Structure and properties of slow-resorbing nanofibers obtained by (co-axial) electrospinning as tissue scaffolds in regenerative medicine
Bibliographic record
Abstract
We investigated the structure and properties of PCL 10 nanofiber, PCL 5 /PCL 10 core-shell type nanofibers, as well as PCL 5 /PCL Ag nanofibres prepared by electrospinning. For the production of the fibre variants, a 5-10% solution of polycaprolactone (M w = 70000-90000), dissolved in a mixture of formic acid and acetic acid at a ratio of 70:30 m/m was used. In order to obtain fibres containing PCL Ag 1% of silver nanoparticles was added. The electrospin was conducted using the above-described solutions at the electrostatic field. The subsequent bio-analysis shows that synthesis of core-shell nanofibers PCL 5 /PCL 10 , and the silver-doped variant nanofiber core shell PCL 5 /PCL Ag by using organic acids as solvents is a robust technique. Such way obtained nanofibres may then be used in regenerative medicine for extracellular scaffolds: ( i ) for controlled bone regeneration due to the long decay time of the PCL, ( ii ) and as carriers of drug delivery nanocapsules. Furthermore, the used solvents are significantly less toxic than the solvents for polycaprolactone currently commonly used in electrospin, like for example chloroform (CHCl 3 ), methanol (CH 3 OH), dimethylformamide (C 3 H 7 NO) or tetrahyfrofurna (C 4 H 8 O), hence the presented here electrospin technique may allow for the production of multilayer nanofibres more suitable for the use in medical field.
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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) | 0.000 | 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".