Dry Spinning Based Spinneret Based Tunable Engineered Parameters (STEP) Technique for Controlled and Aligned Deposition of Polymeric Nanofibers
Bibliographic record
Abstract
Polymeric nanofibers are finding increasing number of applications and hold the potential to revolutionize diverse fields such as tissue engineering, smart textiles, sensors, and actuators. Aligning and producing high aspect ratio fiber arrays (length/diameter > 2 000) in the sub-micron and nanoscale diameters has been challenging due to fragility of polymeric materials, thus making it difficult to deposit them as one dimensional structures functionally interfaced with other systems. Here, we present a pseudo dry spinning technique which allows precise control on fiber diameters and further allows deposition of fiber arrays in aligned configurations. Control on fiber diameters ranging from 50-500 nm and having lengths of several millimeters is achieved by altering the polymeric solution concentration. In the dilute and semi-dilute unentangled concentration domain droplets or beaded fibers are observed to form. Smooth uniform diameter fibers are observed to form at the onset of semi-dilute entangled concentration regime. For a given molecular weight, the increase in fiber diameter with increasing solution concentration is attributed to both the increase in the entanglement density and the decrease in the radius of gyration of solvated polymer molecules. Using this technique polymeric fiber arrays in single and multiple layers are demonstrated which can be used towards developing strong textiles, biological scaffolds, and sensor networks.
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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".