Electrospinning & Microtube-Embedded Hydrogel
Design-of-experiments work on polymer microfiber fabrication — mapping voltage, distance, and solution parameters to fiber diameter, then building a predictive model.
- Minitab
- Python
- Electrospinning
- Design of Experiments
- Predictive modeling
- Statistical analysis
Architecture
Undergraduate research at Binghamton University — experimental design, fabrication, and statistical modeling for medical applications.
Architecture — abstract
- L6Fabrication
Electrospinning parameter runs
- settings· Process
- samples· Measurement
- L5Measurement
Fiber diameter characterization
- samples· Fabrication
- dataset· Statistics
- L4Statistics
Minitab DOE analysis
- dataset· Measurement
- effects· Model
- L3Model
Python predictive model
- effects· Statistics
- predictions· Validation
- L2Validation
Held-out verification & tuning
- predictions· Model
- parameters· Process
- L1Process
Optimized fabrication window
- parameters· Validation
- settings· Fabrication
Closed loop: model outputs inform the next fabrication run.
Problem
Electrospun fiber diameter drives everything downstream in microtube-embedded hydrogel fabrication, but the process is sensitive to a web of interacting parameters — applied voltage, collection distance, flow behavior — that don't respond linearly. Trial-and-error parameter sweeps waste material and don't generalize. The process needed to be characterized systematically and captured in a model that could predict outcomes before running new experiments.
Approach
Structure the fabrication problem as a designed experiment. Collect data across controlled variations of the key parameters — distance, voltage, and their interactions — with measured fiber diameters as the response. Analyze the results in Minitab for statistical significance and effect sizes, then carry the relationships into Python for predictive modeling: regression on the process parameters, validation against held-out observations, and iteration until the model's predictions are trustworthy enough to guide fabrication decisions rather than follow them.
Results
- Experimental dataset mapping fabrication parameters to fiber diameter
- Statistically characterized effects of voltage, distance, and interactions
- Predictive model for fiber diameter from process parameters
- Process window optimized for the target microtube-embedded hydrogel application
Lessons learned
- Interactions matter more than main effects — voltage and distance didn't behave independently.
- A clean dataset beats a clever model; discipline in collection paid off in analysis.
- Statistics is an engineering tool: Minitab for significance, Python for prediction, each where it fits.
- Physical processes reward patience — optimization was iterative, not a single sweep.