Fiber Splitting of Bicomponent Meltblown Nonwovens by Ultrasonic Wave
Bibliographic record
Abstract
Many technologies have been used to produce finer fibers due to their super advantages such as higher specific surfacearea, filtration/barrier property and absorption, as well as moderate porosity. Finer fibers are thus the great interest ofmany researchers in the nonwoven world, and many technologies have been used to make finer fibers. In this study, theauthors addressed a novel avenue to produce finer fibers by splitting side-by-side bicomponent meltblown nonwovenscomposed of polyethylene terephthalate (PET) and polyamide 6 (PA6) by means of ultrasonic wave, in caustic soda andbenzyl alcohol solutions respectively. The efficiency of fiber splitting was characterized in terms of dyeingratio/percentage of the tested webs. Other properties were also examined, including fiber diameter, web weight loss, airpermeability and thickness. In addition, SEM was used to observe fiber damage and fiber structure in the relevant webs.
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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) | 0.000 | 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".