OS1307 Hardness and microstructure of electrodeposited Ni-Cu alloy with various composition gradients
Bibliographic record
Abstract
Vickers hardness of Ni-Cu alloy films which have composition gradient along thickness direction was investigated. The composition-gradient Ni-Cu films were fabricated on copper substrate by the special electrodeposition technique where electrode potential changed continuously. The films were composed of alternate stack of layers having positive and negative gradients. Total thickness of all the films was 1.2μm. The gradient films differed in both composition gradient dc_ /dx and amplitude of Ni concentration Δc_ . The dc_ /dx and Δc_ values varied from 5×10^<-6> to 2×10^<-2>nm^<-1> and 0.006 to 0.6, respectively. The composition-gradient Ni-Cu films having high gradient or high Ni concentration amplitude showed higher Vickers hardness than the Ni-Cu multilayered film with defined Ni/Cu interfaces: the maximum hardness of the composition-gradient Ni-Cu films was amounted up to Hv250 while that of the multilayered film was Hv140. The hardness of the composition-gradient Ni-Cu films revealed good correlation with the gradient dc_ /dx, compared with the Ni concentration amplitude Δc_ . From this result, the high strength of the composition-gradient Ni-Cu films could be explained not by internal stress induced by lattice mismatch, but by high-density misfit dislocations caused by the composition gradient.
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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) | 0.002 | 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".