Surface Technology (表面技术)•2026•DOI: 10.16490/j.cnki.issn.1001-3660.2026.10.002
Marine environments impose combined electrochemical, microbiological, and cavitation erosion degradation on metallic infrastructure, shortening service intervals and inflating maintenance expenditure. High-entropy alloy (HEA) coatings mitigate these failure modes through simple solid-solution or amorphous microstructures that suppress galvanic coupling and promote dense passive film formation. This review systematically examines corrosion-resistant HEA coatings from single-factor to multi-factor coupling perspectives, covering classification and compositional design, fabrication routes, and corrosion behavior under complex marine conditions. Key coating systems include FeCoCrNiMn, AlCoCrFeNi, FeCrNiCoAl, and (FeCoCrNi)75B15Si10 amorphous alloys deposited by atmospheric plasma spraying, high-velocity oxy-fuel spraying, and wire arc spraying. Elemental additions of Cr, Al, and Mo enhance passivation; B and Si promote amorphous phase formation. The review identifies core engineering bottlenecks: compositional design, process optimization, and service performance validation. A multi-scale simulation, process-structure optimization, and in-situ characterization framework is proposed to accelerate coating deployment. These findings provide theoretical and technical guidance for next-generation corrosion-resistant coatings in marine equipment.
Nano-Micro Letters•2025•DOI: 10.1007/s40820-025-01719-y
Urgent requirements of the renewable energy boost the development of stable and clean hydrogen, which could effectively displace fossil fuels in mitigating climate changes. The efficient interconversion of hydrogen and electronic is highly based on polymer electrolyte membrane fuel cells (PEMFCs) and water electrolysis (PEMWEs). However, the high cost continues to impede large-scale commercialization of both PEMFC and PEMWE technologies, with the expense primarily attributed to noble catalysts serving as a major bottleneck. The reduction of Pt loading in PEMFCs is essential but limited by the oxygen transport resistance in the cathode catalyst layers (CCLs), while the oxygen transport in anode catalyst layers (ACLs) in PEMWEs also being focused as the Ir/IrOx catalyst reduced. The pore structure and the catalyst–ionomer agglomerates play important roles in the oxygen transport process of both PEMFCs and PEMWEs due to the similarity of membrane electrode assembly (MEA). Herein, the oxygen transport mechanism of PEMFCs in pore structure and ionomer thin films in CCLs is systematically reviewed, while state-of-the-art strategies are presented for enhancing oxygen transport and performance through materials and structural design. The deeply research opens avenues for exploring similar key scientific problems in oxygen transport process of PEMWEs and their further development.
Transactions of Nonferrous Metals Society of China (中国有色金属学报)•2025•DOI: 10.1016/S1003-6326(25)67030-0
A porous three-dimensional (3D) structure was created on the Zn surface by an electrostripping activation process under high current density, which could suppress the non-uniform Zn2+ deposition induced by the “tip effect.” Moreover, a functional CeO4H4/Ce(OH)3 passivation layer was introduced to prevent electrochemical corrosion and facilitate electrolyte infiltration. Benefiting from the ingenious 3D structure and passivation layer, the assembled symmetric cell delivers a long lifespan of over 1500 h at 5 mA/cm2. Even at 20 mA/cm2, the electrode can still operate for over 300 h. The R-Zn@CeǁMnO2 full cell exhibits a capacity of 205.3 mA·h/g after 300 cycles at a current density of 0.3 A/g.
Int. Journal of Minerals, Metallurgy and Materials (矿物冶金与材料学报)•2025•DOI: 10.1007/s12613-025-3114-x
Digital modeling and autonomous control of the die forging process are significant challenges in realizing high-quality intelligent forging of components. Using the die forging of AA2014 aluminum alloy as a case study, a machine-learning-assisted method for digital modeling of the forging force and autonomous control in response to forging parameter disturbances was proposed. First, finite element simulations of the forging processes were conducted under varying friction factors, die temperatures, billet temperatures, and forging velocities, and the sample data, including process parameters and forging force under different forging strokes, were gathered. Prediction models for the forging force were established using the support vector regression algorithm. The prediction error of Ff, that is, the forging force required to fill the die cavity fully, was as low as 4.1%. To further improve the prediction accuracy of the model for the actual Ff, two rounds of iterative forging experiments were conducted using the Bayesian optimization algorithm, and the prediction error of Ff in the forging experiments was reduced from 6.0% to 1.5%. Finally, the prediction model of Ff combined with a genetic algorithm was used to establish an autonomous optimization strategy for the forging velocity at each stage of the forging stroke, when the billet and die temperatures were disturbed, which realized the autonomous control in response to disturbances. In cases of −20 or −40°C reductions in the die and billet temperatures, forging experiments conducted with the autonomous optimization strategy maintained the measured Ff around the target value of 180 t, with the relative error ranging from −1.3% to +3.1%. This work provides a reference for the study of digital modeling and autonomous optimization control of quality factors in the forging process.