SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-025-3508-3
High-entropy carbides (HECs) are promising candidates for ultrahigh-temperature applications, but their oxidation resistance at temperatures above 2000 °C remains a critical bottleneck. Here, we report a laser-assisted compositional engineering strategy to develop non-equimolar (Zr0.2Ti0.2Ta0.3Cr0.3)C HECs with superior oxidation resistance up to 2500 °C. Using a self-developed laser oxidation platform, we first screened equimolar (Zr0.25Ti0.25Ta0.25Me0.25)C (Me = Hf, W, Nb, Cr, V, Mo) samples at 2500 °C, identifying Cr as a critical element for forming protective oxide scales. Systematic tuning of Cr content revealed that the optimal composition (Zr0.2Ti0.2Ta0.3Cr0.3)C exhibits a dense, crack-free oxide layer composed of molten (Cr, Me)(Ta, Me)O4 and (Ta, Me)2O5 phases embedded with (Zr, Me)O2 crystals, which effectively seal defects and suppress oxygen diffusion. The synergistic effects of these phases lead to a significant reduction in mass gain and oxide layer thickness compared to equimolar counterparts. This work provides a new pathway for designing HECs with long-life oxidation resistance at 2500 °C, enabling their use in extreme environments such as hypersonic vehicle leading edges and rocket nozzle throats.
SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-026-4186-7
The development of ultrahigh-temperature technologies, such as nuclear reactors, rocket nozzles, scramjet propulsion systems, and hypersonic vehicles, demands materials with melting points (Tm) exceeding 3273 K. However, the highest reported Tm among non-radioactive oxides is 3125 K (MgO), limiting progress. Existing predictive models for oxide Tm suffer from a trade-off between physical insight and practical utility: thermodynamic approaches require complex calculations, Lindemann's criterion relies on elusive parameters like Debye temperature, and semi-empirical correlations lack transferability. Machine learning models offer predictive power but lack interpretability and reliable extrapolation. Here, we simplify a previously derived semi-empirical model based on bond-breaking probability, which links Tm to effective potential barrier Ueff, proportional to bond length (d), bond density (Nb), and bond ionicity (fi). By analyzing a dataset of 68 oxides, we establish a simplified linear relationship between Tm and Ueff, expressed as Tm = 0.052Ueff + 122.6 K, with Ueff in kJ/mol. This model achieves a mean absolute error of 76 K and a coefficient of determination (R²) of 0.97, outperforming existing empirical models. The model's physical transparency and simplicity enable rapid screening of novel oxides, guiding the design of materials with Tm exceeding 3273 K. Our findings provide a practical tool for accelerating the discovery of ultrahigh-temperature oxides, addressing a critical bottleneck in next-generation thermal protection systems.