A Study on the Influence of Polypropylene Melt Flow Index on Nonwoven Fibers Produced Through Hot Melt Centrifugal Spinning
Bibliographic record
Abstract
Abstract A hot melt centrifugal spinning process is used to manufacture polypropylene nonwoven textile such as those found in the filtering layers of medical masks. The fiber morphology and diameter distribution is influenced by the extrusion geometry and the polymer viscosity, often characterized by its melt flow index. These important geometric and physical aspects and their effects on the fiber quality are investigated in this work. The characteristics of the obtained nonwoven textile are also compared to those of the filtering layers found in a medical mask, usually made with the meltblown process. A custom-designed open-source lab-scale centrifugal spinning apparatus and the spinneret from a commercial cotton candy machine were used. This device was built at a very low cost while good quality fibers may be obtained compared to electrospinning. Its versatility allows to easily change the extrusion features. Here, a grid, nozzles, and a nozzlefree geometry, in which the polymer is extruded through a slit, were used. The behavior of five grades of polypropylene with five different melt flow indexes were compared in this process. Results show that fiber morphology improves when using the nozzle and nozzlefree geometries with a high melt flow index polymer, which were closer to the medical mask filtering layer.
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 | 0.001 |
| 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".