The influence of grain size and texture on the Young's modulus of nanocrystalline nickel and nickel–iron alloys
Bibliographic record
Abstract
Hardness and Young's modulus were measured by nanoindentation on a series of electrodeposited nanocrystalline nickel and nickel–iron alloys. Hardness values showed a transition from regular to inverse Hall–Petch behaviour, consistent with previous studies. There was no significant influence of grain size on the Young's modulus of nanocrystalline nickel and nickel–iron alloys with grain sizes greater than 20 nm. The Young's modulus values for nanocrystalline nickel and nickel–iron alloys for grain sizes less than 20 nm were slightly reduced when compared to their conventional (randomly oriented) polycrystalline counterparts. The observed trend with decreasing grain size was found to be consistent with composite model predictions that consider the influence of intercrystalline defects. However, there was some notable variability of the measured values when compared to the model predictions. Three theoretical relationships were used to characterise the anisotropic elastic behaviour of these materials. As a result, texture was also considered to have an influence on the measured Young's modulus and used to explain some of the observed variability for the entire grain size range (9.8–81 nm).
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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 | 0.001 |
| 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".