XPS Characterization of TiO2 Nanotubes Growth on the Surface of the Ti15Zr15Mo Alloy for Biomedical Applications
Bibliographic record
Abstract
Ti15Zr15Mo (TMZ alloy) has been studied in recent years for biomedical applications, mainly due to phase beta formation. From the surface modification, it is possible to associate the volume and surface properties with a better biomedical response. This study aimed to evaluate the possibility of using anodization to obtain TiO2 nanotubes due to the presence of valve-type metal (Zr) in their composition. X-ray photoelectron spectroscopy (XPS) was performed to determine the surface chemical composition in both after-processing conditions (passive layer) and after-processing plus anodization (TiO2 nanotube growth). The anodization resulted in nanotubes with diameters and thicknesses of 126 ± 35 and 1294 ± 193 nm, respectively, and predominated anatase phase. Compared to the passive layer of titanium, which is less than ~10 nm, the oxide layer formed was continuous and thicker. High-resolution spectra revealed that the oxide layer of the element alloys contained different oxidation states. The major phase in all depths for the nanotube samples was TiO2. While the stable form of each oxide was found to predominate on the surface, the inner part of the oxide layer consisted of suboxides and metallic forms. This composition included different oxidation states of the substrate elements Ti, Zr, and Mo.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".