Characterization and comparison of N‐, O‐, and N+O‐functionalized polymer surfaces for efficient (HUVEC) endothelial cell colonization
Bibliographic record
Abstract
Surface modifications are often required to enhance cell adhesion and growth around implanted biomaterials. This study compares various functionalization processes in their ability to create high densities of oxygen‐ and/or nitrogen‐containing functional groups, mostly on a polymeric biomaterial, polyethylene terephthalate (PET). Primary amine (NH2)‐rich surfaces were prepared by low‐pressure plasma‐polymerization (L‐PPE:N), plasma modification (functionalized PET, “PETf”), chemical vapour deposition (Parylene diX AM), and grafting of polyallylamine (PAAm). Plasma polymerization was also used to obtain oxygen‐rich (L‐PPE:O) as well as hybrid (L‐PPE:O,N) films, which were respectively compared to oxygen‐rich tissue culture polystyrene (TCP) and hybrid (Primaria™) culture plates. Compositions and bond types were studied by X‐ray photoelectron spectroscopy. Finally, the effect of each surface on cell adhesion and growth was assessed using human umbilical vein endothelial cells (HUVECs). Amine‐containing surfaces manifested a wide [NH2] range, up to 8.9%. Hybrid surfaces, Primaria™ and L‐PPE:O,N, showed lower [NH2] in spite of high [N], suggesting more varied and complex functionalities. Except for Parylene, all O‐ and NH2‐rich surfaces promoted HUVEC adhesion and growth similarly, despite differing chemical compositions. Primaria™ showed the best cell behavior, but L‐PPE:O,N did not reproduce this apparent synergistic effect. To conclude, both N‐ and O‐rich surfaces displayed good cell‐colonization properties, particularly plasma polymers, while “hybrid” surfaces appear somewhat ambiguous and call for further investigation.
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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".