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.
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.
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
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
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 2 2− ) to sulfate (SO 4 2− ) 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.
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.
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
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.
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
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 determ-ination 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 CoCrFeNiTix (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 CoCrFeNiTi0.3 (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
We introduce an innovative perovskite solar cell (PSC) architecture featuring a multifunctional active layer (AL) of FA0.85MA0.15PbI3 (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
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.
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
Accurate modeling of material constitutive relationships under compositional fluctuations poses significant challenges.Tradi-tional 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 mech-anisms,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 scenari-os 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 reli-able 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.
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.
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.
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
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
