Iron-based high-temperature alloys are engineered to withstand extreme conditions, including elevated temperatures, mechanical stress, and corrosive environments. These alloys play a critical role in industries such as aerospace, power generation, and chemical processing, where materials must maintain structural integrity and performance under demanding operational conditions. This review examines recent advancements in iron-based alloys, with a focus on alloying strategies, high-temperature performance, and applications. Traditional approaches—including alloy design, microstructure control, process optimization, and computational modeling—continue to enhance alloy performance. Furthermore, emerging technologies such as high-entropy alloy (HEA) design, additive manufacturing (AM), nanostructured design with nanophase strengthening, and machine learning/artificial intelligence (ML/AI) are revolutionizing the development of iron-based superalloys, creating new opportunities for advanced material applications.

The chemical composition of FeCrAl alloy significantly influences its thermal-mechanical as well as anti-corrosive properties. This study investigates the impact of silicon and yttrium additions on the thermal-mechanical properties and high-temperature oxidation resistance of FeCrAl alloy. The results indicate that thermal conductivity gradually decreases with the incorporation of Y or Si into the lattice, whereas the mechanical strength of the alloy can be enhanced through the addition of Y. A trace amount of Y can improve the alloy’s high-temperature oxidation resistance by mitigating the spallation of the surface oxidation film and promoting the growth of the film, characterized by heterogeneous chemical composition and microstructure. It is observed that Y possesses a higher charge density than FeCrAl, suggesting that Y can lose electrons more readily than other elements, which implies a reduction in oxygen diffusion.

This study systematically investigates the influence of short carbon-fiber (SCF) content on the mechanical, thermal, and tribological properties of self-lubricating polyphenylene sulfide (PPS) composites filled with PTFE and MoS2, addressing the critical need for high-wear resistance in Carbon-Fiber-Reinforced Thermoplastic (CFRTP) structural applications. The results identified 10 wt% SCF as the optimal content that achieved the best balance between load-bearing capacity and friction performance. The coefficient of friction μ and wear amount were reduced by 29.28% and 29.29%, respectively, compared to the PPS/PTFE/MoS2 composite material without SCF, and by 14.67% and 20.75%, respectively, compared to the material with excessive SCF filling (20 wt%). Finite-Element Analysis-Representative Volume Element (FEA-RVE) reveals the mechanism by which excessive content of SCF at the microscopic level leads to a slight decrease in mechanical properties. Critically, the tribological performance exhibited a discrepancy with bulk mechanical properties: above 15 wt% SCF, the wear rate worsened despite high mechanical strength, revealing that increased fiber agglomeration and micro-abrasion effects were the primary causes of performance deterioration. Further in-depth XPS analysis revealed a synergistic lubrication mechanism: In the optimal sample, an ultra-dense PTFE transfer film was formed to mask the underlying MoS2. This masking, coupled with the high surface activity of MoO3 particles leads to stronger physicochemical interactions with the polymer matrix, ensures the exceptional durability and stability of the tribo-film. This research establishes a complete structure–performance relationship by integrating mechanical, thermal, and tribo–chemical mechanisms, offering critical theoretical guidance for the design of next-generation high-performance self-lubricating CFRTPs.

This research centers on the effect of Co–Dy codoping on temperature stability and magnetic properties of Nd–Fe–B alloy for novel rare-earth magnetic material development. Employing X-ray diffraction (XRD), electron probe X-ray microanalyzer-energy dispersive X-ray spectroscopy (EPMA-EDS), magnetic force microscope (MFM), and magnetic tests, the remanence varies complexly with Co–Dy content, decreasing at low doping due to atomic bond disruption and increasing at high doping via exchange enhancement. Coercivity rises at low Co–Dy doping by multiple mechanisms and drops at high doping due to hard magnetic phase change, with 8.6 wt % Co–Dy doping showing high-temperature stability of remanence and coercivity. The soft magnetic phase’s composition, distribution, and Co–Dy content-related changes in its impact on magnetic properties were clarified. Through a combination of experimental and density functional theory (DFT) approaches, the material properties under varying Co–Dy doping levels were systematically investigated. The research involved constructing first-principles calculation models of NdFeBDy with varying Co–Dy content ratios. First-principles calculations identified Co/Dy-preferred Fe sites and doping energy by analyzing state density and magnetic moment changes induced by dual-element doping. Co–Dy codoping synergistically modifies the electronic and magnetic structures, which directly govern the magnetic performance through altered exchange interactions and crystal field effects. Multiple Nd₂Fe¹⁴B samples with diverse Co–Dy content ratios were prepared experimentally. This study offers theoretical and experimental grounds for understanding Co–Dy codoping mechanisms and advancing rare-earth magnetic material performance optimization.

In the oil and gas pipeline industry, numerous small-diameter branch pipe fillet welds exist, which are prone to stress concentration because of diverse geometric shapes. The internal welding defects within these welds pose severe hazards to safe production. Specifically, the irregular geometry often leads to internal root defects where the weld metal fails to fully penetrate the joint or fuse with the base material (referred to as incomplete penetration and incomplete fusion). This study developed a GF-CF-GF (CF is carbon fiber, GF is glass fiber) sandwich composite reinforcement structure for pipe fittings with these specific internal defects (main pipe: Φ323.9 × 12.5 mm; branch pipe: Φ76 × 5 mm) through a combination of finite element analysis (FEA) and burst test verification. The inherent correlation between structural factors and pressure-bearing capacity was revealed by analyzing the influence of defect sizes. Based on FEA, the repair layer coverage should be designed to be within 400 mm from the defect along the main pipe wall direction and within 100 mm from the defect along the branch pipe wall direction, with required thicknesses of 5.6 mm for incomplete penetration and 3.2 mm for incomplete fusion. Analysis of the actual burst test pressure curve showed that the elastic-plastic transition interval of the repaired pipes increased by approximately 2 MPa compared to normal undamaged pipes, and their pressure-bearing capacities rose by 1.57 MPa (incomplete penetration) and 1.76 MPa (incomplete fusion). These results demonstrate the feasibility of the proposed reinforcement design, which has potential applications in the safety and integrity of oil and gas transportation.

To address the challenges of data sparsity and model generalization in small-sample material optimization for flame-retardant resin matrices in composites, this study proposes a “parameterization-visualization” dual-driven strategy. This approach establishes an efficient, standardized pathway for AI-driven material development without relying on black-box algorithms. First, bivariate experimental data involving 10-(2,5-dihydroxyphenyl)-10H-9-oxa-10-phospha-phenanthrene-10-oxide (DOPO-HQ) and a self-developed R-powder (composed of Y₂O₃-stabilized borophosphate glass, zinc borate, and SiO₂) were utilized to generate a high-resolution performance matrix via contour interpolation, thereby transforming sparse experimental points into continuous performance surfaces. Second, a customizable weighted total scoring function was introduced to map multi-objective optimization onto a visualized, single-objective comprehensive scoring field. This not only enabled the precise identification of compliant variable intervals and the global optimum—converting discrete data into a continuous parameter space—but also provided structured training datasets for future machine learning models. Guided by this strategy and leveraging the core AI model of Surrogate-Based Optimization (SBO), the optimal 48.3D/63.9R formulation was rapidly identified with only a limited number of trials. Driven by a synergistic dual-mode mechanism—gas-phase radical quenching by 9,10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide (DOPO) and condensed-phase ceramic glaze formation by molten R-powder—this system achieved superior processability and mechanical integrity while meeting the impregnation and curing demands of large-tow carbon fiber composites. Beyond demonstrating exceptional gas-phase/condensed-phase synergistic flame retardancy, this work validates the proposed parameterized optimization path as a critical bridge connecting small-sample experiments with future large-scale AI optimization. It offers a novel methodological paradigm for accelerating the intelligent design of high-performance flame-retardant resins tailored for large-tow composite materials, effectively overcoming current limitations in data scarcity and model adaptability.