High‐Throughput Production of Gelatin‐Based Touch‐Spun Nanofiber for Biomedical Applications
Bibliographic record
Abstract
Nanofiber production techniques have become increasingly important due to their wide range of applications. However, the complex design of the setup and difficulty in scaling up to high production rate have limited the industrial applicability of some of the conventional fiber generation techniques such as electrospinning. Herein, the touch spinning method is scaled up for nanofiber production using a simple rotating drawing setup with polymers that are relevant for biomedical applications such as polyethylene oxide and gelatin. The process is amenable to use of benign solvent such as water and production of a wide variety of submicron‐scale gelatin‐based nanofibers at a high throughput (≈2.45 g hr−1 with the single channel flow), which is an order of magnitude higher than those produced by other fiber generation methods is shown. The parametric study indicates that the fiber production process can be tuned at a desired rate without sacrificing the fiber quality by simply altering the number of drawing rods, the size of the rotating disk, and the number of solution flow supply channels. The utility of this technique for different biomedical applications such as cell culture and air filtration applications is also demonstrated.
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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) | 0.001 | 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".