Fabrication, Geometry, and Mechanical Properties of Highly Ordered TiO<sub>2</sub> Nanotubular Arrays
Bibliographic record
Abstract
Highly ordered TiO 2 nanotubular arrays have attracted increasing interest because of their exceptional physical properties and wide range of existing and potential applications. Techniques for fabrication and characterization of TiO 2 nanotubular arrays have been reported extensively; however, the mechanical behavior of TiO 2 nanotubular arrays has not been systematically investigated, which could be critical for their applications. In this study, we synthesized a series of highly ordered TiO 2 nanotubular array films from a few to hundreds of microns in thickness by changing the anodic voltage or reaction time during anodization. The as-prepared nanotubular arrays were examined carefully using field emission scanning electron microscopy, and their mechanical properties were evaluated using a micromechanical probe and a microtribometer. It was demonstrated that the diameter and wall thickness of TiO 2 nanotubes were almost independent of the anodic voltage and anodization time, whereas thicker nanotubular array films or longer nanotubes could be fabricated at higher voltage or with longer anodization time. The micromechanical probing tests demonstrated that the apparent Young’s modulus, η value, and hardness decreased as the thickness of the nanotubular array films increased due to the densification and collapse of longer nanotubes under external force. The resistance of the nanotubular array films to scratch was evaluated by performing the microscratch tests with in situ measurement of the contact electrical resistance. To determine their resistance to sliding wear, sliding wear tests were performed in different environments. Compared to wear in air, the wear loss in water significantly decreased. The pH value of water slightly affected the wear loss of TiO 2 nanotubular arrays; the results showed that the wear loss of TiO 2 nanotubular arrays decreased with increasing pH from 4, through 7, to 10.
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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".