Rapid and tunable growth of a well-ordered hexagonal nanoporous anodic aluminum oxide (AAO) structure by two-step high-temperature anodization
Bibliographic record
Abstract
In this study, we have developed a swift and well-ordered growth of a nanoporous anodic aluminum oxide (AAO) structure using two-step high temperature anodization of the pure aluminum substrate. The pre-anodization surface treatment of the aluminum substrate assisted in the formation of well-organized nanoporous structures. The two-step anodization process was performed in 0.3 mol/L of oxalic acid at 20 °C with 40 and 45 V to obtain tunable pore diameters. The high temperature of the electrolyte solution facilitated the rapid growth of the nanoporous AAO structure. The top surface image of AAO shows a well-ordered nanoporous structure with an average pore diameter of 70 nm at 40 V and 100 nm at 45 V. The SEM cross-sectional view also illustrates the well-ordered nano-channel and the elemental mapping elaborates the presence of aluminum and oxygen. The thickness of the nanoporous AAO structure was determined using SEM for three anodization time spans (20, 24, and 28 h), in which an increasing trend was observed. The fabricated AAO has a higher thickness and a well-ordered nanoporous structure, which shows that it can be used as a template for fabricating nanostructured materials.
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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) | 0.000 | 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".