Effects of Textures on Hydrogen Diffusion in Nickel
Bibliographic record
Abstract
Polycrystalline Ni membranes have been prepared using electrodeposition. Plating current density has a strong influence on texture development of nickel deposits. The texture of deposits can be easily manipulated by controlling plating conditions. At low current density, the left angle 100 right angle fiber texture is the main component. At high current density; the left angle 110 right angle fiber texture dominates. After annealing at 800 C for 1 h deposits with left angle 100 right angle the (111) texture has been observed. For (001), (011) and (111), the three basic planes in FCC nickel, absorption energies decrease as one moves from (001) to (011) and then (111). Hydrogen diffusion in single crystal metals is anisotropic. When polycrystalline metals have texture, the preferential orientation of metals will affect hydrogen absorption and diffusion. Hydrogen permeation results show that there are significant differences among (001), (011) and (111) textured nickel membranes. Diffusion coefficients are in increasing order for (001), (011) and (111) texture samples. The diffusion coefficient of sample without a dominant texture is smaller than those values obtained for (001), (100) and (111) textured samples. The diffusion coefficients of (001) and (011) texture membranes are higher than (001) and (011) single crystal membranes, but have the same trends. Texture in nickel samples plays an important role in hydrogen permeation. (orig.)
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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) | 0.001 | 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".