Control of Surface Topography in Biomimetic Calcium Phosphate Coatings
Bibliographic record
Abstract
The behavior of cells responsible for bone formation, osseointegration, and bone bonding in vivo are governed by both the surface chemistry and topography of scaffold matrices. Bone-like apatite coatings represent a promising method to improve the osteoconductivity and bonding of synthetic scaffold materials to mineralized tissues for regenerative procedures in orthopedics and dentistry. Polycaprolactone (PCL) films were coated with calcium phosphates (CaP) by incubation in simulated body fluid (SBF). We investigated the effect of SBF ion concentration and soaking time on the surface properties of the resulting apatite coatings. CaP coatings were examined by scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared spectrometry (FTIR), and energy dispersive X-ray spectrometry (EDX). Young's modulus (E(s)) was determined by nanoindentation, and surface roughness was assessed by atomic force microscopy (AFM) and mechanical stylus profilometry. CaP such as carbonate-substituted apatite were deposited onto PCL films. SEM and AFM images of the apatite coatings revealed an increase in topographical complexity and surface roughness with increasing ion concentration of SBF solutions. Young's moduli (E(s)) of various CaP coatings were not significantly different, regardless of the CaP phase or surface roughness. Thus, SBF with high ion concentrations may be used to coat synthetic polymers with CaP layers of different surface topography and roughness to improve the osteoconductivity and bone-bonding ability of the scaffold.
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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) | 0.000 | 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".