Bibliographic record
Abstract
A key objective in electrospinning is generating fibers of nanoscale diameter consistently and reproducibly. Considerable effort has been devoted to understanding how the parameters affect the spinnability and more specifically the diameter of the fibers resulting from the electrospinning process. Many processing parameters that influence the spinnability and the physical properties of nanofibers have been identified. These parameters include process parameters such as electric field strength, flow rate and spinning distance, spinning dope properties including concentration, viscosity and surface tension, etc., the spinning environment factors like humidity and the spinning setup factors such as the diameter of the orifice and the electrospinning angle. Through observation of the electrospinning process and analyzing these parameters, some governing models have been built and simulations of the motion of jet have been carried out. In this chapter, several main existing models and simulation works will be introduced to help readers gain an understanding of the concept of electrospinning. Electrospinning mechanism For a long time, the mechanism of electrospinning for forming nanoscaled fibers was believed to be a result of a “split” as seen by the naked eye (Fig. 4.1a). The “splitting” is explained by Doshi and Reneker [1, 2] that, as the jet diameter decreases, the surface charge density increases, resulting in high repulsive forces which split the jet into smaller jets splay. When a high-speed camera was used in the investigation of electrospinning jet, unstable bending, also known as “whipping” of jet, was observed, and the “bending instability” started being widely accepted as the electrospinning mechanism, as shown in Fig. 4.1b. As described [3, 4], the electrospun jet vigorously bent spirally and stretched inside a conical envelope resulting in a huge stretch ratio and a nanoscale diameter.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.010 |
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".