Fabrication and Optimization of Electrospun Polyacrylonitrile Nanofiber for Application in Air Filtration
Bibliographic record
Abstract
Background and aims: In nanofibrous filters, morphological properties, diameter of fibers and porosity percent of media are the most filtration characteristics. Therefore, the present study aimed to optimize the electrospinning parameters for reaching to the desired values of the mentioned filter characteristics. Method: For this purpose, a study design was prepared using response surface methodology (RSM), in which electrospinning factors such as solution concentration, applied voltage and electrospinning distance were considered input variables and the fiber diameter, porosity, bead number and average bead diameter to average fiber diameter (ABD/AFD) ratio were considered the output variables. Morphological features of fibers and porosity of media were done through image processing approach of Scanning Emission Microscopy (SEM) images. Results: Maximum concentration in assessed range can provide the best morphology and also the maximum diameter. The highest correlation coefficient has been seen between fiber diameter and solution concentration (p <0.05, r=0.73). Porosity and applied voltage represent the strongest relationship (p >0.05, r=0.39). There was the significant relationship between both concentration and electrospinning distance and bead size (r=-1.6, r=0.56, respectively). Bead number was decreased specially with increase in concentration. Conclusion: Totally, RSM could well determine the relationship between input and response variables. High regression coefficient in mathematical models indicated the importance of the experimental values. The validation test shown the experimental data are in good agreement with the predicted ones.
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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) | 0.000 | 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".