Investigating the effect of PGA on physical and mechanical properties of electrospun PCL/PGA blend nanofibers
Bibliographic record
Abstract
Abstract In the field of tissue engineering there is always a need for new engineered polymeric biomaterials which have ideal properties and functional customization. Unfortunately the demands for many biomedical applications need a set of properties that no polymers can fulfill. One method to satisfy these demands and providing desirable new biomaterials is by mixing two or more polymers. In this work, random nanofibrous blends of poly (ε‐caprolactone) (PCL) and polyglycolic acid (PGA) with various PCL/PGA compositions (100/0, 80/20, 65/35, 50/50, and 0/100) were fabricated by electrospinning method and characterized for soft‐tissue engineering applications. Physical, chemical, thermal, and mechanical properties of PCL/PGA blend nanofibers were measured by scanning electron microscopy (SEM), porosimetry, contact angle measurement, water uptake, attenuated total reflectance Fourier transform‐infrared spectroscopy (ATR‐FT‐IR), X‐ray diffraction (XRD), differential scanning calorimetric (DSC), dynamic mechanical thermal analysis (DMTA), and tensile measurements. Morphological characterization showed that the addition of PGA to PCL results in an increase in the average diameter of the nanofibers. According to these results, when the amount of PGA in the blend solution increased, the hydrophilicity and water uptake of the nanofibrous scaffolds increased concurrently, approaching those of PGA nanofibers. Differential scanning calorimetric studies showed that the PCL and PGA were miscible in the nanofibrous structure and the mechanical characterization under dry conditions showed that increasing PGA content results in a tremendous increase in the mechanical properties. In conclusion, the random nanofibrous PCL/PGA scaffold used in this study constitutes a promising material for soft‐tissue engineering. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2012
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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".