Hydroxyapatite Coatings on a 3D Porous Surface Using Thermal Substrate Method
Bibliographic record
Abstract
Hydroxyapatite (HAp) coated films were obtained using a thermal substrate method, whereby an aqueous solution at pH=7 or 8 containing Ca2+ and PO43− ions was used to deposit a coating on porous-surfaces substrates formed by sintering 44–150 μm-sized Ti6Al4V powders on a solid commercially pure titanium (cpTi) base. The HAp coating conditions used were: coating time = ∼900 s and substrate temperature equal to 373 or 393 K. Coatings formed in a pH=7 solution at 373 K for 900 s and in a pH=8 solution at 393 K for 300 s showed that all of the surfaces of the Ti6Al4V sintered particles (both front and back faces) and the base cpTi substrate were covered with HAp, and that they maintained their original open-pored geometry. The precipitated HAp appeared to be denser on porous-surfaced samples compared with plain-surfaced cpTi samples. Use of the thermal substrate method with appropriate pH control, and substrate heating temperature and time was effective for forming HAp coatings on powder-sintered samples with a complex topography.
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".