Effect of Nanoclay Addition on the Morphology, Fiber Size Distribution and Pore Size of Electrospun Polyvinylpyrrolidone (PVP) Composite Fibers for Air Filter Applications
Bibliographic record
Abstract
The fabrication of Polyvinylpyrrolidone (PVP) electrospun layers for air filter applications is the target of this study. Solutions of 10% PVP containing 0, 3, 10 and 25 wt% nanoclay were used to fabricate electrospun fibers. Scanning electron microscopy showed that the fibers’ roughness increased by increasing the nanoclay content, and it was maximum at the nanoclay concentration of 25 wt%. Concurrently, nanoclay decreased the pore size considerably and increased the range of the fibers’ size distribution up to 100%. In addition, as the nanoclay concentration increased, the frequency distribution decreased abruptly for the larger fiber sizes and increased dramatically for the small fiber sizes. This phenomenon was correlated to the effect of nanoclay concentration on the conductivity of the solution. The solution’s conductivity increased from 1.7 ± 0.05 µS/cm for the PVP solution without nanoclay to 62.7 ± 0.19 µS/cm for the solution containing 25 wt% nanoclay and destabilized the electrospun jet, increasing the range of fiber size distribution. Therefore, the PVP solution containing 25 wt% nanoclay has potential characteristics suitable for air-filter applications, owing to its rougher fibers and combination of fine and thicker fibers.
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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 | 0.001 |
| 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".