Characterization of High Density (111)-oriented Ag Nanotwinned Films Deposited on Sapphire Wafers
Bibliographic record
Abstract
The exceptional properties of nanotwinned structures have been a hot area of research in recent years. Silver (Ag) has the lowest stacking fault energy (SFE) among all FCC metals, which has a strong tendency to form a twin structure. Also, sapphire substrates are ideal for use in LED applications due to high-temperature resistance, high strength, good electrical insulations, and low dielectric loss. Depositing Ag nanotwinned films on sapphire substrates can serve as a perfect candidate for die bonding in LED manufacturing. In this study, both sputtering and evaporating methods had been demonstrated for the fabrication of high density (111)-textured Ag nanotwinned films on sapphire wafers. Microstructural analyses show that both the sputtered and evaporated Ag grains presented a high density of twin structure. The cross-sectional EBSD analysis of the sputtered Ag nanotwinned film indicated a highly (111)-preferred orientation to 34.6% of the overall grains. Further, the sputtering process allows the production of surface roughness of the Ag nanotwinned film up to 65.1 nm. The epitaxial growth of Ag nanotwinned films with (111)-preferred orientation can be utilized by both the deposition methods.
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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".