MTA-enriched nanocomposite TiO<sub>2</sub>-polymeric powder coatings support human mesenchymal cell attachment and growth
Bibliographic record
Abstract
The objective of the study described in this paper was the development of novel polymer/ceramic nanocomposite coatings for implants through the application of ultrafine powder coating technology. Polyester resins were combined with µm-sized TiO(2) (25%) as the biocompatibility agent, nTiO(2) (0.5%) as the flow additive and mineral trioxide aggregates (ProRoot® MTA, 5%) as bioactive ceramics. Ultrafine powders were prepared and applied to titanium to create continuous polymeric powder coatings (PPCs) through the application of electrostatic ultrafine powder coating technology. Energy dispersive x-ray analysis confirmed that MTA had been incorporated into the PPCs, and elemental mapping showed that it had formed small clusters that were evenly distributed across the surface. Scanning electron microscopy (SEM) revealed continuous and smooth, but highly textured surface coatings that contrasted with the scalloped appearance of commercially pure titanium (cpTi) controls. Atomic force microscopy revealed intricate nano-topographies with an abundance of submicron-sized pits and nano-projections, evenly dispersed across their surfaces. Inverted fluorescence microscopy, SEM and cell counts showed that human embryonic palatal mesenchymal cells attached and spread out onto PPC and MTA-enriched PPCs within 24 h. Mitochondrial enzyme activity measured viable and metabolically active cells on all of the surfaces. After 72 h of growth, cell counts and metabolic activity were significantly higher (P < 0.05) on the grey-MTA enriched PPC surfaces, than on unmodified PPC and cpTi. The novel polymer/ceramic nanocomposites that were created with ultrafine powder coating technology were continuous, homogenous and nano-rough coatings that enhanced human mesenchymal cell attachment and growth.
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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".