Systematic Parameter Optimization for Electrospraying of PVA and PVP Aqueous Solutions
Bibliographic record
Abstract
This study systematically optimizes the key electrospraying parameters—flow rate, applied voltage, and nozzle-collector distance—for generating polymer micro/nanospheres from aqueous solutions of polyvinyl alcohol (PVA) and polyvinylpyrrolidone (PVP). Solutions at concentrations of 10%–15% w/v were characterized by conductivity measurements, revealing a significant solvent-dependent effect (450 µS/m–590 µS/m for water vs. 44 µS/m–56 µS/m for ethanol). Through iterative testing, two distinct sets of optimal parameters were identified: 10% PVA at 20 µL/h, 25 kV, and 12 cm distance, and 15% PVP at 10 µL/h, 30 kV, and 14 cm distance. Statistical analysis (ANOVA) confirmed a significant interaction between polymer type and concentration on solution conductivity (p< 0.05). Strict environmental control (≤24 °C, ≤44% RH) was essential for process stability. Optical microscopy confirmed the formation of structures under the optimized conditions. This work establishes a reproducible parametric framework for the electrospraying of PVA and PVP, providing a critical foundation for the subsequent development of functional polymer particles for potential applications in catalysis and drug delivery.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".