ZnO Films from Thermal Oxidation of Zn Films: Effect of the Thickness of the Precursor Films on the Structural, Morphological, and Optical Properties of the Products
Bibliographic record
Abstract
Zinc oxide (ZnO) films with different structural, morphological, and optical properties were obtained by (fixed) thermal oxidation of deposited metallic zinc (Zn) films. The main characteristics of the oxidized films are discussed in terms of the Zn film thickness. On-axis preferential crystallographic oriented growth of ZnO can be tuned based on the control of the thickness of the deposited Zn: c-axis (a-axis) for the thinnest (thicker) Zn film. The thicker ZnO film is rather a-textured, whereas the grains hosted by the ZnO films corresponding to the Zn films of intermediate thicknesses are more randomly oriented. For Zn films of ever-increasing thickness, a tendency towards the crystallization of larger ZnO nanocrystals holds, combined with a continuous increment on the surface roughness. In contrast, the fundamental bandgap of the resultant oxide-based films decreases with thickness. The roughness of the ZnO films is not directly measured. It is qualitatively described by the analysis of Zn-film micrographs obtained by Scanning Electron Microscopy and by the demonstration of strong optical scattering interactions present in the thicker ZnO films by their random lasing activity.
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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".