Nano-TiO 2 Enriched Polymeric Powder Coatings Support Human Mesenchymal Cell Attachment and Growth
Bibliographic record
Abstract
The objective of this study was to utilize ultrafine powder coating technology to prepare (PPC) that can support human mesenchymal cell attachment and growth. Resins were modified with titanium dioxide and polytetrafluoroethylene (PTFE), and enriched with either SiO(2) or TiO(2) nanoparticles (nSiO(2) or nTiO(2)) to create continuous PPC. Scanning electron microscopy (SEM) revealed complex surface topographies with nano features, and energy dispersive X-ray (EDX) analysis with Ti mapping confirmed a homogenous dispersion of the material. SEM and inverted fluorescence microscopy showed that human embryonic palatal mesenchymal (HEPM) cells attached and spread out on the PPC surfaces, particularly those enriched with nTiO( 2). Cell counts were higher, and the MTT assay measured more metabolic activity from the nTiO(2) enriched PPCs. Furthermore, these cellular responses were enhanced on PPC surfaces that were enriched with a higher concentration of nTiO(2) (2% vs. 0.5%), and appeared comparable to that seen on commercially pure titanium (cpTi). Therefore the nTiO( 2) enrichment of PPC was shown to favor human mesenchymal cell attachment and growth. Indeed, this modification of the materials created continuous surface coatings that sustained a favorable cellular response.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".