Fabrication of high performance chitosan/polyvinyl alcohol nanofibrous mat with controlled morphology and optimised diameter
Bibliographic record
Abstract
The main aim of the present study is to fabricate a high performance chitosan (CS)/polyvinyl alcohol (PVA) electrospun nanofibrous mat having a high content of CS, a desirable morphology (defect‐free structure) and a superfine diameter (approx. 100 nm). As electrospinning of constructions containing CS is known as a complex process, it is necessary to employ systematic control and optimisation of processes. In this regard, the controlling and optimisation of the processes were followed by two subsequent stages. In the first stage, morphology controlling parameters were investigated with respect to CS/PVA solution characteristics including CS concentration, solvent concentration and the content of the partner polymer (PVA). In the second stage, in order to attain the finest possible diameter, process modelling was carried out in terms of processing parameters (applied voltage, nozzle‐collector distance and feed rate) by using response surface methodology (RSM). According to the experimental results of the first stage, the best morphological structure containing the highest content of CS was obtained under 3% (w/v) of CS, concentrated acetic acid (90%) and 20% weight ratio of PVA. The significance of the applied model was confirmed by statistical approaches and the effect of the selected parameters on the diameter was studied. Experimentally, the finest diameter of 104 ± 18 nm was obtained under optimised processing parameters determined from the RSM technique. The experimental value of the nanofibre diameter was in close agreement with the predicted value in which the prediction error of the model was only 1.92% confirming the high reliability of the applied model.
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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".