# Application of Response Surface Methodology for Optimization of Nanosized Zinc Oxide Synthesis Conditions by Electrospinning Technique

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Materials

_{w}= 40,000) were purchased from Sigma Aldrich (Amsterdam, The Netherlands). Al foil (~20 mm) and 99% pure ethanol were used.

#### 2.2. ZnAc-PVP Nanofiber Preparation and ZnO Synthesis

#### 2.3. Experimental Design, Statistical Analysis, and Optimization by RSM

_{1}: 12–16 kV), the distance between the collector and the nozzle (X

_{2}: 8–12 cm), and the calcination temperature (X

_{3}: 600–800 °C). Fifteen experiments were conducted based on the BB design and optimized by the RSM method. Each experiment was run twice, and the average response was taken into consideration. The ranges of minimum and maximum coded values of process parameters in the present study were fixed according to the initial trial runs (presented in Table 1). A second-order polynomial model with the coded independent variables (X

_{i},

_{j}) was used to obtain minimized-size ZnO particles (Y) as shown in the equation below (Equation (1)):

_{i}and X

_{j}define the independent variables, b

_{0}is the constant coefficient; b

_{i}is the coefficient of linear effect, b

_{ij}is the coefficient of interaction effect, b

_{ii}is the coefficient of quadratic effect, and n is the number of variables. To specify the significance of the model, an ANOVA (analysis of variance) was conducted. The response surface and contour plots of the model-predicted responses were applied to specify the interactive relations between the significant variables. Design Expert, v. 8.0.7.1 (Stat-Ease Inc., Minneapolis, MN, USA), was used for designing the tests as well as regression and graphical analysis of the obtained data.

#### 2.4. Characterization

## 3. Results and Discussions

#### 3.1. Response Surface Model

_{1}), tip-to-collector distance (X

_{2}), and applied potential (X

_{3})—were selected for the optimization process to form ZnO particles. The interaction effect of the chosen parameters on the response observed in the experimental runs can be explained using the analysis of variance (ANOVA). Furthermore, the adequacy of the model was examined using diagnostic diagrams, and the model should be validated by evaluating the optimum experimental conditions, as previously explained [45]. The optimization results show the parameter interaction effect, which consists of 15 experiments, as is presented in Table 2. For each experiment, the ZnO diameter (the response) was measured; a two-set average is noted in Table 3, column Y. When one decides if the overall results are significant, the F statistic must be used in combination with the p-value. The p-value indicates the degree to which the data is consistent with the null hypothesis. The successive p-value of <0.0001 and F value of 99.22 indicate significant model terms. In this scenario, there were key model terms: A, B, C, A

^{2}, B

^{2}, and C

^{2}. Only the quadratic terms for the two electrospinning variables made it into the refined model, which was an interesting finding [43]. Coefficient of variance (CV) indicates the reproducibility of the model, for which a value of less than 10% is desirable [46]. According to the results, the CV value is 3.38%, and the model was statistically valid. The predicted residual error sum of squares (PRESS) is cross validation used in data analysis to offer a statistical summary of model performance [47]; the obtained PRESS of the model is 58.71. The adjusted R

^{2}of 0.9546 in the improved model agreed reasonably well with the expected R

^{2}of 0.9424. Figure 2 depicts the relationship between the measured diameter of the ZnO particles and the models’ anticipated diameter. The linear correlation coefficient indicates reasonable agreement between the experimental and model values across the factor space.

#### 3.2. Response Surface Plots

#### 3.3. Response Surface Plotting and Characterization of ZnO Nanoparticles at Optimized Conditions

_{g}value was different from the band gap of bulk ZnO (3.37 eV). Bang gap can be attributed to the optical confinement effect, corresponding to the size and length of nanoparticles [66].

#### 3.4. XRD Patterns and Transmission Electron Microscopy (TEM)

## 4. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**SEM micrographs of the PVP-ZnAc fibers at a constant distance of 12 cm; a flow rate of 1 mL/h; and applied potential of (

**a**) 12 kV, (

**b**) 14 kV, and (

**c**) 16 kV.

**Figure 3.**Response surface plots of ZnO diameter (response) versus (

**a**) distance and temperature, (

**b**) voltage and temperature, and (

**c**) voltage and distance.

**Figure 4.**SEM images of the ZnO nanoparticles synthesized at a constant applied potential of 16 kV; a distance between collector and nozzle of 12 cm; a flow rate of 1 mL/h; and calcination temperature of (

**a**) 600 °C, (

**b**) 700 °C, and (

**c**) 800 °C.

**Figure 5.**(

**a**,

**b**) EDS mapping showing the distribution of ZnO nanoparticles, (

**c**) zinc, and (

**d**) oxygen synthesized at an applied potential of 16 kV, a distance of 12 cm, a flow rate of 1 mL/h, and a calcination temperature of 600 °C.

**Figure 6.**UV-Visible spectra and energy band gap of ZnO nanoparticles synthesized at a constant applied potential of 16 kV, a distance between collector and nozzle of 12 cm, a flow rate of 1 mL/h, and a calcination temperature of 600 °C.

**Figure 7.**(

**a**) TEM micrographs of ZnO nanoparticles synthesized at a constant applied potential of 16 kV, a distance between collector and nozzle of 12 cm, a flow rate of 1 mL/h, and a calcination temperature of 600 °C. (

**b**) XRD patterns of ZnO nanoparticles synthesized at a constant applied potential of 16 kV; a distance between collector and nozzle of 12 cm; a flow rate of 1 mL/h; and calcination temperatures of 600 °C, 700 °C and 800 °C.

Independent Variables | Factor X_{i} | Range and Level | ||
---|---|---|---|---|

−1 | 0 | +1 | ||

Applied potential (kV) | X_{1} | 12 | 14 | 16 |

Distance (cm) | X_{2} | 8 | 10 | 12 |

Calcination temperature (°C) | X_{3} | 600 | 700 | 800 |

Source | Sum of Squares | df | Mean Square | F Value | p-Value |
---|---|---|---|---|---|

Model | 982.28 | 3 | 327.43 | 99.22 | 0.0001 significant |

A-Temperature | 593.77 | 1 | 593.77 | 179.93 | 0.0001 |

B-Distance | 0.093 | 1 | 0.093 | 0028 | 0.8697 |

C-Applied potential | 9.19 | 1 | 9.19 | 2.79 | 0.1233 |

Residual | 36.30 | 11 | 3.30 | ||

Lack of fit | 35.62 | 9 | 3.96 | 11.64 | 0.0816 not significant |

Pure error | 0.68 | 2 | 0.34 | ||

Cor total | 1018.58 | 14 | |||

Std. Dev. | 1.82 | R^{2} | 0.9644 | ||

Mean | 53.79 | Adj R^{2} | 0.9546 | ||

CV % | 3.38 | Pred R-Square | 0.9424 | ||

PRESS | 58.71 | Adequate Precision | 22.609 |

Run Order | Calcination Temperature (°C) | Tip-to-Collector Distance (cm) | Applied Potential (kV) | Response, Y (ZnO Size in nm) | Predicted Values (ZnO Size in nm) |
---|---|---|---|---|---|

1 | 800 | 8.00 | 14.00 | 66 | 65.68 |

2 | 800 | 8.00 | 12.00 | 65 | 65.30 |

14 | 600 | 10.00 | 16.00 | 46 | 46.34 |

9 | 700 | 8.00 | 12.00 | 64.30 | 65.03 |

8 | 800 | 10.00 | 16.00 | 45.20 | 44.52 |

10 | 600 | 12.00 | 12.00 | 65.10 | 64.28 |

5 | 600 | 8.00 | 16.00 | 53.60 | 52.12 |

6 | 800 | 10.00 | 12.00 | 62.00 | 62.11 |

7 | 700 | 12.00 | 14.00 | 54.00 | 53.90 |

11 | 700 | 8.00 | 16.00 | 45.00 | 44.88 |

15 | 700 | 10.00 | 14.00 | 50.00 | 50.12 |

3 | 600 | 12.00 | 14.00 | 44.00 | 44.50 |

13 | 700 | 10.00 | 14.00 | 52.00 | 52.23 |

4 | 800 | 12.00 | 12.00 | 44.60 | 44.52 |

12 | 600 | 12.00 | 16.00 | 43.00 | 43.23 |

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**MDPI and ACS Style**

Rakhmanova, A.; Kalybekkyzy, S.; Soltabayev, B.; Bissenbay, A.; Kassenova, N.; Bakenov, Z.; Mentbayeva, A.
Application of Response Surface Methodology for Optimization of Nanosized Zinc Oxide Synthesis Conditions by Electrospinning Technique. *Nanomaterials* **2022**, *12*, 1733.
https://doi.org/10.3390/nano12101733

**AMA Style**

Rakhmanova A, Kalybekkyzy S, Soltabayev B, Bissenbay A, Kassenova N, Bakenov Z, Mentbayeva A.
Application of Response Surface Methodology for Optimization of Nanosized Zinc Oxide Synthesis Conditions by Electrospinning Technique. *Nanomaterials*. 2022; 12(10):1733.
https://doi.org/10.3390/nano12101733

**Chicago/Turabian Style**

Rakhmanova, Aizhan, Sandugash Kalybekkyzy, Baktiyar Soltabayev, Aiman Bissenbay, Nazym Kassenova, Zhumabay Bakenov, and Almagul Mentbayeva.
2022. "Application of Response Surface Methodology for Optimization of Nanosized Zinc Oxide Synthesis Conditions by Electrospinning Technique" *Nanomaterials* 12, no. 10: 1733.
https://doi.org/10.3390/nano12101733