Bioactive hydroxyapatite coatings on polymer composites for orthopedic implants
Bibliographic record
Abstract
Hydroxyapatite [HA, Ca10(PO4)6(OH)2] coatings on polymer composite substrates were investigated for their bioactivity and their physicochemical and mechanical characteristics. HA holds key characteristics for use in orthopedic applications, such as for coating of the femoral stem in a hip replacement device. The plasma-spray technique was used to project HA onto a carbon fiber/polyamide 12 composite substrate. The resulting HA coatings exhibited mechanical adhesion as high as 23 MPa, depending on the surface treatment of the composite substrate. The purpose of this investigation was to evaluate the bioactivity of an HA-coated composite substrate. HA- coated samples have been immersed in simulated body fluid (SBF) and maintained within a shaker bath for periods of 1, 7, 14, 21, and 28 days at 37 degrees C. Scanning electron microscopy, energy dispersive X-ray spectroscopy, and X-ray diffraction techniques were performed on the samples before and after immersion into SBF. SBF was analyzed using inductively coupled plasma atomic emission spectrometry for element concentration and evaluation of the solution's purity. SBF conditioning led to the deposition of crystalline HA onto the surface of the coatings. The calcium-to-phosphorous ratios of initial HA coating and of newly deposited HA were respectively 1.72 and 1.65, close to the HA theoretical calcium/phosphorous value of 1.67. Results demonstrated that bioactive HA coatings were produced by plasma spraying, because SBF conditioning induced newly formed HA with high crystallinity. Mechanical adhesion of the HA coatings was not significantly affected upon SBF conditioning.
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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) | 0.001 | 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".