On the importance of crystallographic texture in the biocompatibility of titanium based substrate
Bibliographic record
Abstract
The role of grain size and crystallographic orientation on the biocompatibility of commercially pure titanium was investigated. Samples, with significant differences in crystallographic texture and average grain size (from 0.4 to 40 µm) were produced by equal channel angular pressing (ECAP) and post deformation annealing. X-ray diffraction and electron back scattered diffraction (EBSD) were used to evaluate differences in texture and microstructural characteristics. The titanium oxide film present on the surface of the samples was analyzed to determine the oxidation state of titanium and the chemical bonds between oxygen and titanium using X-ray photoelectron spectroscopy (XPS). Biocompatibility experiments were conducted using MC3T3 preosteoblast cells. Cell attachment was found to be texture-sensitive, where the number of attached cells was higher on the samples with higher number of (0002) planes exposed to the surface, regardless of the grain size. A relationship was also found between the titanium oxide species formed on the surface and the crystallographic texture underneath. The surface texture consisting of more densely packed basal planes promote the formation of Ti-OH on the surface, which in turn, enhances the cell-substrate interactions. These surface characteristics are deemed responsible for the observed difference in cell attachment behaviour of surfaces with different textures. Finally, it is inferred that texture, rather than the grain size, plays the major role in controlling the surface biocompatibility of biomedical devices fabricated from pure metallic titanium.
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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 | 0.001 |
| 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".