Rotary jet spinning review – a potential high yield future for polymer nanofibers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it