Structure and bioactivity of Ti/bioactive glass ‘changing landscape coatings’
Bibliographic record
Abstract
Porous titanium alloy–bioactive glass composite coatings were produced via flame spray deposition. The porous coatings, targeted for bone fixation and orthodontic devices, were made from bioactive glass (45S5) powder blended with one of two titanium powders: commercially pure titanium (Cp-Ti) or titanium alloy (Ti-6Al-4V). The coatings were characterized by cross-sectional quantitative metallography, optical microscopy, scanning electron microscopy, Fourier transform infrared spectroscopy and X-ray diffraction. The porosity and glassy phase distributions were determined by image analysis of optical micrographs, in accordance with ASTM Standard E2109. Selection of Cp-Ti or Ti-6Al-4V alloy, along with alteration of the particle size distribution of bioactive glass, dramatically changed the morphology and phases present within each coating. The consequences on the bioactive response were characterized with immersion testing in simulated body fluid for up to 14 d. The coatings were analyzed for the presence of hydroxyapatite (HA) after testing; HA formation was observed as soon as 7 d for Ti-6Al-4V-based composites, while no formation occurred on Cp-Ti-based composites. HA formation on the negative Ti-6Al-4V control coating was observed after 14 d, while formation was not observed on the Cp-Ti coating, suggesting that the incorporation of bioactive glass into flame-sprayed Ti-6Al-4V alloy coatings enhances bioactivity.
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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".