Enhancing mechanical strength of nanofiber mats by coaxialelectrospinning and thermal treatment
Bibliographic record
Abstract
Electrospun nanofibers find many engineering applications because of their porous structure, high specific surface area, and relative ease of manufacture. However, the applications of nanofiber-based engineering products are limited by their low mechanical strength. Current technologies to improve the mechanical strength often result in structural deformations, e.g., shrinkage. This work aims to improve the mechanical strength of nanofiber mats aimed by coaxial electrospinning and thermal treatment. Homogenous PVDF-HFP nanofibers prepared by electrospinning were heated at 178 C for 5 to 30 minutes. The thermal treatment increased the tensile strength of the nanofiber mats by 50% or greater. In addition, the porosity and electrolyte uptake of the heat-treated samples remained stable, indicating a minimal impact on the structure of the nanofiber mats prior to the onset of sudden severe shrinkage at 180 C. Nonetheless, the thicknesses of the mats varied with treatment time. The mechanical strength will be further improved by coaxial electrospinning, with PAN core and PVDF-HFP sheath, followed by thermal treatment. The corresponding effectiveness will be reported in terms of mechanical strength, physical deformation, and scalability of the heat treatment process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".