journal article·2023-03Open access

Unveiling the synergistic effects of Re-Mo alloying on diffusion behaviors in γ-Ni: From a theoretical perspective

Peng Hu, Wenyue Zhao, Yi Ru, Yanling Pei, Shusuo Li, Shengkai Gong, Huibin Xu

Abstract

Fine-tuning of alloy composition is crucial for improving the temperature capacity of Ni-based superalloys, which is required for deeper understanding of the strengthening mechanisms of complex alloying. However, the typical strengthening elements Re and Mo still lack clear physical explanations on their synergistic effects on diffusion behaviors. They directly control the dislocation climb and further influence the steady creep rate under high temperatures. In this work, we perform a theoretical investigation on the vacancy-mediated diffusion behaviors regulated by Re and Mo conjunctively in the γ-Ni phase. Combined with the high-accuracy first-principles calculations, a kinetic Monte Carlo model is applied to Ni-Re-Mo ternary systems to evaluate their diffusion kinetics for the first time. The results verify that the 1Re1Mo additions exhibit comparable hindering effects on vacancy formation. The most increased effects on Ni self-diffusion barrier have also been found for 1Mo1Re substitutions compared with 2Re and 2Mo cases. Besides, Mo could elevate the diffusion barriers of Re to some extent and Re would dramatically improve the diffusion barriers of Mo. Especially, the quantified predictions of vacancy diffusion constants of Ni-Rex-Moy (x + y = 5 at.%) systems vary slightly within ∼5% compared with Ni-Re5 system. From the viewpoints of diffusion kinetics, we provide the theoretical interpretations for the synergistic effects of Re–Mo co-alloying and verify the feasibility of partly substituting Mo for Re as an alloy design strategy.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: High Temperature Alloys and Creep · Advanced Materials Characterization Techniques · Intermetallics and Advanced Alloy Properties

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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