400 K, the H₂ was easy to be adsorbed, while the CO would like to react on the Fe_tet1-terminated surface. These thermodynamically tendencies were reversed on the Fe_oct2-terminated surface. The thermodynamic disadvantage of the reaction of H₂ on the Fe_tet1-terminated surface was offset by an increase in the temperature. Furthermore, the adsorption of H₂ and CO on the Fe_tet1-terminated surface was competitive, whereas the adsorption of them on the Fe_oct2-terminated surface was synergistic. Therefore, iron ores with a higher proportion of Fe_tet1-terminated surface can be applied for the HSF process. In conjunction with the increases in the reduction temperature and the ratio of H₂ in the reducing gas would promote efficient HSF smelting. These observations provide effective guidance for optimizing the practical operation parameters and advancing the development of the HSF process.

Automated classification of gas flow states in blast furnaces using top-camera imagery typically demands a large volume of labeled data, whose manual annotation is both labor-intensive and cost-prohibitive. To mitigate this challenge, we present an enhanced semi-supervised learning approach based on the Mean Teacher framework, incorporating a novel feature loss module to maximize classification performance with limited labeled samples. The model studies show that the proposed model surpasses both the baseline Mean Teacher model and fully supervised method in accuracy. Specifically, for datasets with 20%, 30%, and 40% label ratios, using a single training iteration, the model yields accuracies of 78.61%, 82.21%, and 85.2%, respectively, while multiple-cycle training iterations achieves 82.09%, 81.97%, and 81.59%, respectively. Furthermore, scenario-specific training schemes are introduced to support diverse deployment need. These findings highlight the potential of the proposed technique in minimizing labeling requirements and advancing intelligent blast furnace diagnostics.

The sulfation and decomposition process has proven effective in selectively extracting lithium from lepidolite. It is essential to clarify the thermochemical behavior and kinetic parameters of decomposition reactions. Accordingly, comprehensive kinetic study by employing thermalgravimetric analysis at various heating rates was presented in this paper. Two main weight loss regions were observed during heating. The initial region corresponded to the dehydration of crystal water, whereas the subsequent region with overlapping peaks involved complex decomposition reactions. The overlapping peaks were separated into two individual reaction peaks and the activation energy of each peak was calculated using isoconversional kinetics methods. The activation energy of peak 1 exhibited a continual increase as the reaction conversion progressed, while that of peak 2 steadily decreased. The optimal kinetic models, identified as belonging to the random nucleation and subsequent growth category, provided valuable insights into the mechanism of the decomposition reactions. Furthermore, the adjustment factor was introduced to reconstruct the kinetic mechanism models, and the reconstructed models described the kinetic mechanism model more accurately for the decomposition reactions. This study enhanced the understanding of the thermochemical behavior and kinetic parameters of the lepidolite sulfation product decomposition reactions, further providing theoretical basis for promoting the selective extraction of lithium.

This study utilizes wet/dry cyclic corrosion testing combined with corrosion big data technology to investigate the mechanism by which chloride ions (Cl⁻) influence the corrosion behavior of 650 MPa high-strength low-alloy (HSLA) steel in industrially polluted environments. The corrosion process of 650 MPa HSLA steel occurred in two distinct stages: an initial corrosion stage and a stable corrosion stage. During the initial phase, the weight loss rate increased rapidly owing to the instability of the rust layer. Notably, this study demonstrated that 650 MPa HSLA steel exhibited superior corrosion resistance in Cl-containing environments. The formation of a corrosion-product film eventually reduced the weight-loss rate. However, the intrusion of Cl⁻ at increasing concentrations gradually destabilized the α/γ* phases of the rust layer, leading to a looser structure and lower polarization resistance (R_p). The application of corrosion big data technology in this study facilitated the validation and analysis of the experimental results, offering new insights into the corrosion mechanisms of HSLA steel in chloride-rich environments.

Understanding the temperature dependent deformation behavior of Mg alloys is crucial for their expanding use in the aerospace sector. This study investigates the deformation mechanisms of hot-rolled AZ61 Mg alloy under uniaxial tension along rolling direction (RD) and transverse direction (TD) at −50, 25, 50, and 150°C. Results reveal a transition from high strength with limited elongation at −50°C to significant softening and maximum ductility at 150°C. TD samples consistently showed 2%–6% higher strength than RD; however, this yield anisotropy diminished at 150°C due to the shift from twinning to thermally activated slip and recovery. Fractography indicated a change from semi-brittle to fully ductile fracture with increasing temperature. Electron backscattered diffraction (EBSD) analysis confirmed twinning-driven grain refinement at low temperatures, while deformation at high temperatures involved grain elongation along shear zones, enabling greater strain accommodation before material failure.

During electrochemical machining (ECM), the passivation film formed on the surface of titanium alloy can lead to uneven dissolution and pitting. Solid particle erosion can effectively remove this passivation film. In this paper, the electrochemical dissolution behavior of Ti–6.5Al–2Zr–1Mo–1V (TA15) titanium alloy at without particle impact, low (15°) and high (90°) angle particle impact was investigated, and the influence of Al₂O₃ particles on ECM was systematically expounded. It was found that under the condition of no particle erosion, the surface of electrochemically processed titanium alloy had serious pitting corrosion due to the influence of the passivation film, and the surface roughness (S_a) of the local area reached 10.088 µm. Under the condition of a high-impact angle (90°), due to the existence of strain hardening and particle embedding, only the edge of the surface is dissolved, while the central area is almost insoluble, with the surface roughness (S_a) reaching 16.086 µm. On the contrary, under the condition of a low-impact angle (15°), the machining efficiency and surface quality of the material were significantly improved due to the ploughing effect and galvanic corrosion, and the surface roughness (S_a) reached 2.823 µm. Based on these findings, the electrochemical dissolution model of TA15 titanium alloy under different particle erosion conditions was established.

This study investigated enhancing the wear resistance of Ti6Al4V alloys for medical applications by incorporating TiC nano-reinforcements using advanced spark plasma sintering (SPS). The addition of up to 2.5wt% TiC significantly improved the mechanical properties, including a notable 18.2% increase in hardness (HV 332). Fretting wear tests against 316L stainless steel (SS316L) balls demonstrated a 20wt%–22wt% reduction in wear volume in the Ti6Al4V/TiC composites compared with the monolithic alloy. Micro-structural analysis revealed that TiC reinforcement controlled the grain orientation and reduced the β-phase content, which contributed to enhanced mechanical properties. The monolithic alloy exhibited a Widmanstätten lamellar microstructure, while increasing the TiC content modified the wear mechanisms from ploughing and adhesion (0–0.5wt%) to pitting and abrasion (1wt%–2.5wt%). At higher reinforcement levels, the formation of a robust oxide layer through tribo-oxide treatment effectively reduced the wear volume by minimizing the abrasive effects and plastic deformation. This study highlights the potential of SPS-mediated TiC reinforcement as a transformative approach for improving the performance of Ti6Al4V alloys, paving the way for advanced medical applications.

TiB₂ coatings can significantly enhance the high-temperature oxidation resistance of molybdenum, which would broaden the application range of molybdenum and alloys thereof. However, traditional methods for preparing TiB₂ coatings have disadvantages such as high equipment costs, complicated processes, and highly toxic gas emissions. This paper proposes an environmentally friendly method, which requires inexpensive equipment and simple processing, for preparing TiB₂ coating on molybdenum via electrophoretic deposition within Na₃AlF₆-based molten salts. The produced TiB₂ layer had an approximate thickness of 60 µm and exhibited high density, outstanding hardness (38.2 GPa) and robust adhesion strength (51 N). Additionally, high-temperature oxidation experiments revealed that, at 900°C, the TiB₂ coating provided effective protection to the molybdenum substrate against oxidation for 3 h. This result indicates that the TiB₂ coating prepared on molybdenum using molten salt electrophoretic deposition possesses good high-temperature oxidation resistance.

Oxide dispersion strengthened (ODS) alloys are extensively used owing to high thermostability and creep strength contributed from uniformly dispersed fine oxides particles. However, the existence of these strengthening particles also deteriorates the processability and it is of great importance to establish accurate processing maps to guide the thermomechanical processes to enhance the formability. In this study, we performed particle swarm optimization-based back propagation artificial neural network model to predict the high temperature flow behavior of 0.25wt% Al₂O₃ particle-reinforced Cu alloys, and compared the accuracy with that of derived by Arrhenius-type constitutive model and back propagation artificial neural network model. To train these models, we obtained the raw data by fabricating ODS Cu alloys using the internal oxidation and reduction method, and conducting systematic hot compression tests between 400 and 800°C with strain rates of 10⁻²–10 s⁻¹. At last, processing maps for ODS Cu alloys were proposed by combining processing parameters, mechanical behavior, microstructure characterization, and the modeling results achieved a coefficient of determination higher than >99%.

The outstanding performance of O3-type NaNi_1/3Fe_1/3Mn_1/3O₂ (NFM111) at both high and low temperatures coupled with its impressive specific capacity makes it an excellent cathode material for sodium-ion batteries. However, its poor cycling, owing to high-pressure phase transitions, is one of its disadvantages. In this study, Cu/Ti was introduced into NFM111 cathode material using a solid-phase method. Through both theoretically and experimentally, this study found that Cu doping provides a higher redox potential in NFM111, improving its reversible capacity and charge compensation process. The introduction of Ti would enhance the cycling stability of the material, smooth its charge and discharge curves, and suppress its high-voltage phase transitions. Accordingly, the NaNi_0.27Fe_0.28Mn_0.33Cu_0.05Ti_0.06O₂ sample used in the study exhibited a remarkable rate performance of 142.97 mAh·g⁻¹ at 0.1C (2.0–4.2 V) and an excellent capacity retention of 72.81% after 300 cycles at 1C (1C = 150 mA·g⁻¹).

Heteroatom-doped carbon is considered a promising alternative to commercial Pt/C as an efficient catalyst for the oxygen reduction reaction (ORR). This study presents the synthesis of iron-loaded, sulfur and nitrogen co-doped carbon (Fe/SNC) via in situ incorporation of 2-aminothiazole molecules into zeolitic imidazolate framework-8 (ZIF-8) through coordination between metal ions and organic ligands. Sulfur and nitrogen doping in carbon supports effectively modulates the electronic structure of the catalyst, increases the Brunauer–Emmett–Teller surface area, and exposes more Fe–N_x active centers. Fe-loaded, S and N co-doped carbon with Fe/S molar ratio of 1:10 (Fe/SNC-10) exhibits a half-wave potential of 0.902 V vs. RHE. After 5000 cycles of cyclic voltammetry, its half-wave potential decreases by only 20 mV vs. RHE, indicating excellent stability. Due to sulfur’s lower electronegativity, the electronic structure of the Fe–N_x active center is modulated. Additionally, the larger atomic radius of sulfur introduces defects into the carbon support. As a result, Fe/SNC-10 demonstrates superior ORR activity and stability in alkaline solution compared with Fe-loaded N-doped carbon (Fe/NC). Furthermore, the zinc–air battery assembled with the Fe/SNC-10 catalyst shows enhanced performance relative to those assembled with Fe/NC and Pt/C catalysts. This work offers a novel design strategy for advanced energy storage and conversion applications.

This study focused on improving the cathode performance of Ba_0.6Sr_0.4Co_0.85Nb_0.15O_3−δ (BSCN)-based perovskite materials through molybdenum (Mo) doping. Pure BSCN and Mo-modified-BSCN—Ba_0.6Sr_0.4Co_0.85Nb_0.1Mo_0.05O_3−δ (BSCNM_0.05), Ba_0.6Sr_0.4Co_0.85Nb_0.05Mo_0.1O_3−δ (BSCNM_0.1), and Ba_0.6Sr_0.4Co_0.85Mo_0.15O_3−δ (BSCM)—with Mo doping contents of 5mol%, 10mol%, and 15mol%, respectively, were successfully prepared using the sol–gel method. The effects of Mo doping on the crystal structure, conductivity, thermal expansion coefficient, oxygen reduction reaction (ORR) activity, and electrochemical performance were systematically evaluated using X-ray diffraction analysis, thermally induced characterization, electrochemical impedance spectroscopy, and single-cell performance tests. The results revealed that Mo doping could improve the conductivity of the materials, suppress their thermal expansion effects, and significantly improve the electrochemical performance. Surface chemical state analysis using X-ray photoelectron spectroscopy revealed that 5mol% Mo doping could facilitate a high adsorbed oxygen concentration, leading to enhanced ORR activity in the materials. Density functional theory calculations confirmed that Mo doping promoted the ORR activity in the materials. At an operating temperature of 600°C, the BSCNM_0.05 cathode material exhibited significantly enhanced electrochemical impedance characteristics, with a reduced area specific resistance of 0.048 Ω·cm², which was lower than that of the undoped BSCN matrix material by 32.39%. At the same operating temperature, an anode-supported single cell using a BSCNM_0.05 cathode achieved a peak power density of 1477 mW·cm⁻², which was 30.71%, 56.30%, and 171.50% higher than those of BSCN, BSCNM_0.1, and BSCM, respectively. The improved ORR activity and electrochemical performance of BSCNM_0.05 indicate that it can be used as a cathode material in low-temperature solid oxide fuel cells.

Biochar and biochar composites are versatile materials that can be used in many applications. In this study, biochar was prepared from sawdust and combined with the yttrium iron garnet (YIG) nanocrystal to investigate the shielding effectiveness of the composite structure. Firstly, the effect of the pyrolysis temperature on the shielding effectiveness of biochar was investigated. Secondly, biochars combined with YIG nanocrystals with different contents and shielding effectiveness of the composites were investigated. The electromagnetic effectiveness of the samples was investigated within the X band (8–12 GHz). The findings indicate that biochar demonstrates enhanced absorption properties with elevated pyrolysis temperatures. Biochars demonstrated an approximate 40 dB shielding effectiveness, while YIG exhibited approximately 7 dB, corresponding to absorption at 8 GHz. However, the combination of biochar and YIG exhibited exceptional absorption, reaching 67.12 dB at 8 GHz.

The electromagnetic wave absorption of silicon carbide nanowires is improved by their uniform and diverse cross-structures. This study introduces a sustainable and high value-added method for synthesizing silicon carbide nanowires using lignite and waste silicon powder as raw materials through carbothermal reduction. The staggered structure of nanowires promotes the creation of interfacial polarization, impedance matching, and multiple loss mechanisms, leading to enhanced electromagnetic absorption performance. The silicon carbide nanowires demonstrate outstanding electromagnetic absorption capabilities with the minimum reflection loss of −48.09 dB at 10.08 GHz and an effective absorption bandwidth (the reflection loss less than −10 dB) ranging from 8.54 to 16.68 GHz with a thickness of 2.17 mm. This research presents an innovative approach for utilizing solid waste in an environmentally friendly manner to produce broadband silicon carbide composite absorbers.

MnO_x–CeO₂ catalysts for the low-temperature selective catalytic reduction (SCR) of NO remain vulnerable to water and sulfur poisoning, limiting their practical applications. Herein, we report a hydrophobic-modified MnO_x–CeO₂ catalyst that achieves enhanced NO conversion rate and stability under harsh conditions. The catalyst was synthesized by decorating MnO_x crystals with amorphous CeO₂, followed by loading hydrophobic silica on the external surfaces. The hydrophobic silica allowed the adsorption of NH₃ and NO and diffusion of H, suppressed the adsorption of H₂O, and prevented SO₂ interaction with the Mn active sites, achieving selective molecular discrimination at the catalyst surface. At 120°C, under H₂O and SO₂ exposure, the optimal hydrophobic catalyst maintains 82% NO conversion rate compared with 69% for the unmodified catalyst. The average adsorption energies of NH₃, H₂O, and SO₂ decreased by 0.05, 0.43, and 0.52 eV, respectively. The NO reduction pathway follows the Eley–Rideal mechanism, NH₃* + * → NH₂* + H* followed by NH₂* + NO* → N₂* + H₂O*, with NH₃ dehydrogenation being the rate determining step. Hydrophobic modification increased the activation energy for H atom transfer, leading to a minor decrease in the NO conversion rate at 120°C. This work demonstrates a viable strategy for developing robust NH₃-SCR catalysts capable of efficient operation in water- and sulfur-rich environments.