Design of high H2-permeable Nb-based alloy membranes using a simple physical-chemical parameter matching approach
Bibliographic record
Abstract
Alloys containing Group VB metals represent a promising alternative to commercially available Pd-based membranes. However, eliminating the need for complex trial-and-error experiments and accurately predicting suitable alloys using simple empirical rules remains a significant challenge. To address this, a series of Nb-based alloys with the composition Nb 30 Ti 35 Co 30 M 5 (at.%, where M = Fe, Al, or Au) was developed in this study using a physical-chemical parameter matching (PCPM) design strategy for the first time. The structure, morphology, and hydrogen permeability of the alloys were characterised using X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and other techniques. The results indicate that all doped samples exhibit a dual-phase structure similar to that of Nb 30 Ti 35 Co 35 . However, samples containing Fe and Al display a hypereutectic structure, characterised by the formation of a primary BCC-(Nb, Ti, M) phase. Notably, the samples that conform to the PCPM rules exhibit good hydrogen permeability. At each test temperature, their permeabilities follow the order: Nb 30 Ti 35 Co 30 Fe 5 > Nb 30 Ti 35 Co 35 > Nb 30 Ti 35 Co 30 Al 5 . In contrast, the H 2 flux of the Au-containing specimen, which does not conform to the PCPM rules, remains close to zero throughout the test – even at temperatures as high as 673K. Among the tested alloys, Nb 30 Ti 35 Co 30 Fe 5 exhibits a hydrogen permeability of 2.69 × 10 −8 mol H 2 m −1 s −1 Pa −0.5 at 673K, surpassing that of most Nb-based membranes. This high permeability is attributed to a specific orientation relationship. These findings provide novel insights into the design of highly H 2 -permeable Nb-based alloy membranes.
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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 | 0.000 |
| 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 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".