## 2 Materials and Methods
### Experimental Design and Statistical Analysis  
A Box-Behnken design in response surface methodology (RSM) was employed to study the influence of electrospinning parameters on the applied voltage and the average fiber diameter. Applied voltage was selected as the dependent variable for the response surface analysis in this study, compared to other studies. The reason was that the applied voltage responded to other parameters sensitively, using the conductive polymer solutions due to the addition of LiCl. LiCl concentration, PES concentration, feed rate, and tip-to-collector distance was selected as independent variables because these mainly influence to fiber-diameter which determine the pore size of ENMs. The four independent variables were LiCl concentration (X\({}_{1}\)), PES concentration (X\({}_{2}\)), feed rate (X\({}_{3}\)), and tip-to-collector distance (X\({}_{4}\)). The experimental range and coded levels of the independent variables are listed in Table 2. A total of 27 experimental points were needed to fit in the four variable Box-Behnken design (Table 3). In this study, a second-order polynomial model was used to describe the correlation between factors X and response Y. Applied voltage (Y\({}_{1}\)) and average fiber diameter (Y\({}_{2}\)) were set as the responses:  
\[\mathrm{Y}=b_{0}+\sum_{i=1}^{k}b_{i}x_{i}+\sum_{i=1}^{k}b_{ii}x_{i}^{2}+\sum_{ i<}\sum_{j=2}^{k}b_{ij}x_{i}x_{j}+\varepsilon \tag{2}\]  
where \(b_{0}\) is the constant coefficient and means the response when none of the factors work. \(b_{i}\), \(b_{ii}\), and \(b_{ij}\) are the linear, quadratic, and interaction coefficients, respectively. \(\varepsilon\) is the statistical error.  
Determination coefficient (R\({}^{2}\)) evaluated the coincident extent between observed response values and the model equation. R\({}^{2}\) ranges from 0 to 1. The lower values of R\({}^{2}\) indicate that the observed response values do not fit the model equation well; however, a high value of R\({}^{2}\) does not necessarily ensure the adequacy of the regression model. When additional factors are included in the model equation, R\({}^{2}\) will always increase. Therefore, an adjusted R\({}^{2}\) (R\({}_{\mathrm{adj}}\)2) is induced as follows:  
\[\mathrm{R}_{\mathrm{adj}}^{2}=1-\frac{n-1}{n-p-1}\Big{(}1-\mathrm{R}^{2}\Big{)} \tag{3}\]  
where \(n\) is the number of experiments and \(p\) is the number of coefficients in the regression model [14; 15]. An analysis of variance (ANOVA) and a t-statistic of responses were conducted to judge the statistical significance using Minitab 17.0 software (Minitab Inc., State College, PA, USA). The level of confidence was set to 0.05.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Independent Variables**} & \multirow{2}{*}{**Code**} & \multicolumn{3}{c}{**Real Values of the Coded Level**} \\ \cline{3-5}  & & **–1** & **0** & **1** \\ \hline LiCl concentration (\% (_v\)/\(v\))) & X\({}_{1}\) & 0.01 & 0.03 & 0.05 \\ PES concentration (\% (_v\)/\(v\))) & X\({}_{2}\) & 28 & 30 & 32 \\ Feed rate (mL/h) & X\({}_{3}\) & 0.3 & 0.4 & 0.5 \\ Tip-to-collector distance (cm) & X\({}_{4}\) & 20 & 25 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental range and coded levels of the independent variables for fabricating PES nanofibers.