3D printed multiplexed electrospinning sources for large-scale production of aligned nanofiber mats with small diameter spread
Bibliographic record
Abstract
We report the design, fabrication, and characterization of novel, low-cost, and modular miniaturized nanofiber electrospinning sources for the scalable production of non-woven aligned nanofiber mats with low diameter variation. The devices are monolithic arrays of electrospinning emitters made via stereolithography; the emitters are arranged so each element has an independent line of sight to a rotating collector surface. Linear and zigzag emitter packing were evaluated using a PEO solution with the aim of maximizing the throughput of nanofibers with the smallest diameter and narrowest distribution. Current versus flowrate characterization of the devices showed that for a given flowrate a zigzag array produces more current per emitter than a linear array of the same emitter pitch and array size. In addition, the data demonstrate that larger and denser arrays have a net gain in flow rate per unit of active length. Visual inspection of the devices suggests uniform operation in devices with as many as 17 emitters with 300 μ m inner diameter and 1.5 mm emitter gap. Well-aligned nanofiber mats were collected on a rotating drum and characterized; the 17-emitter device produced the same narrow nanofiber distribution (∼81 nm average diameter, ∼17 nm standard deviation) for all tested flow rates, which is strikingly different to the performance shown by 1-emitter sources where the average fiber diameter significantly increased and the statistics notably widened when the flowrate increases. Therefore, the data demonstrate that massively multiplexing the emitters is a viable approach to greatly increase the throughput of non-woven aligned nanofiber mats without sacrificing the statistics of the nanofibers generated. The production of dry nanofibers by the 17-emitter array is estimated at 33.0 mg min −1 (1.38 mg min −1 per mm of active length), which compares favorably with the reported multiplexed electrospinning arrays with emitters distributed along a line.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".