Improvement of mechanical properties of collagen electrospun mats by halloysite nanotubes
Bibliographic record
Abstract
Collagen electrospun fibers had emerged as a promising scaffold for tissue engineering applications, nonetheless, pristine collagen fibers fail to provide of adequate mechanical properties. Therefore, here we propose the addition of halloysite nanotubes (HNT) into collagen solution for the obtention of nanofibrous mats with improved mechanical performance. Collagen was isolated and purified from tilapia skin and different concentrations of HNT (0.5, 1.0, and 2.0 %wt) were added to further spin the collagen-HNT solutions. HNT incorporation augmented the elongation at break in 800% but not in a linear manner, the smallest concentration of HNT used was the one with the better results, probably due to the agglomeration of HNT at higher concentrations as shown by SEM micrographs. Finally, the human dermal fibroblast (HDF) cell viability assay demonstrated that COL-HNT membranes were biocompatible up to a concentration of less than 1.0% and that concentrations greater than 2.0% significantly affect membrane permeability, subsequently leading to the death of the cells. Our results show that HNT can be incorporated into collagen to obtain nanofiber scaffolds, with improved mechanical properties up to 0.5% of HNT, being important in the field of tissue engineering.
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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".