Tunable growth of TiO<sub>2</sub>nanostructures on Ti substrates
Bibliographic record
Abstract
A simple and facile method is described to directly synthesize TiO(2) nanostructures on titanium substrates by oxidizing Ti foil using small organic molecules as the oxygen source. The effect of reaction temperature and oxygen source on the formation of the TiO(2) nanostructures has been studied using scanning electron microscopy, x-ray diffraction, transmission electron microscopy, Raman spectroscopy and water contact angle measurement. Polycrystalline grains are formed when pure oxygen and formic acid are used as the oxygen source; elongated micro-crystals are produced when water vapour is used as the oxygen source; oriented and aligned TiO(2) nanorod arrays are synthesized when ethanol, acetaldehyde or acetone are used as the oxygen source. The growth mechanism of the TiO(2) nanostructures is discussed. The diffusion of Ti atoms to the oxide/gas interface via the network of the grain boundaries of the thin oxide layer is the determining factor for the formation of well-aligned TiO(2) nanorod arrays. The wetting properties of the TiO(2) nanostructured surfaces formed are dictated by their structure, varying from a hydrophilic surface to a strongly hydrophobic surface as the surface structure changes from polycrystalline grains to well-aligned nanorod arrays. This tunable growth of TiO(2) nanostructures is desirable for promising applications of TiO(2) nanostructures in the development of optical devices, sensors, photo-catalysts and self-cleaning coatings.
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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".