A non-damaging chemical amination protocol for poly(ethylene terephthalate) – application to the design of functionalized compliant vascular grafts
Bibliographic record
Abstract
Bioengineering approaches have been intensively applied to create small diameter vascular grafts using artificial materials. However, a fully successful, high performing and anti-thrombogenic structure has not been achieved yet. In this study, we present the first step of a process aiming at biofunctionalizing previously designed compliant polyethylene terephthalate (PET) scaffolds (Moreno et al., 2011). The main challenge of such a surface modification is to prevent the bulk polymer from any damage, so that it preserves the mechanical properties that the structures have been designed for. In that endeavor, an aminated long-chain polymer (polyvinylamine, PVAm) was used as an aminolysis reagent to get amine (–NH2) moieties only on the very surface of PET. Different reaction conditions were assayed, leading to a large range of amino group densities associated with slight variations of the planar tensile properties. These results were in stark contrast with those generated with a common small diamine substrate (ethylenediamine, EtDA), as the latter yielded a strong degradation of the mechanical properties for comparable amine densities. Tubular mechanical assays were then carried out on PVAm-functionalized PET scaffolds. The latter showed a compliance match with arteries under the chosen reaction conditions, as initially observed for pristine PET tubular scaffolds.
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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) | 0.001 | 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".