Characterization of an air‐spun poly(<scp>L</scp>‐lactic acid) nanofiber mesh
Bibliographic record
Abstract
It was previously showed that PLLA nanofiber mesh promoted good endothelial cell proliferation. A new technique was developed to produce nanofibers by air jet spinning inside the tubular shape of vascular prostheses and to characterize this nanofiber mesh. Polymer macromolecule stability was assessed by gel permeation chromatography. Thermal analyses were conducted with differential scanning calorimetry and dynamic mechanical analysis on PLLA nanofibers obtained with 4% and 7% solutions (w/v) in chloroform. Polyethylene terephthalate (PET) was also treated with atmospheric pressure dielectric barrier discharge under air or nitrogen atmosphere to optimize PLLA nanofiber adherence, assessed by peel tests. Air spinning induced a reduction of number-average molecular weight (M(n)) for the 7% PLLA solution but not for the 4% solution. The nanofibers were more crystalline and less sensible to viscoelastic relaxation as a function of aging in the 4% solution than in the 7% solution. Discharge treatment of the PET promoted identical surface modification on PET film and PET textile surfaces. Moreover, the best PLLA nanofibers adhesion results were obtained under nitrogen atmosphere. This study demonstrates that it is possible to coat the internal side of tubular vascular prostheses with PLLA nanofibers, and provides a better understanding of the air spinning process as well as optimizing nanofibers adhesion.
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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".