Optical Properties of Al<sub>2</sub>O<sub>3</sub>-Ni-Al Nano-Composite Films
Bibliographic record
Abstract
In the nature, there are a variety of organisms and minerals that produce colors as a result of nanostructures that interacting with light. This phenomenon is called structural colors. Structural colors are based on several basic optical effects, including thin layer interference, diffraction, and light scattering. [1] Compared to pigmented colors, structural colors have many useful properties, including iridescence, high reflectance, and polarization. These optical properties have been applied in many fields such as color displays, decorations, anti-counterfeiting and fluid sensors. Specifically, their studies were reported that porous thin films formed by AAO processes produces bright colors in the visible range. [3] In addition, fabricated aluminanickel film by AAO process and electroplating has been reported to exhibit structural color. Aluminum was deposited by sputtering on the nanocomposite film fabricated by the AAO process and the electroplating process. The AAO process was performed by applying a voltage of 20 V in 0.1 M sulfuric acid electrolyte. The pore diameter of the formed porous alumina was 25 nm, the depth was formed to 600 nm. After the AAO process, nickel thickness of 50 nm was formed using an electroplating process using AC. Thereafter, aluminum was deposited on the nanocomposite film having a thickness of 20 nm using a sputter.
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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".