Response Surface Optimization of Electro-Spun Polyvinyl Alcohol Nano-Fiber Membrane Process Parameters and its Characterization
Bibliographic record
Abstract
An empirical exploration into the effects of time duration, voltage supply, concentration and flow rate on the membrane average fiber diameter and surface pore size distribution were done using response surface methodology (RSM) based on central compact design (CCD). The average fiber diameter and average surface pore diameter of the membrane (i.e. 110 nm and 130 nm, respectively) were obtained at optimum input parameters of 45 min, 10 wt. %, 12 kV and 1.0 mL/h for time duration, concentration, voltage supply, and flow rate, respectively. The optimization study shows that the predicted versus actual values of both membrane fiber diameter and surface pore diameter are at R2 = 0.96. In addition, the effect of glutaraldehyde on membrane crosslinking was also assessed for further studies. The results from FESEM images of the fabricated PVA nanofiber membranes using the optimized parameters revealed that the membranes showed smooth morphological structures without formation of beads. The thermo gravimetric analysis (TGA) results displayed an improvement in thermal stability after membrane crosslinking. From this study we have observed that the membrane average fiber diameter and surface pore diameter can be controlled by varying the electro-spinning parameters and can be utilized for wastewater treatment application.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".