Mechanistic insights into V‑enhanced corrosion resistance of CoCrFeNiMn high‑entropy alloys: A quantitative modeling study on the passivation process
Source abstract
Abstract
This study prepared a series of (CoCrFeMnNi) 100-x V x (x = 2, 4, 6, 8) high entropy alloys (HEAs) using vacuum arc melting and systematically investigated the effect of increasing V content on their corrosion behavior and passive film composition in 3.5 wt% NaCl solution. Microstructural analysis shows that all alloys consist primarily of an FCC phase, along with a minor amount of tetragonal phase. The electrochemical test results show that the corrosion resistance of the alloys improves significantly with higher V content. When the V content reaches 6 at%, the HEA begins to exhibit a stable passive region. Further increasing in V content broadens the passive zone and reduces the passive current density, thereby further enhancing corrosion resistance. The performance improvement is mainly attributed to the increase in the content of compact oxides (V 2 O 5 and Cr 2 O 3 ) and a decrease in loose oxides (Fe 2 O 3 , MnO, and Fe 3 O 4 ) within the passive film. The beneficial effect of V addition operates through the following synergistic mechanism: firstly, a higher V content reduces the dissolution concentration of other HEA elements, inhibits the hydrolysis of Cr 3 + , and thus moderates the local pH environment. Secondly, the increased V content promotes uniform nucleation of V 2 O 5 , which provides more favorable thermodynamic conditions for the subsequent heterogeneous nucleation of oxides such as Cr 2 O 3 . Therefore, the combined effects of cation concentration control, local pH regulation, and V 2 O 5 -induced heterogeneous nucleation significantly enhance the corrosion resistance of this series of HEAs.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: High Entropy Alloys Studies · Additive Manufacturing Materials and Processes · High-Temperature Coating Behaviors
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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