Anodizations of Al and Ti in NH<sub>4</sub>F or H<sub>3</sub>PO<sub>4</sub> Solutions and Formation of Porous Anodic Alumina with Special Morphology
Bibliographic record
Abstract
Porous anodic alumina (PAA) and anodic TiO 2 nanotubes (ATNTs) have been widely investigated for decades. However, their formation mechanism and growth kinetics remain unclear. Here two unconventional and two conventional anodizations of aluminum and titanium are contrasted to overcome this challenge. PAA with special morphology was fabricated in NH 4 F electrolyte for the first time, which cannot be explained by the popular field-assisted theory. Combining the oxygen bubble mold and the oxide flow model with the dissolution model, a new explanation for the special morphology is presented. In addition, anodic titanium oxide films with plenty of cavities were obtained in H 3 PO 4 electrolyte, which absolutely differ from the general compact films. The cavities in anodic titanium oxide films also result from the oxygen bubbles within the oxide films. The anions in electrolyte (e.g., F –, OH –, PO 4 3– ) accumulate, and the anion-contaminated layer (ACL) forms due to the electric field. The ACL plays a decisive role in the generation of the electronic current ( J e ) and the formation of oxygen bubble mold. Regular ATNTs were obtained as titanium was anodized in NH 4 F electrolyte. The ACL forms and appropriate J e generates because the dissolution from F – on TiO 2 is limited. However, PAA with special morphology was obtained as aluminum was anodized in NH 4 F electrolyte. Thicker ACL results in the impulse peak of the J e because the dissolution and corrosion from F – on alumina is severe.
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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".