Rare earth-containing magnesium alloys are critical materials in biomedical applications, yet their corrosion performance directly determines service safety. To overcome the time-consuming limitations of traditional experiments and the difficulty in quantifying complex corrosion mechanisms, this study established a machine learning prediction framework using literature-derived alloy compositions and environmental data. Six algorithms, including Random Forest Regressor, Extreme Gradient Boosting, and Support Vector Machine, were rigorously evaluated. Beyond standard grid search, an advanced optimization strategy integrating the Local Outlier Factor method for noise reduction and learning curve analysis was employed to effectively mitigate overfitting. The results indicate that the optimized Random Forest Regressor model achieved the highest accuracy for corrosion potential prediction (coefficient of determination R^2 of 0.98 for training and 0.93 for testing), while the Extreme Gradient Boosting model excelled in predicting corrosion current density (coefficient of determination R^2 of 0.97 for training and 0.94 for testing). Notably, validation through independent electrochemical experiments demonstrated the models’ excellent generalization ability, with prediction errors for corrosion potential and current density within 2

Developing non-platinum (Pt) electrocatalysts that couple high activity with long-term stability is essential for hydrogen production via water electrolysis. Osmium (Os), with high cost-advantage and distinctive electronic structures, is a highly promising catalyst for the hydrogen evolution reaction (HER). However, its overly strong hydrogen adsorption and limited stability have hindered practical applications. Herein, by virtue of grain-boundary and oxygen-vacancy engineering, we construct a cerium dioxide support featuring a "grain boundary-oxygen vacancy dual-defect" network (GBOV-CeO x ). The dual-defect network stabilizes ultrafine Os nanoclusters (Os-GBOV-CeO x ) by strengthening the electronic metal-support interaction (EMSI), enhancing the durability of the catalyst. In addition, the oxygen-vacancy-induced charge redistribution tunes the Os d-band center, mitigating excessive adsorption of hydrogen intermediates and accelerating HER kinetics. Meanwhile, the permeable grain-boundary network provides rapid charge-transport pathways and reduces interfacial resistance. As a result, Os-GBOV-CeO x delivers an ultralow alkaline HER overpotential of only 11.2 mV at 10 mA cm-2 and an ultrahigh mass activity over 56 times larger than that of commercial Pt, with high stability for more than 1000 h. When deployed as the cathode in an anion-exchange membrane water electrolyzer (AEMWE), it even reaches 1 A cm-2 at 1.71 V and operates stably for over 240 h. This work highlights the critical role of a multi-effect synergy design in activating and stabilizing metal sites for advanced energy conversion.

In this work, we introduce a self-guided design framework that combines target property and feature importance evaluation to optimize the aging behavior of Mg alloys. The design efficiency was built by a precise and reliable two-step feature engineering training strategy (2SFE) guided by machine learning models capable of assessing a range of Mg alloy properties across high-dimensional datasets. A multi-objective hybrid algorithm combines efficient global optimization (EGO) and a gradient-based feature reduction is established within this framework. EGO incorporates an innovative multi-objective gradient designed to speed up the search process along the Pareto front scheme, while 2SFE reduces features to improve the design space. A novel two-step feature engineering selection strategy (2SFE) was introduced, reducing computational input features to six (6) while enhancing prediction accuracy. Compared to the traditional approach, the scaled super learner model improved accuracy from 0.86 to 0.97. This methodology was experimentally validated, resulting in the creation of a novel Mg-6Gd-Zr-6Y-Nd, Mg-9Zn-5Gd-Zr-8Y-3Nd, Mg-8Zn-Zr-3Y-Nd, and Mg-9Zn-Zr-2Y (wt%) alloy, processed under defined specific conditions. The integrated approach was validated and assessed by (a) comparing predicted values with experimental results and (b) benchmarking the model’s outcomes with existing experimental and theoretical findings reported for Mg alloys. This integrated approach offers a cost-effective, efficient pathway for designing advanced Mg alloys while significantly reducing manual input in the alloy development process.

The development of new energy materials means higher requirements for energy storage equipment. Aqueous zinc-ion batteries (AZIBs) have attracted much attention due to their high energy density, low cost, safety, and environmental protection. Vanadium pentoxide (V2O5) has high capacity as the cathode material of zinc-ion battery due to its unique layered structure, but its practical application is limited due to its low conductivity and poor cycle performance. In this study, the laboratory-specific microwave-assisted ball-milling technique was used to prepare V2O5-polyaniline (PANI), V2O5-polypyrrole (PPy), and V2O5-poly(3,4-ethylenedioxythiophene) (PEDOT) composites from V2O5 and conductive polymers (CPs). The three materials exhibit excellent electrochemical performance as positive electrode materials for AZIBs, among which V2O5-PEDOT has the highest specific capacity and excellent rate performance, with a discharge-specific capacity of up to 543 mAh g-1 at 0.1 A g-1 and a capacity retention rate of 35.8% from 0.1 A g-1 to 5 A g-1. The insertion/extraction process of Zn2+ was analyzed in depth using density functional theory (DFT) calculations and ex-situ XRD characterization, and the reaction mechanism of the conductive polymer-intercalated V2O5 composite material in the battery was described.

Polyetheretherketone (PEEK) faces a critical trade-off between flame retardant and mechanical performance in safety-critical applications. To address this dilemma, we propose an innovative MOF-on-MOF strategy, fabricating a hierarchical NH2-UIO-66@NH2-MIL-125 (U/M) nanofiller via PVP-assisted solvothermal synthesis. Incorporated into PEEK, U/M simultaneously enhances mechanical properties and flame retardant/smoke suppression. At 2 wt% loading (P-U/M-2 %), tensile strength increases by 21.5 % compared to pure PEEK, while cone calorimetry reveals a 19.7 % reduction in peak heat release rate (pHRR), a 52.5 % decrease total smoke production (TSP), and 36.6 % prolonged ignition time. Synergistic mechanisms are revealed: In the gas phase, U/ M quenches radicals, dilutes combustibles, and adsorbs smoke; in the condensed phase, in-situ formed ZrO2/TiO2 catalyzes the formation of a robust graphitized char layer. Comprehensive analyses (sXAS, TG-FTIR, Raman, XPS, SEM) and theoretical calculations confirm this dual-phase action. This work pioneers MOF-on-MOF architectures as a unified solution to break the performance trade-off in high-safety PEEK composites.

Aqueous zinc‐ion batteries (AZIBs) are promising energy storage systems due to their low cost, high safety, and considerable theoretical energy density. However, the use of zinc anodes could lead to dendrite growth, low Zn stripping/plating efficiency, and side reactions, hindering rate performance and cycling stability. To address these issues, a hollow Cu 2 O@CuSe core–shell structure was synthesized via a simple and eco‐friendly solution method. Benefiting from its unique core–shell structure, the material exhibits high specific capacity (430 mAh g −1 at 0.1 A g −1 ), good rate performance (248 mAh g −1 at 5.0 A g −1 ), and better cycling stability (74.4% retention after 4000 cycles at 1.0 A g −1 ). A series of tests and characterizations, including in situ electrochemical impedance spectroscopy (EIS), distribution of relaxation times (DRT) analysis and density functional theory (DFT) calculation, confirmed that the CuSe shell not only enhanced the redox activity at the electrode/electrolyte interface, but also provided structural stability for the Cu 2 O core. A rocking‐chair battery using Cu 2 O@CuSe as the anode and ZnMn 2 O 4 as the cathode achieves long‐term stable cycling (95.7% retention after 500 cycles at 0.1 A g −1 , 84% retention after 20000 cycles at 2.0 A g −1 ), demonstrating a promising strategy for high‐performance AZIBs anodes.

A dual-modification strategy integrating hydrogenation with plasma-enhanced atomic layer deposition (PE-ALD) is developed to address bulk carrier recombination and sluggish OER kinetics in TiO₂ photoanodes. Oxygen vacancies (Ov) introduced by hydrogenation broaden light response and suppress bulk charge recombination, while ALD-deposited Co nanoparticles form a Schottky junction serving as hole-trapping centers, reducing OER overpotential and extending carrier lifetime. The optimized TNB-H2@Co-200 photoanode achieves a photocurrent density of 1.02 mA cm−2 at 1.23 V vs. RHE under AM 1.5G illumination — about five times that of pristine TiO₂ — demonstrating outstanding PEC performance.

Aqueous zinc-ion batteries (AZIBs) are promising energy storage systems due to their low cost, high safety, and considerable theoretical energy density. However, the use of zinc anodes could lead to dendrite growth, low Zn stripping/plating efficiency, and side reactions, hindering rate performance and cycling stability. To address these issues, a hollow Cu2O@CuSe core-shell structure was synthesized via a simple and eco-friendly solution method. Benefiting from its unique core-shell structure, the material exhibits high specific capacity (430 mAh g-1 at 0.1 A g-1), good rate performance (248 mAh g-1 at 5.0 A g-1), and better cycling stability (74.4% retention after 4000 cycles at 1.0 A g-1). A series of tests and characterizations, including in situ electrochemical impedance spectroscopy (EIS), distribution of relaxation times (DRT) analysis and density functional theory (DFT) calculation, confirmed that the CuSe shell not only enhanced the redox activity at the electrode/electrolyte interface, but also provided structural stability for the Cu2O core. A rocking-chair battery using Cu2O@CuSe as the anode and ZnMn2O4 as the cathode achieves long-term stable cycling (95.7% retention after 500 cycles at 0.1 A g-1, 84% retention after 20000 cycles at 2.0 A g-1), demonstrating a promising strategy for high-performance AZIBs anodes.

Diamond-like carbon (DLC) films offer high hardness, low friction, and excellent chemical stability; however, their near-surface evolution in reactive remote plasma environments remains poorly understood. This study used a remote plasma source (RPS) to compare the effects of O2 and NF3 plasmas on the structure, chemical state, and properties of DLC films. First-principles calculations were also performed to analyze the migration behavior of O and F atoms. O2 plasma produced relatively mild selective oxidative etching, with an etching rate of approximately 4.57 nm/min. Moderate O2 etching promoted near-surface structural rearrangement, forming a modified region approximately 130 nm thick. This increased the peak near-surface hardness measured by CSM to approximately 37 GPa and reduced the wear rate by approximately 69.6% compared with the untreated film. In contrast, NF3 plasma exhibited a higher etching rate of approximately 22 nm/min and caused pronounced fluorination. The formation of C–Fx bonds, defect generation, and relaxation of the carbon network weakened C–C network connectivity, resulting in degraded mechanical properties and long-term tribological performance. First-principles calculations showed that the migration barriers of O and F atoms are much lower in sp2 than in sp3 carbon structures, indicating that sp2 clusters, edge sites, and defects serve as preferred sites for the migration and reaction of reactive species. These results reveal the distinct mechanisms of O2-induced near-surface reconstruction and strengthening and NF3-induced coupled etching and fluorination, explaining the contrasting effects of the two plasmas on the mechanical performance of DLC films.

Nanocomposites have attracted significant attention as lubricant additives due to their advantages in reducing friction, enhancing wear resistance, and improving thermal and oxidative stability. In recent years, increasing research has explored how different types of nanomaterials (such as carbon-based materials, metallic nanoparticles, and ceramic phases) can use synergistic effects to achieve performance surpassing that of their single components. This review focuses on relevant studies published between 2020 and 2025, providing an updated overview of the advantages, synthesis methods, structures, dispersion stability, lubrication mechanisms, and tribological behavior of nanocomposites. Various structural types are discussed, including coreu2013shell, layered, and in situ hybrid systems, along with their fabrication routes, such as solu2013gel processing, hydrothermal synthesis, and surface modification strategies. The lubrication mechanism of nanocomposites is analyzed based on the material structure and the testing conditions. Particular attention is paid to the synergistic effects among multiple components within the nanocomposites and to how these synergies enhance tribological performance. Furthermore, the challenges faced by nanocomposites and potential future developments are discussed. This review aims to clarify the current status of nanocomposites as lubricant additives and facilitate their future application in advanced lubrication systems.

Endodontic microsurgery (EMS) places strict requirements on intraoperative positioning accuracy and postoperative tissue response. Traditional guide materials are often limited in their clinical promotion due to low molding accuracy, poor mechanical properties and insufficient biocompatibility. In this study, PEG-MX-LDH composite reinforcement phase was prepared by solution ultrasonic mixing technology, and polyurethane acrylate (PUA) open composite surgical templates was formed by digital light processing (DLP) technology, systematically revealing its interface structure regulation and multi-dimensional performance synergistic mechanism. The tensile strength of the modified EMS surgical templates composite material reached 25.7 MPa, which was 44.4% higher than that of pure PUA, and the elongation at break was increased by 72.8%, which can completely replace the metal guide ring,and effectively reduced the deep tissue temperature, which was 1.9 times that of the traditional templates; the surgical path overlap rate was 90.7 +/- 1.2%, which was better than that of the traditional templates. In addition, the composite material was non-toxic to human gingival fibroblasts, non-irritating to the oral mucosa, and had good biocompatibility. 3D printed composite materials and optimized EMS surgical templates design can effectively solve the clinical problem of low heat dissipation efficiency during osteotomy, reduce the risk of bone thermal damage, improve surgical precision, and achieve full-link collaboration from structural design, functional enhancement to clinical transformation, providing a new material path that can be promoted for precision interventional oral templates.

Aqueous zinc-ion batteries (AZIBs) hold great promise for large-scale energy storage; however, their development is hindered by Zn anode instability, including dendrite growth, parasitic hydrogen evolution, and interfacial passivation. Here, we report a low-cost and scalable composite separator (GB50-ZrO2-40) fabricated by ball milling and vacuum filtration of glass fiber, bacterial cellulose (BC), and 40 wt % ZrO2 nanoparticles. The resulting three-phase network exhibits high mechanical strength (∼44 MPa), hierarchical porosity, and strong water/ZrO2 interactions, which together provide multiple functions: it resists dendrite penetration through mechanical reinforcement, homogenizes the local electric field and Zn2+ flux via interfacial Maxwell-Wagner polarization, and promotes partial desolvation of Zn2+ by preferential water adsorption on ZrO2. These synergistic effects inhibit side reactions and promote uniform, dense Zn deposition. As a result, Zn||Zn symmetric batteries with GB50-ZrO2-40 deliver ultrastable cycling performance exceeding 4500 h at 0.5 mA cm-2 with 0.25 mAh cm-2 and 1307 h at 10 mA cm-2 with 5 mAh cm-2. Furthermore, Zn||NaV3O8·1.5H2O full batteries retain more than 92% of their capacity after 1000 cycles at 5 A g-1. The underlying mechanisms are supported by combined electrochemical measurements, in situ microscopy, finite-element simulations, and density functional theory (DFT) adsorption calculations, highlighting the practical scalability of the GB50-ZrO2-40 separator for high-performance AZIBs.

In the Artificial Intelligence (AI) framework, data-driven machine learning (ML) has emerged as a powerful approach for accelerating the discovery and design of Magnesium (Mg) alloys by transforming conventional materials research methodologies. This review focuses on the application of ML techniques to Mg alloys, highlighting their role in analyzing key properties such as mechanical properties, corrosion behavior, and microstructure-property relationships. Although ML models offer significant advantages, their reliance on large datasets and inherent black-box nature remain limited. Thus, the integration of domain knowledge and alloy descriptors is emphasized to improve the model generalization and prediction accuracy by integrating both qualitative insights and quantitative analyses, highlighting the role of alloy descriptors, data-driven models, and performance evaluation metrics, such as the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). The influence of chemical composition, processing conditions, and microstructural features on Mg alloy performance is critically examined, and recent advances in data-driven approaches for material discovery, property optimization, and multi-objective design are discussed. Furthermore, this review addresses key challenges, including data limitations, model interpretability, and generalization, and emphasizes the importance of integrating domain knowledge with ML techniques. Finally, future research directions are outlined, focusing on advanced alloy design strategies, improved datasets, and the development of reliable and interpretable ML frameworks for Mg-alloy applications.

Topological carbon profoundly influences the electrochemical behavior of carbon frameworks. However, prevailing research primarily focuses on increasing topological defect densities, whereas the influence of geometric microenvironments, particularly curvature, on intrinsic activity remains unexplored. Here, we synthesize nanobubble-like high-curvature pentagon carbon (HCPC) with ultralow curvature radius of 1.47 nm via a molten salt-assisted strategy that stabilizes highly strained pentagonal motifs. HCPC exhibits a half-wave potential of 0.80 V for oxygen reduction and a specific capacitance of 250.4 F g−1, outperforming low-curvature counterpart carbon (LCCC). Crucially, the intrinsic activity of pentagonal sites in HCPC is 2.48 and 1.61 times higher than that of LCCC at 0.80 and 0.85 V, respectively, indicating that high curvature fundamentally transforms site reactivity. Futher analyses unveil that curvature-induced electronic redistribution and intensified local electric fields synergistically facilitate rapid OH− desorption, preventing site poisoning and accelerating reaction kinetics. The topological-defect curvature engineering provides a method for designing high-performance catalysts.

Predicting the mechanical properties of Mg alloys is challenging due to the complexity of the underlying mechanisms and the limited availability of datasets. Traditional parametric machine learning (ML) models often struggle with both accuracy and generalization when dealing with high-dimensional feature spaces. On the other hand, non-parametric machine learning methods demonstrate strong predictive capability but require adaptation to the specific structure of property problems. To address these challenges, we propose a two-stage hybrid intelligent machine learning framework designed to enhance the accuracy of alloy prediction. The model incorporates processing conditions and compositional information as inputs and outputs the UTS, YS, and Elongation. In the first stage, machine learning (ML) methods are employed to perform initial predictions while simultaneously extracting feature importance information. In the second stage, a Cheetah Optimizer (CO) algorithm integrated with the group method of data handling (GMDH) refines the outputs from the models and guides the prediction accuracy. The resulting CO-GMDH model demonstrates notable improvements in predictive performance compared with individual ML approaches when applied to the Mg alloy dataset. The CO-GMDH framework introduces a novel predictive structure that combines knowledge discovery with enhanced modelling, offering significant potential for accurate multi-property alloy prediction.

Aqueous zinc-ion batteries hold significant promise for next-generation energy storage due to their high natural abundance, intrinsic safety, and low cost. However, zinc anodes still face interfacial challenges in practical applications, including dendrite growth, hydrogen evolution, and corrosion. This work reports a glass fiber/aramid nanofiber composite separator prepared via ball milling and vacuum filtration. The separator combines the hydrophilic framework of glass fibers (GFs) to reduce interfacial polarization with the high mechanical strength of aramid nanofibers (ANFs) to suppress separator swelling and maintain a stable pore structure. The polar amide groups on the ANF surface facilitate Zn2+ desolvation and enhance Zn2+ affinity, thereby guiding uniform zinc deposition and inhibiting side reactions. The GF/ANF-30 separator enables the Zn||Zn symmetric cell to operate for more than 1820 h at 1 mA cm−2, versus approximately 330 h for the cell with the pure GF separator. When assembled with the GF/ANF-30 separator, the ZnI2 full cell delivers an initial capacity of 248.55 mAh g−1 at 1 A g−1 and maintains 82.8% of its capacity after 10,000 cycles at 10 A g−1. This work offers a scalable strategy for designing high-performance separators for aqueous zinc-ion batteries and provides valuable insights for their large-scale fabrication and practical application.

Dendrite formation and water-induced interfacial side reactions on the Zn anode pose critical challenges to realizing durable cycling stability in aqueous zinc-ion batteries (AZIBs). Herein, we demonstrate that the addition of the biomass-derived additive phenylalanine (Phe) to a 1 M ZnSO4 electrolyte significantly improves the stability of the zinc anode/electrolyte interface. Specifically, Phe exhibits strong interactions with Zn2+, which modulate the primary [Zn(H2O)(6)](2+) solvation sheath and reduce the activity of free water molecules, thereby suppressing the hydrogen evolution reaction (HER). Furthermore, the Phe-containing electrolyte promotes the homogeneous deposition of Zn2+ along the Zn(002) crystal plane, effectively preventing the continuous growth of zinc dendrites and the formation of Zn4SO4(OH)(6)center dot xH(2)O. Thanks to these outstanding advantages, Zn||Zn symmetric cells exhibit ultra-stable cycling performance for 1400 h at 10 mA cm(-2) and 5 mA h cm(-2), while Zn||Cu asymmetric cells achieve an exceptional average coulombic efficiency (CE) of 99.28% at 4 mA cm(-2). Additionally, the Zn||I-2 full cell demonstrates stable operation over 10,000 cycles with a capacity retention of 75.64% at 10 A g(-1), effectively mitigating the shuttle effect of polyiodide ions. These results underscore the significant potential of Phe to unlock the full performance of AZIBs.

For oxygen evolution reaction (OER) catalysts, the exploration focuses on the kinetic process of reactant transformation to products while ignoring the influence of mass transfer determining whether the reactants can smoothly reach active sites and release products, which is responsible for breakthroughs in catalysis performance, especially at large current densities. However, the mass transfer-performance relationship mediated by catalyst structures is rarely displayed due to complexity. Herein, a RuO2 catalyst with a 3D continuous nanonetwork structure is constructed by a facile hydrothermal approach in pure water and annealing in air. Then we correlate the nano-network geometry with diffusion fields and quantitatively reveal how the 3D continuous nano-network structure unlocks buried active sites and promotes product release based on optimized mass transfer channels by combining 3D tomography reconstruction with finite element simulations. As a result, the calculated flow rate of O2 is 5.39 times that of bulk RuO2. Further, both the confinement effect of small-sized channels and the enhanced hydrophilicity inhibit the blockage of O2 bubbles to the mass transfer channels. Therefore, the experimental mass transfer overpotential value is 23% lower than that of bulk commercial RuO2 at 2 A cm? 2. Also, its mass activity (48.02 A g? 1) at an overpotential of 200 mV is 47.54 times that of commercial RuO2. This work provides a solution for developing high-efficiency Ru-based and other catalysts through considering spatial structure and mass transfer influences in catalysis.

ABSTRACT Oxygen evolution reaction electrocatalysts following the adsorbate evolution mechanism (AEM) exhibit exceptional structural stability, but the activity is inherently constrained by the linear scaling relationship associated with single‐metal sites. Here, we propose a metal‐site proton‐acceptor‐assisted deprotonation AEM (MP‐AEM) by incorporating Pd into RuO 2 as secondary metal‐site proton acceptors to activate a novel proton‐transfer pathway. Theoretical calculations and operando characterizations confirm that Pd‐RuO 2 generates new intermediates, reducing the rate‐determining energy barrier by 0.41 eV. Moreover, Pd proton acceptor lowers the O─H bond dissociation barrier and optimizes the hydrogen‐bond network, thereby accelerating the deprotonation kinetics. Furthermore, reduced Ru valence and weakened Ru─O covalency inhibit Ru oxidative dissolution and the lattice‐oxygen‐mediated mechanism. The Pd‐RuO 2 applied in proton exchange membrane water electrolyzers (PEMWEs) requires only 1.55 V @ 1 A cm −2 and shows 15‐fold higher stability than pure RuO 2 . This work establishes an innovative pathway and insight for designing highly efficient PEMWEs catalysts.

Currently, conventional methods for preparing silane-functionalized particles are inefficient, limiting their application in coatings. In this study, superhydrophobic ZnO particles were rapidly synthesized from commercially available ZnO (n-ZnO and μ-ZnO) using microwave-assisted ball milling. ZnO was modified with N-octyltriethoxysilane, and epoxy resin was used as a binder to prepare superhydrophobic paints with a pollution-free and sustainable process. These coatings can be easily applied to various substrates by spraying. Morphological analysis showed micro/nanoscale roughness, ensuring excellent hydrophobicity, mechanical durability, and chemical stability. The coatings delayed icing at − 20 °C for 1263 s, and after 14 days of immersion in a 3.5 wt