Electrospinning of polyurethane/graphene oxide for skin wound dressing and its in vitro characterization
Bibliographic record
Abstract
Electrospinning polyurethane has been utilized as skin wound dressing for protecting skin wounds from infection and thus facilitating their healings, but also limited by its imperfect biocompatibility, mechanical and antibacterial properties. This paper presents our study on the addition of graphene oxide to electrospinning polyurethane for improved properties, as well as its in vitro characterization. Polyurethane/graphene oxide wound dressing was electrospun with varying amount of graphene oxide (from 0.0% to 2.0%); and in vitro tests was carried out to characterize the wound dressing properties and performance from the structural, mechanical, and biological perspectives. Scanning electron microscopy and Fourier-transform infrared spectroscopy were used to confirm the interaction between graphene oxide particles and polyurethane fibers, while the scanning electron microscopy images further illustrated that the wound dressing was of a porous structure with fibre diameters depending on the amount of graphene oxide added; specifically, 20 to 180 nm were for composite polyurethane/graphene oxide fibers and 600 to 900 nm for pure polyurethane. Our results also revealed that the hydrophilicity and swelling properties of the wound dressing could be regulated by the amount of graphene oxide added to the polyurethane/graphene oxide composites. Mechanical, antibacterial, and cytotoxicity properties of the composite polyurethane/graphene oxide wound dressing were examined with the results illustrating that the addition of graphene oxide could improve the properties of the electrospun wound dressing. Combined together, our study illustrates that electrospinning polyurethane/graphene oxide composite is promising as skin wound dressing.
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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".