Fibrous electrospun polycaprolactone nanomat reinforced with halloysite nanotubes: Preparation and study of its potential application as tissue engineering scaffold
Bibliographic record
Abstract
Abstract In this work, the biocompatible and biodegradable polycaprolactone (PCL) was synthesized by a ring‐opening synthesis mechanism. To improve the mechanical and biological properties of the polymer, electrospun nanocomposite scaffolds were prepared using halloysite nanotubes (HNTs) as the reinforcing agent with the concentrations of 5, 10, 15, 20, and 25% (w/w) of PCL. PCL‐HNTs composites were prepared as fibrous nanomats by electrospinning method. The morphology and wettability of the electrospun PCL‐HNTs nanomats were investigated by scanning electron microscope images and water contact angel measurement and based on the structure of the fibers, fibers diameter and higher wettability, the PCL‐HNT composite containing 5% (w/w) of halloysite nanotubes (PCL + 5%HNTs) was selected as the proper composite and its application as tissue engineering scaffold was studied. The mechanical properties of the PCL + 5%HNTs composite was 2.6 times higher than that of PCL, as well as, comparatively higher thermal stability. To improve the antibacterial properties, HNTs were loaded with gentamycin sulfate (GM) prior to electrospinning and the PCl + 5%HNTs+GM composite were prepared and studied. The drug release profile showed that by the incorporation of HNTs into the PCL based composites, slow drug release was continued for 164 h, while neat HNTs were completed the drug release after 8 h. The GM loaded composite scaffold showed a high antibacterial effect for Staphylococcus aureus and Listeria monocytogenes , Escherichia coli . Adequate cell growth environment was provided by PCL + 5%HNTs, as indicated by the biocompatibility and protein adsorption test results. The hemolytic assay results showed a higher hemolysis value for the HNTs‐containing sample, but still lower than 5%.
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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".