Optimization of the Electrospun Niobium–Tungsten Oxide Nanofibers Diameter Using Response Surface Methodology
Bibliographic record
Abstract
The present research aimed to investigate the effect of working parameters on the electrospinning of niobium–tungsten oxide nanofibers and optimize the process using central composite design (CCD) based on the response surface methodology (RSM). An experiment was designed to assess the effects of five variables including the applied voltage (V), spinning distance (D), polymer concentration (P), flow rate (F), and addition of NaCl (N) on the resulting diameter of the nanofibers. Meanwhile, a second-order prediction model of nanofibers diameter was fitted and verified using analysis of variance (ANOVA). The results show that the diameter of the nanofibers was significantly influenced by all the variables except the flow rate. Some second-order and cross factor interactions such as VD, DP, PF, PN, and P2 also have significant effects on the diameter of the nanofibers. The results of the ANOVA yielded R2 and adjusted R2 values of 0.96 and 0.93 respectively, this affirmed that the predictive model fitted well with the experimental data. Furthermore, the process parameters were optimized using the CCD method and a maximum desirability function of 226 nm was achieved for the diameter of the nanofibers. This is very close to the 233 nm diameter obtained from a confirmatory experiment using the optimum conditions. Therefore, the model is representative of the process, and it could be used for future studies for the reduction of the diameter of electrospun nanofibers.
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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 | 0.000 |
| 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 | 0.001 | 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".