Optimizing electrospinning parameters is critical for producing nanofibers with desired properties such as diameter, morphology, and mechanical strength. Design of Experiments (DOE) provides a systematic approach to identify the most influential parameters and their optimal settings. Key parameters include voltage, flow rate, and humidity, which significantly impact fiber formation. Statistical methods such as Taguchi and Response Surface Methodology (RSM) are widely used to minimize experimental runs while maximizing data quality.
The Taguchi method is a robust DOE approach that uses orthogonal arrays to study multiple parameters with minimal experiments. It focuses on reducing variability by identifying optimal conditions that are less sensitive to noise factors. In electrospinning, a typical Taguchi design may include three levels for each parameter:
- Voltage (kV): Low (10), Medium (15), High (20)
- Flow rate (mL/h): Low (0.5), Medium (1.0), High (1.5)
- Humidity (%): Low (30), Medium (50), High (70)
An L9 orthogonal array can efficiently evaluate these parameters with nine experiments instead of a full factorial design requiring 27 runs. The signal-to-noise (S/N) ratio is calculated to determine the parameter combination that maximizes consistency in fiber diameter. For example, a study optimizing polycaprolactone (PCL) nanofibers found that voltage had the highest contribution (45%) to fiber diameter uniformity, followed by flow rate (30%) and humidity (25%). The optimal conditions were 15 kV, 1.0 mL/h, and 50% humidity, producing fibers with the least diameter variation.
Response Surface Methodology (RSM) is another powerful DOE technique that models the relationship between parameters and responses using quadratic equations. Central Composite Design (CCD) or Box-Behnken Design (BBD) are commonly used in RSM. A BBD for electrospinning might include:
- Voltage: 10–20 kV
- Flow rate: 0.5–1.5 mL/h
- Humidity: 30–70%
A second-order polynomial equation is fitted to experimental data to predict responses such as fiber diameter (Y):
Y = β0 + β1A + β2B + β3C + β12AB + β13AC + β23BC + β11A² + β22B² + β33C²
Where A, B, and C represent voltage, flow rate, and humidity, respectively. Coefficients (β) are determined via regression analysis. For instance, an RSM study on polyvinyl alcohol (PVA) nanofibers revealed that voltage and flow rate had a significant interaction effect. At low voltage (10 kV), increasing flow rate from 0.5 to 1.5 mL/h increased fiber diameter by 120 nm, whereas at high voltage (20 kV), the same flow rate change only increased diameter by 40 nm. The model predicted optimal conditions of 18 kV, 1.2 mL/h, and 40% humidity for minimal fiber diameter (150 nm).
Case studies demonstrate the effectiveness of DOE in electrospinning optimization. In one study, polyacrylonitrile (PAN) nanofibers were optimized for filtration applications using Taguchi methods. The analysis showed that humidity was the most critical factor, with 50% humidity yielding the highest filtration efficiency (98%). Another study on silk fibroin nanofibers employed RSM to enhance mechanical properties. The model identified 12 kV, 0.8 mL/h, and 60% humidity as optimal, resulting in a tensile strength of 45 MPa, a 30% improvement over baseline conditions.
Statistical validation is essential to confirm model accuracy. Analysis of variance (ANOVA) determines the significance of each parameter and interaction. A p-value < 0.05 indicates statistical significance. For example, an ANOVA table for an electrospinning DOE might show:
Source | Sum of Squares | df | Mean Square | F-value | p-value
Voltage | 4500 | 2 | 2250 | 12.5 | 0.002
Flow rate | 3000 | 2 | 1500 | 8.3 | 0.008
Humidity | 2500 | 2 | 1250 | 6.9 | 0.015
Error | 1800 | 10 | 180
The high F-values and low p-values confirm that all three parameters significantly affect fiber diameter. The model’s adequacy is further verified by the coefficient of determination (R²). An R² > 0.90 indicates a strong correlation between predicted and experimental values.
Practical considerations must also be addressed. For instance, while DOE identifies optimal conditions, real-world constraints such as equipment limitations or material properties may require adjustments. Additionally, interactions between parameters can complicate optimization. A follow-up confirmation experiment is recommended to validate predicted results under optimal settings.
In summary, DOE methods like Taguchi and RSM provide a structured framework for optimizing electrospinning parameters. By systematically analyzing voltage, flow rate, and humidity, researchers can achieve reproducible nanofiber production with tailored properties. Case studies underscore the utility of these approaches in diverse applications, from filtration to biomedical engineering. Statistical validation ensures reliability, while practical adjustments accommodate real-world variability. These methodologies streamline the optimization process, reducing time and resource expenditure while enhancing nanofiber performance.