NanoTiO<sub>2</sub>-Enriched Biocompatible Polymeric Powder Coatings: Adhesion, Thermal and Biological Characterizations
Bibliographic record
Abstract
The success of orthopedic and dental implants largely depends on their biocompatibility with the surrounding body environment and the biocompatibility depends on the physical, chemical, mechanical, topographical and biological properties of the implant materials chosen. Since the last few decades, titanium and its alloys have been among the most widely used ones due to their superior biocompatibility and mechanical properties; however, pure titanium needs to be pre and/or post treated chemically or physically to maintain appropriate textures and surface roughness. In the present study, TiO2 nanoparticles incorporated polymeric powder coatings consisting of smooth and micro-nanoscale roughness were developed that exhibited biocompatibility towards Human Embryonic Palatial Mesenchymal (HEPM) Cells. In addition, an experimental set up was designed and executed to evaluate the adhesion/ bond strength of the coating and to measure the load bearing capacity that the coatings can withstand before being detached from the substrate. Coating’s topographical features were analyzed by using Scanning Electron Microscopy (SEM). Differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) were performed to evaluate the thermal stability of the coating materials.
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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".