Synergistic effects of Nb and Si co-regulation on microstructural evolution and molten aluminum corrosion behavior of Nb–Ti–Mo–Al–W refractory high-entropy alloys
Source abstract
Abstract
Conventional tool steels suffer rapid degradation in molten aluminum, necessitating advanced materials with superior corrosion resistance and high-temperature strength. Refractory high-entropy alloys (RHEAs) are promising, yet how co-regulated alloying elements govern phase constitution, mechanical behavior, and corrosion remains unclear. This study investigates Nb and Si co-regulation in Nb 42-x Ti 25 Mo 10 Al 20 Si x W 3 (x = 10, 5, 0) RHEAs via thermodynamic calculations, multi-scale characterization, and static immersion tests at 750 °C. Increasing Si at the expense of Nb promotes (Nb,Ti) 5 Si 3 silicide formation: discrete precipitates at 5 at.% Si evolve into a continuous network at 10 at.% Si. The 5 at.% Si alloy achieves the highest yield strength (1452 MPa at 750 °C) due to dispersed silicide reinforcement, whereas the continuous network in the 10 at.% Si alloy introduces stress concentration. The Si-free alloy (highest Nb content) exhibits the best corrosion resistance, with a corrosion rate 92.1% lower than that of the 10 at.% Si alloy after 24 h, attributed to its homogeneous BCC solid-solution matrix and absence of rapid diffusion channels along phase boundaries. A competitive diffusion-dissolution kinetic model incorporating a diffusion promotion factor (γ) quantitatively links silicide network continuity to accelerated Al infiltration, with γ increasing from ∼1.00 (x = 0) to 1.42 (x = 10). These findings establish microstructure–property relationships and design principles for balancing strength and corrosion resistance in multiphase RHEAs for high-temperature aluminum processing.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: High Entropy Alloys Studies · Intermetallics and Advanced Alloy Properties · Additive Manufacturing Materials and Processes
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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