Enhancing mechanical strength of electrospun nanofibers by coaxial electrospinning and thermal treatment
Bibliographic record
Abstract
Abstract Electrospinning of nonwoven nanofibrous mats has received significant attention in recent years due to the high versatility and porosity of electrospun mats. However, electrospun mats generally suffer from low mechanical strength. This work reports a method to improve the mechanical strength of nanofiber mats by coaxial electrospinning combined with thermal treatment, without incurring significant dimensional shrinkage. Coaxial polyacrylonitrile (PAN)/polyvinylidene fluoride‐hexafluoropropylene (PVDF‐HFP) mats showed no shrinkage when tested at temperatures up to 240°C for 20 min, compared to a shrinkage of 94% for the homogenous PVDF‐HFP mats when treated at 190°C for 20 min. When treated at 178°C for up to 30 min, the coaxial fibres consistently showed changes in thickness of less than 10% and no significant change in area. The coaxial PAN/PVDF‐HFP mats showed negligible changes in average porosity after treatment at 178°C for 20 min. The mechanical strength of the coaxial samples heat treated at 178°C for 5 min was 7.72 MPa, a 22% increase from that before heating, and a 54% increase compared to the as‐spun homogenous PVDF‐HFP. Thus, the proposed technique of combining heat treatment with coaxial morphologies demonstrates significant potential for improving mechanical strength without dimensional shrinkage.
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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 | 0.000 | 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".