Co–Cr–Fe–Ni–Nb high-entropy alloy for hydrogen permeation: Influence of physical and chemical parameters on microstructure and hydrogen permeation properties
Bibliographic record
Abstract
Recently, the physical-chemical parameter matching (PCPM) rules were proposed for designing Nb-based ternary and quaternary hydrogen permeation alloys. However, their applicability to high-entropy alloys (HEAs) remained unclear. To address this, a series of dual-phase Co 5 Cr 15 Fe 50 Ni (30– x ) Nb x ( x = 6, 8, 10, 11, 12, 14) HEAs were prepared. Their key physicochemical parameters, microstructures, and hydrogen permeation properties were investigated. These results, combined with an analysis of existing literature, were used to evaluate the relevance of the PCPM rules for hydrogen-permeating HEAs. The findings indicated that all Co 5 Cr 15 Fe 50 Ni (30– x ) Nb x alloys consisted of FCC and Laves phases. A hypoeutectic microstructure formed when x < 11, while a hypereutectic structure formed when x > 11. The Co 5 Cr 15 Fe 50 Ni 19 Nb 11 ( x = 11) alloy exhibited a fully eutectic microstructure. Accompanying these microstructural changes, hydrogen solubility gradually increased, whereas hydrogen diffusivity decreased. Except for the alloys with x = 6 and 8, hydrogen permeation flux was nearly negligible across all other compositions. Furthermore, the suitable range of physicochemical parameters for designing HEAs deviated from the original PCPM rules. Notably, the valence electron concentration (VEC) range for effective HEAs was significantly higher than that for Nb-based ternary and quaternary alloys (∼6.2) but remained below approximately 6.43. Exceeding this VEC threshold risked detrimental changes in crystal structure, leading to poor hydrogen permeation performance or hydrogen embrittlement—even if a potentially suitable dual-phase microstructure was present. In summary, this study elucidated the relationship between microstructure and performance in hydrogen-permeating HEAs from a physicochemical parameter perspective, and it proposed revised guidelines for their design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".