This review provides a systematic analysis of metal-supported solid oxide fuel cells (MS-SOFCs) as next-generation energy conversion devices. By integrating multiscale simulations with experimental validation, we establish performance benchmarks for key components, including thin electrolytes, mixed ionic–electronic conductors (MIECs) used as cathodes, and corrosion-resistant metal substrates. The paper elucidates critical degradation pathways, such as chromium poisoning and interfacial instability, and proposes mitigation strategies based on advanced manufacturing techniques, including plasma spraying and in situ sintering. System-level challenges related to thermal management, gas transport optimization, and scalable production are identified, ultimately delineating research priorities for achieving sub-600°C operation and commercial deployment.

Aqueous Zn metal batteries (AZMBs) have emerged as promising energy-storage systems owing to their inherent safety, environmental compatibility, and cost-effectiveness. However, their practical application is severely hindered by critical challenges pertaining to Zn anodes, including uncontrolled dendrite growth and parasitic side reactions at this anode. Although employing excess Zn foil can mitigate anode failure, this strategy inevitably compromises the energy density of full batteries. Recent advances have demonstrated that current collector design coupled with controlled electrodeposition can generate high-quality Zn deposits, which effectively suppress dendrite formation and side reactions. Carbon-based materials featuring favorable electrical conductivities, tunable architecture, and exceptional chemical stabilities have shown unique advantages in constructing/modifying current collectors. This review systematically summarizes the recent progress in the design of carbon-based current collectors for AZMBs, categorizing their functional roles and elucidating the structure–performance relationships that govern Zn deposition behaviors. Mechanistic insights into how carbon materials regulate the Zn plating/stripping processes are provided. Finally, future research directions are proposed to guide the development of advanced current collectors for high-performance AZMBs.

Carbon materials, characterized by diverse allotropes, have played critical roles in the advancement of human civilization and industrial manufacturing. As a prominent allotrope, two-dimensional (2D) graphene materials have attracted increasing attention since their discovery owing to their exceptional properties; however, they suffer from the fundamental challenges of restacking and agglomeration, which diminish their performance in practical applications. The design of three-dimensional (3D) frameworks composed of 2D graphene sheets is considered an effective strategy to resolve these issues and enable the efficient utilization of the properties of graphene. Compared with conventional fabrication methods, such as graphene oxide assembly and template-assisted chemical vapor deposition, the chemical blowing strategy is distinguished by its low cost, facile process, and superior controllability. Despite these advantages, few review articles have focused specifically on the fabrication of 3D graphene materials via chemical blowing. This review outlines the chemical blowing strategy and clarifies the fundamentals of the blowing process, its historical evolution, and the classification of 3D graphene materials. Subsequently, the recent progress in 3D graphene foams and powders fabricated via chemical blowing is detailed, with an emphasis on the underlying synthesis chemistry. Following an analysis of the correlation between 3D graphene foam and powder materials, their design considerations and functional applications are discussed. This discussion provides recommendations for the synthesis of specific 3D graphene materials and elucidates their differences and commonalities across various application scenarios. Finally, after a brief summary, current challenges, opportunities, and future research directions for the development of chemical blowing are proposed.

Direct seawater electrolysis presents a promising pathway for sustainable “green hydrogen” production. However, the complex composition of seawater, particularly the presence of chloride ions (Cl⁻), poses significant challenges to the structural stability and electrocatalytic performance of oxygen evolution reaction (OER) catalysts. Although recent studies have demonstrated that anion modification can improve the stability and activity of catalysts, the extent of these improvements varies considerably across different anions, and the underlying mechanisms remain poorly understood. This review examines the electrochemical behavior of anions related to their physicochemical properties and provides a comprehensive overview of recent advances and remaining challenges in anion-oriented strategies for seawater electrolysis. First, we propose a novel framework for determining anion properties based on adsorption energy, ionic potential, and acid-base character, which evaluates the physicochemical properties of anions from three dimensions and serves as a guideline for selecting modification materials for catalysts. Second, we critically discuss the underlying mechanisms by which anion modification enhances OER stability and activity in seawater, with a focus on chlorine chemistry and oxygen evolution dynamics. Classical approaches for stability improvement, such as the introduction of external anions and the regulation of Cl⁻ and hydroxide ions (OH⁻), are discussed. We also summarize mechanisms for activity enhancement, including electronic structure modulation, active species engineering, and mass transfer optimization. Finally, we outline future research directions for anion modification strategies and highlight persistent challenges.

Market demand for cold storage systems, a critical component of modern cold-chain logistics, is rapidly expanding. Phase change cold technology offers a low-carbon route for energy savings in cold storage systems and is now a major research focus. This paper outlines the application of cold storage technology, systematically reviews the characteristics of three types of phase change materials (PCMs), and provides a comparative analysis of their advantages and disadvantages. It elaborates on key techniques for enhancing material properties and details the primary encapsulation strategies to address engineering challenges. This study categorizes cold storage systems into active and passive types, and identifies refrigeration units and external heat ingress as the primary sources of energy consumption. Based on this classification, three specific application scenarios for integrating PCMs into cold storage systems are clearly outlined. This study provides a comprehensive summary and analysis of the current development prospects and challenges of phase change cold storage technology. It proposes that future research should prioritize the development of high-performance PCMs, optimization of cold storage panel arrangements, and studies on system-level integration.

In order to improve excavation efficiency, we considered coal mine rock roadway blasting excavation as a background to examine the influence of delay time on the blasting effect of different charging structures under single free-surface conditions. Single-pore dispersed-charge models, dual-pore continuous-charge models, and dual-pore composite charge models were established. Their respective explosive rock-breaking mechanisms were explained using different models. These three numerical models were used to analyze the influence of delay time changes on the pressure and velocity of the measurement points near boreholes. The models were used to evaluate the blasting effects by determining the number of free-surface rocks. Engineering experiments were conducted to validate the numerical findings. The results showed that a single-hole dispersed charge creates a cavity and a new free surface, which increases the impact of deep-hole blasting compared to stress wave superposition. Dual-hole continuous-charge detonation is difficult, and short-delay detonation can effectively use stress wave superposition and prolong the action time of the explosive gas. The cavities created by the dispersed charges can be used to increase the efficiency of dual-hole composite charge blasting.

This study aimed to elucidate the influence of thermal decomposition under an inert atmosphere on the phase composition, microstructure, and flotation performance of bastnaesite. Experiments showed that decomposition was strongly temperature-dependent. After complete decomposition, the release of CO₂ increased the rare earth oxide grade from 72.90wt% to 86.83wt%, accompanied by an increase in the Ce oxidation degree. Major decomposition products included rare earth oxyfluoride (REOF), rare earth trifluoride (REF₃), and Ce₇O₁₂, with some products showing low crystallinity. The decomposition damaged the particle structure, resulting in the extensive lamellar cracking, a significant increase in specific surface area, and partial fragmentation of the particles. Flotation tests revealed that optimum recovery was achieved at pH 8.00–9.00. However, thermal decomposition increased the initial pulp pH to almost 11.00, making pH adjustment difficult. Salicylhydroxamic acid (SHA) was adsorbed on the surfaces by both physical and chemical interactions, with chemical adsorption being significantly enhanced after decomposition. During flotation, SHA was distributed not only on particle surfaces but also in internal pores after decomposition. Due to the phase and microstructural changes, the required dosage of SHA increased from 20 mg·L⁻¹ for the raw ore to 250 mg·L⁻¹. These results provide insights into the development of reagents suitable for the flotation of bastnaesite roasting products.

Conventional lime depressants used in copper sulfide flotation separation are limited by persistent challenges of scaling, corrosion, and compromised target-metal recovery, which necessitates the development of efficient and green alternatives. This study demonstrates the synergistic depression of pyrite by H₂O₂/Fe³⁺ under low-alkalinity conditions. The complementary action pathways were systematically elucidated by multiscale characterization techniques including mono- and mixed-mineral flotation tests and surface and solution analysis. Flotation test results showed that the combined depressant system H₂O₂/Fe³⁺ enabled efficient separation of chalcopyrite and pyrite. Under the optimal mixed-mineral separation conditions of 0.025vol% H₂O₂ and 2 × 10⁻⁵ mol/L Fe³⁺, the artificial mixed-mineral flotation test yielded a copper concentrate with a chalcopyrite grade of 30.5lwt%, while chalcopyrite recovery remained stably above 88%. Investigations into the depression mechanism and surface hydrophobicity revealed that H₂O₂ selectively oxidized disulfide (S₂²⁻) to sulfate (SO₄²⁻) while facilitating Fe²⁺ conversion to Fe³⁺, generating hydrophilic Fe–SO₄/Fe–OOH/Fe–OH coatings that disrupted natural surface natural hydrophobicity. Simultaneously, Fe³⁺ hydrolyzed to hydroxyl complexes ([Fe(OH)₂]⁺ and Fe(OH)₃), which electrostatically adsorbed onto and chemically bonded to H₂O₂-oxidized pyrite surfaces, forming dense hydrophilic layers. The faster oxidation of pyrite resulted from its fundamental structural properties, specifically its high surface electronic activity and relatively weak Fe–S bonds, which collectively rendered it more susceptible to H₂O₂ attack, unlike chalcopyrite with its stable lattice and strong covalent Cu–S bonds. Consequently, the robust covalent Cu–S bonds of chalcopyrite effectively resisted oxidation, while its limited Fe³⁺ adsorption capacity favored the adsorption of sodium ethyl xanthate (SEX) at copper-active sites. As a result, the H₂O₂/Fe³⁺ system exerted only minimal depression on chalcopyrite, providing a sound theoretical basis and a practical technical strategy for the selective separation of copper-sulfide ores. Furthermore, the findings of this study contribute to the development of low-alkalinity, high-selectivity sulfide-ore processing methods, demonstrating considerable potential for industrial application.

This study explores the selective application of an environmentally friendly organic inhibitor, ellagic acid (EA), in the flotation separation of galena from chalcopyrite. Single-mineral flotation experiments revealed that the galena flotation recovery significantly decreased from approximately 95% to 6.16% following the addition of EA, whereas chalcopyrite maintained a high recovery of approximately 95%. In artificial mixed-ore flotation, effective separation was achieved under optimized conditions. This yielded a Cu concentrate with a Cu grade of 29.73% and recovery of 95.67%, and a Pb grade of 8.39% with a recovery of 8.62%, resulting in a separation index of 15.30. Comprehensive analyses were conducted using various techniques including X-ray photoelectron spectroscopy, density functional theory calculations, zeta potential measurements, time-of-flight secondary ion mass spectrometry, xanthate adsorption capacity measurements, and contact angle measurements. These analyses suggested that selective EA adsorption on the galena surface is the key mechanism underlying the separation. Under alkaline conditions, the deprotonated phenolic hydroxyl groups in EA preferentially interact with Pb sites on galena, increasing the surface hydrophilicity via the formation of –OH groups, oxides, and sulfur oxides. These interactions effectively occupied the active sites on the galena surface, inhibiting xanthate adsorption. However, EA exhibited minimal influence on the surface chemistry of chalcopyrite and its interaction with collectors, thereby enhancing the wettability difference between the two minerals. These results demonstrated the feasibility of effectively separating these minerals using the proposed approach.

The development of effective adsorbents is crucial for the sustainable recovery of noble metals from secondary resources. In this study, MIL-101(Cr)-Tu, a Cr-based metal–organic framework (MOF) modified with thiourea, was successfully prepared by double-phase encapsulation followed by post-synthetic modification. At 298 K, the largest adsorption capacities of Au(III), Pd(II), and Pt(IV) by MIL-101(Cr)-Tu were (1230.83 ± 8.72), (330.41 ± 7.29), and (315.58 ± 13.67) mg·g⁻¹, respectively. The uptakes of Au(III), Pd(II), and Pt(IV) increased with temperature. The adsorption data fit well to both the Langmuir and pseudo-second-order kinetic models, indicating that the adsorption process conformed to monolayer adsorption and chemisorption. Mechanistic analysis revealed that the amino and thiourea groups facilitated the adsorption of noble metals through coordination and electrostatic attraction. Notably, Au(III), Pd(II), and Pt(IV) interacted strongly with the thiourea groups owing to their “soft–soft” interactions. Moreover, Au(III) was reduced to Au(I) and Au(0), Pt(IV) was reduced to Pt(II), and the amino groups were oxidized to NO₂. The adsorption of Pd(II) did not involve redox reactions. Additionally, MIL-101(Cr)-Tu exhibited strong selectivity for noble metals and excellent reusability, demonstrating its great potential for the extraction of noble metals from secondary resources.

Thiosulfate gold extraction technology has gained considerable interest owing to its environmental compatibility and broad applicability to diverse ore types. However, the lack of efficient methods to recover Au(S₂O₃)₂³⁻ from leaching solutions has hindered its industrial implementation. To address this challenge, this study used a thiosulfate leaching solution from quartz-type gold ores to design an activated carbon-coated titanium electrode (Ti@AC) with a porous surface structure. This electrode facilitated the direct reduction of low-concentration Au(S₂O₃)₂³⁻ to metallic gold (Au⁰) in solution, achieving a gold recovery of 99.58%, surpassing other recovery methods from the leaching solution by 30%–80%. After ten consecutive 200 L pilot-scale tests, the Ti@AC electrode demonstrated remarkable stability, consistently maintaining a recovery >98% and yielding 20.23 g of gold. The porous architecture of the activated carbon (AC) promoted the adsorption of low-concentration Au(S₂O₃)₂³⁻, while its low charge transfer resistance facilitated the efficient conversion of Au(S₂O₃)₂³⁻ to Au⁰. Moreover, the reduction reaction generated a concentration gradient near the cathode, promoting the diffusion of Au(S₂O₃)₂³⁻ toward the electrode and ensuring an efficient recovery process. This study provides a feasible strategy for the direct reduction of low-concentration precious metal ions to monomers with high recovery and low costs, which is promising for industrial applications.

In this study, a porous porphyrin-based metal–organic framework/reduced graphene oxide ((Fe–P)_n–MOF/graphene) composite was prepared via hydrothermal reduction with tetracarboxyphenyl porphyrin (TCPP) and iron(III) chloride (FeCl₃) as the main raw materials. The composite was designed to serve as a selective adsorbent for yttrium ions (Y³⁺). The adsorption performance of the composite toward Y³⁺ was investigated. The results indicated that the maximum adsorption capacity of the composite was 102.1 mg/g. The adsorption process followed the quasi-second-order kinetic and Langmuir isotherm models, indicating a monolayer chemical adsorption mechanism. The composite material was comprehensively characterized to analyze its adsorption mechanism. Using density functional theory (DFT) calculations, the electrostatic potential distribution in (Fe–P)_n–MOF and the binding energies of its adsorption sites toward metal ions were simulated to further determine the Y³⁺ adsorption mechanism of (Fe–P)_n–MOF. The composite demonstrated excellent selective adsorption of Y³⁺ from rare-earth leaching solutions and maintained a recovery rate exceeding 90% even after more than five regeneration cycles. Thus, (Fe–P)_n–MOF/graphene is a promising Y³⁺ adsorbent.

The compressive strength of oxidized pellets is a key indicator for evaluating pellet quality and stability. Accurate prediction of its variation trend is essential for improving production efficiency and optimizing process parameters. However, due to the high dimensionality and strong nonlinearity of compressive strength prediction, existing models still face limitations in terms of reliability, applicability, and generalization. This study proposes the metallurgical-random forest-based Bayesian optimized bidirectional gated recurrent unit (BiGRU) attention prediction model (MRF-BBAPM) model, which employs feature selection guided by metallurgical mechanisms and random forest to enhance model efficiency and relevance. The BiGRU network parameters are optimized using Bayesian optimization, and an attention mechanism is incorporated to focus on critical features, further improving model performance. The SHapley Additive exPlanations (SHAP) method is introduced to quantify the contribution of each feature to the prediction results, revealing the model’s decision-making process and enhancing its interpretability and reliability. The model also incorporates a self-learning mechanism that automatically updates and optimizes itself based on weekly prediction errors. Experimental results show that the proposed model achieves a mean absolute error of 80.58 N (2.77% of the mean) and a root mean square error of 95.75 N (3.29% of the mean) in predicting pellet compressive strength, demonstrating strong stability and reliability in real-world applications. This method provides effective data support for accurate prediction of pellet compressive strength and informed decision-making in production.

As demand grows for low-carbon ironmaking, it is essential to understand how hydrogen reduces iron ore pellets under varying gangue compositions and gas atmospheres. In this work, fired hematite pellets with a basicity (mass ratio of CaO to SiO₂) of 0.3 and SiO₂ contents ranging from 1wt% to 4wt% were systematically investigated under three typical shaft furnace atmospheres (Midrex, HYL, and coke oven gas (COG)) as well as under 100% H₂, to clarify the reduction kinetics, reaction mechanism, and microstructural evolution of the fired pellets. The results indicate that a higher hydrogen proportion significantly accelerates the reduction rate of the fired pellets, while an increase in SiO₂ content generally leads to a decrease in the overall reaction rate. However, the effect of hydrogen concentration on the reduction behavior of the fired pellets varied markedly with their silicon content. For the fired pellets containing 1wt% and 2wt% SiO₂, an increase in hydrogen concentration causes deterioration in reduced pellet characteristics, as evidenced by the increase in reduction swelling index from 26.14% to 34.26% and the decrease in cold compressive strength from 110 to 78 N. In contrast, fired pellets with 3wt% and 4wt% SiO₂ exhibit the opposite trend, with the reduction swelling index decreasing from 15.26% to 9.23% and cold compressive strength improving from 179 to 271 N. Kinetics analysis indicates that under 100% H₂, the reduction of fired pellets with 1wt% SiO₂ is governed by a mixed gas-diffusion and uniform reaction model, whereas fired pellets with 4wt% SiO₂ follow an unreacted core model. These differences in reduction kinetics, reduction behavior, and post-reduction properties are closely associated with the formation of more Al-bearing calcium silicate slag phases in high-SiO₂ reduced pellets, which strengthen intergranular bonding, buffer phase-transformation-induced stress, and promote the evolution of metallic iron from whisker-like to granular or layered morphologies.

As the sole solid material in the lower part of a blast furnace (BF), the multiphase reaction behavior of coke within the dead-man region of the hearth is of significant theoretical and practical importance for carbon emission control and low-carbon production. The multidimensional characterization of the occurrence state, multiphase reaction behavior, and renewal mechanism of deadman coke in the hearth was performed through the dissection of a 3200-m³ BF, combining various methods such as rope-sawing residual iron removal, image processing techniques, microscopic analysis, and coke dissolution experiments. The results showed that the deadman root in the hearth exhibited a “curved” shape and a distinct ‘floating’ state, with the floating height at the center approximately 0.45 m, increasing toward the hearth edge. Vertically, the deadman was divided into three regions: the “slag–coke zone,” the “iron–coke zone,” and the “coke–free zone.” The average deadman voidage was calculated to be 54.75% (±0.85%), and the coke particle size was 21.47 mm (± 0.53 mm), based on image processing techniques. A “slag + CaS isolation layer” was identified on the outer surface of the deadman coke during the dissolution and erosion process of hot metal analysis of the “slag–iron–coke” three-phase microstructure indicated that this layer suppressed the carburization reaction and represented the most limiting factor for deadman coke renewal. A coke renewal formula was established from the coke dissolution experiments, and the renewal time was calculated to be 14.74 d. The renewal mechanism of deadman coke was elucidated through comprehensive analysis, and recommendations for low-carbon operation were proposed. These findings provide a scientific basis and theoretical guidance for low-carbon production and fuel optimization in ironmaking blast furnaces.

The utilization of bauxite-vitrified argon–oxygen decarburization (AOD) slag as a supplementary cementitious material is explored as an alternative approach for recycling unmanageable AOD slag and reducing the CO₂ emission levels. The results demonstrate that AOD slag can be effectively vitrified by incorporating 15wt% bauxite as the alumina source, facilitating the formation of a stable glass phase. The resulting vitrified product exhibits excellent properties, such as initial and final setting times of 337 and 437 min, respectively, good soundness (<0.05 mm), and a compressive strength of 88.7 MPa. In particular, the robust cementitious matrix effectively encapsulates and immobilizes the Cr ions, thus reducing the total chromium leaching concentration to 0.09 mg/L, which is significantly below the regulatory limit (0.15 mg/L) specified by the HJ/T 301–2007 industrial standard. The leached Cr³⁺ ions can be easily oxidized in a liquid environment with a pH > 11 at a positive oxidation–reduction potential. The carbonation reaction inhibits the conversion from trivalent chromium to hexavalent chromium, thereby lowering its concentration. These findings suggest that vitrifying AOD slag using Al₂O₃-rich solid waste as a supplementary cementitious material is a promising and environmentally sustainable method.

Tin slag is a problematic residue with high economic value that is produced during the refining process of crude tin. Due to the highly refractory nature of these materials, the industrial extraction of the metals contained in the matrix depends on leaching with hydrofluoric acid, which poses problems in the handling and safety of materials. To minimize the effects related to the processing of these materials, a theoretical and experimental investigation was carried out to develop a process through thermal treatment, followed by aqueous or oxidative leaching to obtain a Pregnant Leach Solution (PLS) with the target metals. Here, an approach based on a thermodynamic simulation model is proposed to evaluate the optimal conditions for the maximum extraction of the Nb–Ta and Zr–Hf systems. The experimental results revealed that thermodynamic simulations allowed the identification of the ideal conditions for the formation of sulfates at 200°C, with a slag–H₂SO₄ ratio (g/mL) of 1:4 in a treatment time of 6 h, followed by two leaching routes. Aqueous leaching with a solid–liquid (S/L) ratio (g/mL) of 1:10 at 90°C for 2 h resulted in a recovery of 97% Nb, 61% Ta, 62% Zr, and 75% Hf. Meanwhile, oxidative leaching with a 0.5 M concentration, S/L ratio of 1:10 at 90°C for 2 h, resulted in a recovery of 81% Nb, 99% Ta, 98% Zr, and 99% Hf, confirming the efficiency of the process. This study highlights the importance of thermal treatment, thermodynamic modeling, water content, and the addition of oxidants in effective extraction, providing a pathway to optimize the recovery of economically valuable metals from tin slags.

Accurate modeling of material constitutive relationships under compositional fluctuations poses significant challenges. Traditional mechanism-driven methods struggle to capture the complex nonlinear behavior of material properties as composition varies, while data-driven deep learning approaches, despite their high accuracy and robustness, lack strict constraints from physical metallurgical mechanisms, often leading to substantial prediction deviations. To address this critical issue, this study proposes a multimodal deep learning model based on an encoder-decoder framework, integrating physical metallurgical theory with machine learning to achieve high-precision prediction of material constitutive relationships under complex loading and compositional fluctuations. Firstly, the performance of three encoder architectures, long short-term memory (LSTM), gated recurrent unit (GRU), and temporal convolutional network (TCN), was systematically compared, with the TCN encoder-based model demonstrating the best performance, achieving mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (R) values of 1.84 MPa, 2.75 MPa, 4.48%, and 0.9918, respectively. By comparing with purely data-driven models, the critical guiding role of physical metallurgy in the deep learning process was validated. Furthermore, the accuracy and generalizability of the proposed model in material processing scenarios were verified by embedding it into a numerical simulation framework and applying it to out-of-domain data. Finally, the influence of elemental content on material mechanical properties was analyzed using the developed model. This study provides an efficient and reliable alternative method for obtaining material constitutive relationships, avoiding the high costs associated with traditional experimental approaches, and offering potential for computer-aided material design and process optimization for material processing.

Al–Mg–Si alloys are widely employed in automotive vehicles; however, challenges such as cracking often arise during the hemming process (180° bending). Based on the molecular dynamics simulations and experiments, this study investigated the effects of the size and number of MgSi(Fe) clusters on the mechanical properties of 6xxx Al alloys. The results showed that medium-sized MgSi clusters (containing 10–19 atoms) at the grain boundaries (GBs) enhanced the strength of the GBs, effectively inhibiting crack initiation and significantly suppressing intergranular cracking. In addition, the ductility and brittleness of the model with the Fe-containing phase were significantly affected by the Si/Fe atomic ratio (∼0.71). Tensile experiments confirmed that the failure morphology exhibited a distinct brittle fracture when the Si/Fe atomic ratio of the phase was 0.90. The pre-aging treatment promoted the dispersion of solute atoms, thereby reducing the yield strength of the AA6016 Al alloy to ∼120 MPa, which improved its hemming performance. Furthermore, preaging facilitated the generation of finer Mg–Si phases at the GBs during bake hardening.

Although Sn has been established as an effective microalloying element for suppressing the negative natural aging (NA) effect in Al–Mg–Si alloys, its potential to mitigate the negative NA effect in Al–Mg–Si–Cu alloys remains to be confirmed. This study systematically investigated the role of Sn in the NA of Al–Mg–Si–Cu alloys through hardness measurements, differential scanning calorimetry, and atomic-resolution high-angle annular dark-field scanning transmission electron microscopy. Our results demonstrate that the addition of Sn significantly suppresses the adverse impact of NA on the peak-aged hardening capacity during subsequent artificial aging and substantially alleviates early-stage hardening kinetics degradation. Our findings suggest that Sn modifies the nature of the NA clusters in Al–Mg–Si–Cu alloys. A significant proportion of NA clusters in the Sn-added alloy effectively served as heterogeneous nucleation sites for strengthening the precipitates during artificial aging, thereby preserving the precipitate nucleation rates and preventing coarsening at the peak-aging stage. Atomic-resolution energy-dispersive X-ray spectroscopy revealed preferential occupation of Si atomic sites by Sn atoms within the β′ and C/Q′ phases. This investigation provides critical theoretical insights for optimizing alloy design in automotive-body aluminum applications.

Corrosion rates of biodegradable Zn alloys are directly related to their post-implantation safety and effectiveness. However, highly accurate and interpretable “white-box” machine learning models for predicting their corrosion rates remain largely unexplored. This study proposes a data-driven method coupled with accelerated corrosion testing for predicting the corrosion rates of biodegradable Zn–0.45Mn–0.2Mg (wt%) alloy. A symbolic regression (SR) machine-learning model was established based on an analytical expression of the corrosion rate and four corrosion parameters. Outperforming five other machine-learning models, the SR model achieved a determination coefficient of 0.97 and prediction errors in the verification experiments of less than 10%. This study contributes to a paradigm shift from qualitative to quantitative analysis for corrosion research on biodegradable metals.

In this study, vacuum laser-engineered directed energy deposition (V-LDED) was employed to fabricate CoCrFeNiTi_x (x = 0.1, 0.2, 0.3) high-entropy alloys (HEAs) by strategically mixing equiatomic pre-alloyed CoCrFeNi and CoCrFeNiTi powders. With increasing Ti content, the lattice distortion of the HEAs intensified, grains were refined, and precipitate content increased; however, the face-centered cubic (FCC) structure remained the predominant structure. The strength and plasticity of the HEAs initially increased and then decreased with the addition of Ti. The CoCrFeNiTi_0.3 (Ti0.3) alloy exhibited the best mechanical properties, with a tensile yield strength (TYS) of 604 MPa, an ultimate tensile strength (UTS) of 882 MPa, and a plastic elongation of 13.5%. Compared to the Ti-free alloy, the TYS and UTS were increased by 124% and 83%, respectively. The CoCrFeNiTi_0.2 (Ti0.2) alloy showed the best corrosion resistance with the corrosion potential (E_corr), corrosion current density (I_corr), passivated film resistor (R_c), and charge transfer resistance (R_ct) values of −0.208 V, 4.889 × 10⁻⁷ A/cm², 7.03 × 10³ Ω/cm², and 8.50 × 10⁵ Ω/cm², respectively. The addition of Ti increased the Cr and Ti contents in the passive film, which are easily passivated elements. The multiple effects of Ti on the corrosion resistance were mainly attributed to the formation and composition of the passive film and density of the precipitates.

We introduce an innovative perovskite solar cell (PSC) architecture featuring a multifunctional active layer (AL) of FA_0.85MA_0.15PbI₃ (FA: formamidine, MA: methylamine) integrated with two dimensional (2D) transition metal carbides (TiC/WC). The properties of the chemically reduced WC and TiC were thoroughly validated through structural and morphological analyses. This design significantly enhances the conventional charge-transporting properties of the AL by utilizing conductive carbide networks (conductivity (σ) > 1200 S/cm) and achieving defect passivation at grain boundaries (reducing trap density from 10¹⁶ to 10¹⁴ cm⁻³), along with providing intrinsic stability against environmental stressors. Devices constructed with the AL@WC configuration achieved a remarkable power conversion efficiency (PCE) of 24.25%, surpassing both the TiC-incorporated devices (23.74%) and the pristine AL devices (22.56%). Significantly, after 800 h in ambient air conditions (40% relative humidity), the AL@WC device retained 86% of its initial PCE under dark conditions and 81% under continuous illumination. In a nitrogen environment, dark stability measurements indicated a retention rate of 89%, while illumination stability exhibited an 83% retention for the AL@WC, affirming the enhanced protective capabilities and stability provided by the WC-integrated FA_0.85MA_0.15PbI₃ AL configuration. This work establishes a simplified and high-performance PSC paradigm by effectively uniting charge extraction and stabilization within the AL, paving the way for scalable photovoltaics exceeding 24% efficiency and operational stability.

Na-doped CeO₂ (NDC) electrolytes with 0.05, 0.10, 0.15, and 0.20 molar ratios of Na ions (0.05NDC, 0.1NDC, 0.15NDC, and 0.2NDC) were synthesized and systematically evaluated for low-temperature solid oxide fuel cell (SOFC) applications. Density functional theory (DFT) calculations reveal that Na doping lowers the oxygen-vacancy formation energy. Structural analysis confirms progressive lattice expansion in NDCs and a maximum oxygen-vacancy concentration in 0.15NDC, while incomplete incorporation of Na in 0.2NDC yields residual Na₂CO₃. Conductivity studies demonstrate negligible electronic conductivity and a peak ionic conductivity in 0.15NDC, suggesting that moderate Na doping enhances ionic transport, whereas excessive dopant is detrimental. Two 0.15NDC-based SOFCs are fabricated by ceramic and dry-pressing methods, and their maximum power densities at 550°C are 208 and 778 mW·cm⁻², respectively, indicating the rapid ionic transport of the 0.15NDC electrolyte. These results demonstrate that Na doping is an effective route for developing advanced low-temperature SOFC electrolytes.

For proton exchange membrane fuel cell (PEMFC) prognostics, deploying deep learning models in real applications depends not only on the network architecture but also on carefully chosen hyperparameters and training strategies. Hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) models can attain high predictive accuracy, yet their sensitivity to practical implementation choices has not been systematically quantified. This work addresses that gap by performing a methodological assessment of a representative CNN–LSTM framework rather than proposing a new architecture. Using the static-load IEEE PHM 2014 FC1 dataset as a controlled benchmark, we examine how two key factors—sliding window length and training data partitioning—jointly affect short-term accuracy and long-term forecast stability. Our results show that the hybrid CNN–LSTM reduces the root mean square error (RMSE) of short-term voltage prediction by 49% compared with a standalone LSTM baseline. For recursive long-horizon lifetime forecasting, a moderately sized sliding window of 40 h is identified as optimal, keeping the remaining useful life prediction error within ±5 h. In addition, the model exhibits strong robustness with respect to training set size: once more than 500 h of data are used for training, the variation in RMSE remains below 0.001 V. By confining the analysis to a static-load test case, we isolate the influence of these implementation parameters and provide practical, data-driven guidance for configuring CNN–LSTM-based prognostic models in PEMFC health management. The proposed validation methodology can be extended in future work to dynamic-load scenarios.

Advancing multifunctionality in microwave absorbing materials through strategic component selection and architectural tailoring is an emerging research focus. In this work, novel heterostructured composite-silver nanoparticles and silver nanowires anchored on hydrophilic carbon cloth fibers (AgNPs/AgNWs@HCCF) were synthesized via a polyol process coupled with impregnation. The flexible, three-dimensional HCCF scaffold served as a support matrix for the AgNPs and AgNWs, which are known for their outstanding dielectric properties and antibacterial capabilities. By forming heterojunctions, these components were integrated into the carbon cloth framework, enabling simultaneous microwave absorption and antimicrobial activity. The heterojunction interfaces contributed to enhanced electromagnetic attenuation by tuning the balance between conduction and polarization losses and thereby improving impedance matching. Notably, sample S2 achieved a peak reflection loss of −53.19 dB at a thickness of 2.86 mm and offered a broad effective absorption bandwidth of 5.36 GHz at 3.50 mm. In addition, the maximum radar cross-sectional reduction reached 35.21 dB·m² at 0°. The antibacterial rates against Escherichia coli and Staphylococcus aureus were 99.40% and 99.93%, respectively.