Controlled growth and monitoring of tantalum oxide nanostructures
Bibliographic record
Abstract
Nanoporous metal oxide structures produced by the electrochemical anodization of valve metals, such as Zr, Ti, W, Nb, Al, and recently Ta, have attracted increasing interest because of their potential use as catalysts, waveguides, and three-dimensionally arranged Bragg-stack reflectors. Here we demonstrate the formation of either supported nanotubular Ta oxide films or free-standing Ta oxide membranes, produced by controlling the conditions of Ta anodization in organic-free aqueous HF/H(2)SO(4) solutions. The supported oxide nanotubes, which are at least 15 mum in length, are characterized by very good adhesion to the Ta substrate, and extremely smooth and homogeneous walls. It is also reported here, for the first time, that these nanotubular films can be removed as free-standing Ta oxide membranes that are easily transferable to other substrates, making them potentially useful in sensors, optics, and catalysis. We also show that, when the Ta oxide nanotubes detach to form the membranes, they leave behind an ordered array of dimples in the Ta surface, with the dimples having the identical distribution and size as the pores in the previously attached nanotubes. Finally, we demonstrate how the in situ electrochemical response during anodization can be used to determine which of these highly useful Ta surface morphologies (nanotubes vs. dimples) are formed, without the need for post factum microscopic analysis. Knowledge of the meaning of these in situ signals can now serve to accelerate the controlled formation of oxide nanotubes or dimpled surfaces using other combinations of metals and anodization conditions.
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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".