Impact of electrospinning process parameters on the measured current and fiber diameter
Bibliographic record
Abstract
In this article, polycaprolactone (PCL) nanofibres were processed by electrospinning using a 3:1 ratio of tetrahydrofuran to methanol as solvent. The solvent choice was motivated by the possibility of greener alternatives to the halogenated compounds most often used for electrospinning. The morphologies and fiber diameters resulting from the electrospinning of PCL solutions at room temperature under various conditions are presented in this article. The material morphology was characterized using scanning electron microscopy and a measuring software. The process was optimized for smaller fibers with a narrower fiber diameter distribution by studying parameters such as polymer concentration, applied voltage, the tip to collector distance (TCD), and the solution flow rate. A comparison analysis was used to separate the current resulting from whipping and that resulting from spraying at high voltage. The fiber diameters obtained under various processing conditions were effectively modeled using the terminal jet theory, referenced in several works. Process parameters were optimal for a 20% PCL concentration spun at a flow rate of 0.5 mL/h, with a TCD of 15 cm and an applied voltage of 8 kV. Fibers spun under these conditions displayed diameters of 546 ± 173 nm. POLYM. ENG. SCI., 55:2576–2582, 2015. © 2015 Society of Plastics Engineers
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".