Rotary jet spinning review – a potential high yield future for polymer nanofibers
Bibliographic record
Abstract
Polymeric nanofibers have been the focus of much research due to their continually evolving applications in fields such as biomedicine, tissue engineering, composites, filtration, battery separators, and energy storage. Although several methods of producing nanofibers have shown promise for large scale production, none have yet produced large enough volumes at a low cost to be the front runner in the field, and therefore the preferred choice for industrialization. Rotary jet spinning (RJS) could be the answer to high throughput, low cost, and environmentally friendly nanofiber production. Being exploited in only the last decade, it is a technology that has seen relatively little research, but one which could potentially be the answer to large scale manufacturing of polymer nanofibers. In this review, we focus on fundamental processing characteristics and initial application driven research. A comparison between existing nanofiber production methods is drawn with the key differences noted. Two methods of utilizing RJS in nanofiber production are discussed, namely spinning from a polymer melt, and solution-based spinning as is typically used in more traditional methods such as electrospinning. Modeling of the process is introduced, in which material selection and processing parameters play an important role.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".