Optimization of the Electrospun Niobium–Tungsten Oxide Nanofibers Diameter Using Response Surface Methodology

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.

Optimization of the preparation process of electrostatic-solution blow spinning nanofiber yarn using response surface methodology

“…The residuals of the test data are randomly distributed between –2 and +2, all within the red line anomaly detection range, indicating a good correlation between the experimental data and the model predicted data. 30,38 Figure 2(c) compares the tested values of nanofiber yarn diameters with the predicted values under each group of experiments. It can be seen from the figure that the measured and predicted values overlap, further confirming the model accuracy, so the model can be used to represent the relationship between the response and the independent variables.…”

“…(b) expresses the distribution of the internally studentized residuals for the test data and the predicted values. The residuals of the test data are randomly distributed between -2 and þ2, all within the red line anomaly detection range, indicating a good correlation between the experimental data and the model predicted data 30,38. Figure2(c) compares the tested values of nanofiber yarn diameters with the predicted values under each group of experiments.…”

“…Fatile et al. 30 studied the effect of process parameters on the electrospinning of niobium oxide nanofibers based on the response surface methodology using the central composite design (CCD). Viana et al.…”

“…These process parameters affect the morphology and properties of the nanofibers, which in turn affect the morphology and properties of the yarn. Fatile et al 30 studied the effect of process parameters on the electrospinning of niobium oxide nanofibers based on the response surface methodology using the central composite design (CCD). Viana et al 31 prepared poly(vinyl alcohol)/ chitosan-cross-linked nanofibers by optimizing the electrospinning conditions through Box-Behnken design (BBD).…”