Polyether ether ketone surface modification with plasma and gelatin for enhancing cell attachment
Bibliographic record
Abstract
Polyether ether ketone (PEEK) has shown great promise for implant and biomedical applications because of its excellent chemical, mechanical, and biocompatible properties. However, PEEK is bioinert, which causes weak cell adhesion and limits its use for biomedical applications such as bone implants. Therefore, the activation of the PEEK's surface for cell attachment is desirable. In this study, oxygen plasma and gelatin were used to modify PEEK's surface and the effects of surface roughness, wettability, and cell adhesion to the surface were studied. Surface roughness was measured using a laser scanning confocal microscope, and wettability was measured using the sessile drop method. There was no significant difference in the roughness of the three samples. The gelatin-coated surface showed higher wettability than the plasma-modified or control samples. The cell attachment and proliferation rate were assessed by scanning electron microscopy and the XTT assay, respectively. The XTT assay results indicated that a greater number of cells grew on the gelatin-coated PEEK surface than on the control or plasma-treated surfaces. These results confirmed that the plasma and gelatin treatments enhanced the biocompatibility of the PEEK samples. The increase in biocompatibility could make PEEK a better material candidate for treating bone related injuries and defects.
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".