Etching the Oxide Barrier of Micrometer-Scale Self-Organized Porous Anodic Alumina Membranes
Bibliographic record
Abstract
We develop a quantitative model to calculate the optimal experimental conditions for the etching of the oxide barrier of porous anodic alumina (PAA) membranes. The method is applied to a membrane fabricated at 370 V in a solution of 2% citric acid. The process creates a network of small pores at the bottom of the larger pores, which accelerates the oxide barrier etching relatively to the pore walls of the PAA membranes, when etched in a solution of phosphoric acid. The oxide barrier etching is confirmed by observation of PAA membranes using scanning electron microscopy, revealing the formation of the small pores and the preferential etching of the bottom of the pores rather than the pore walls. The proposed method, which leads to a better control over the fabrication of nanoporous templates, can be adapted to oxide barriers of different PAA membranes formed at different voltages and in different acids.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".