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.)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".