Epoxy resin‐based ultrafine dry powder coatings for implants
Bibliographic record
Abstract
ABSTRACT Ultrafine dry powder coating technology creates biocompatible polymeric coatings for implants. Nanoparticles (nTiO2) modify flow to prevent agglomeration and create homogenous coatings. Since polyester‐based coatings require the potentially harmful 1,3,5‐triglycidyl isocyanurate (TGIC) curing agent, this study's objective was to develop alternative TGIC‐free formulations. Epoxy and epoxy/polyester (1:1) hybrid mixtures were enriched with CaO (5% w/w) and nTiO2 (0.5% w/w), as functional additives and flow modifiers, respectively. Epoxy‐TiO2 and Hybrid‐TiO2 mixtures were prepared with micron‐sized TiO2 (25% w/w) to enhance biocompatibility. Polymer chips and additives were combined in a high‐shear mixer and passed through a sieve (35 µm) to yield ultrafine particles that were sprayed (20 kV) onto metal sheets and cured (200 °C). Particle size analyses showed that all formulations were ultrafine (D 0.5 < 35 µm), and epoxy/polyester/TiO2 mixtures were the smallest (D 0.5 = 16.34 µm). Angles of repose, avalanche and resting indicated reduced flowability when epoxy was enriched with TiO2 and/or polyester, although all formulae were highly flowable. Elemental mapping of coatings showed a predominance of carbon (C) and oxygen (O) from resin polymer, and elevated titanium (Ti) in the TiO2 enriched surfaces. However, calcium (Ca) clusters were higher on the epoxy/polyester Hybrid coatings. Optical microscopy showed human mesenchymal cells (ATCC CRL‐1486) attached and spread out, and Alizarin Red staining showed mineral deposits in 2–4 week cultures, particularly on epoxy/polyester/TiO2 Hybrid surfaces. These epoxy resin‐based formulations were effective TGIC‐free substitutes for ultrafine dry powder coatings on implants. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016, 133, 43960.
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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) | 0.002 | 0.001 |
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".