Rapid prototyping of a miniaturized Electrospinning setup for the production of polymer nanofibers
Bibliographic record
Abstract
ABSTRACT Fabrication of polymeric micro/nanofibers with controllable size, density, orientation, and composition is required for their translation into functional devices and materials. Electrospinning (ES) is a frequently used fiber fabrication technique, where ES parameters such as the applied electric field strength, architecture of the setup, and solution composition are manipulated to control the fiber properties. Here, we present a bench‐top method for fabricating miniaturized, integrated, and highly tunable ES setups based on shrinkable polymer substrates. We show that using a combination of numerical modeling and controlling different parameters in the ES setup, including the spinneret to collector distance, and spinneret and collector designs, it is possible to tune the density, alignment, and orientation of electrospun fibers. In this way, we have produced 300–600 nm wide poly(ethylene oxide) fibers arranged as nonwoven mats on planar electrodes, aligned fibers on electrode edges, and individual suspended fibers spanning gaps between collector electrodes. The ability to rapidly prototype ES setups should enable us to study the effects of spinneret–collector configurations on fiber morphology, distribution, and conformation and to aid in the development of miniaturized ES setups designed to serve specific applications. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014, 131, 40629.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".