Surface modification of a polycarbonate-urethane using a vitamin-E-derivatized fluoroalkyl surface modifier
Bibliographic record
Abstract
Fluorinated surface-modifying macromolecules (SMMs) have been previously reported on and shown to limit the hydrolytic degradation of polyurethanes. The SMM molecules achieve this effect by allowing for the selective migration of terminal fluorinated groups to the polymer's surface, which may then shield more hydrolytically-sensitive groups in the base polyurethane backbone. A further extension of the SMM concept would be to utilize the migration of the fluorine tails to simultaneously deliver biologically active moieties to the surface. This study explored the synthesis and characterization of a vitamin-E (natural anti-oxidant) coupled surface modifier, as a model for the bioactive SMM concept. The SMM was synthesized using lysine diisocyanate (LDI), polycarbonate diol (PCN), and a fluoroalcohol. By derivatizing the LDI pendant ester, vitamin E was coupled to the SMM. The vitamin-E SMM was physically characterized using gel-permeation chromatography (GPC) and its anti-oxidant activity was assessed in the presence of 0.1 mM NaOCl. Polymer degradation experiments were carried out using 10 mM NaOCl incubation solutions, and the relative material breakdown was assessed using GPC and scanning electron microscopy (SEM). The results indicate that while the fluoro-component reduced damage of the PU, the bioactive component achieved a further deactivating effect. A similar action may also be effective against superoxide anions generated by human macrophages.
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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".