Electrospinning of High-Performance Nanofibres: State of the Art and Insights into the Path Forward
Bibliographic record
Abstract
Nanofibrous membranes have gained interest for their small pore size, light weight, and excellent filtration. When produced from high-performance polymers, nanofibrous membranes also benefit from excellent mechanical properties, thermal resistance, and chemical resistance. Electrospinning is a common method of producing high-performance nanofibres. However, there are still major challenges with the dissolution and electrospinning of these polymers, as well as in the performance of the resulting nanofibres, which is often less than what would be expected from a conventional high-performance fibre. This review assesses the state of progress in the electrospinning of five high-performance fibres: meta-aramid (m-aramid), para-aramid (p-aramid), polyamide-imide (PAI), polybenzoxazole (PBO), and polybenzimidazole (PBI). Polymers that can be readily dissolved in organic solvents, such as m-aramid, PAI, and PBI, have been more widely researched for electrospinning compared to those that can only be spun from precursors or dissolved in non-volatile solvents. Major focuses within the literature include optimizing the electrospinning process and improving the mechanical performance of the nanofibres. This review demonstrates a clear need for more standardized characterization methods and consideration for the longevity of the nanofibrous membranes. Future research should also focus on scale-up methods of electrospinning so that the benefits of nanofibres made from high-performance polymers can be leveraged by the industry.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".