A new strategy for designing Nb-based H2-selective alloys based on physical-chemical parameter matching rules
Bibliographic record
Abstract
: The current research on Nb-based H 2 -selective alloys encompasses a wide range of compositions and a variety of systems. Designing alloys with more desirable properties, either individually or in series, remains a great challenge. To this end, a simple physical-chemical parameter matching (PCPM) rule has been established for the first time in this study to accelerate the design of Nb-based hydrogen permeable alloys. Specifically, four parameters – valence electron concentration, mixed entropy, atomic radius difference, and electronegativity difference – were calculated to determine an effective parameter selection window with outstanding permeability. Based on this, six alloys with the formula Nb 30 Hf 35 Co 30 M 5 (M = Fe, Cu, Mo, W, Al, Cr,) were arc melted to verify the newly established PCPM rules. The H 2 permeability of produced samples was further evaluated using a hydrogen permeable test instrument. It was shown that a good match between the four parameters, rather than a single valence electron concentration value, is critical for controlling performance in terms of hydrogen permeation. Additionally, the presence of an impurity phase was confirmed to further deteriorate permeability, even if the PCPM rules were met. The significance of this study lies in introducing a new strategy for screening and exploring H 2 -selective alloys to find the optimal combination of elements that provide better predictions for performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".