Electrospinning-Data.org is a FAIR data platform that organizes electrospinning experiments into a structured, failure-inclusive corpus to enable predictive modeling and inverse design of nanofiber morphologies.
Zenodo, 2025
2 Pith papers cite this work. Polarity classification is still indexing.
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Solution concentration is the only robust feature across ML models for electrospinning while flow rate and applied voltage show high model-dependent variability in importance rankings.
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Electrospinning-Data.org: A FAIR, Structured Knowledge Resource for Nanofiber Fabrication
Electrospinning-Data.org is a FAIR data platform that organizes electrospinning experiments into a structured, failure-inclusive corpus to enable predictive modeling and inverse design of nanofiber morphologies.
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Cross-Model Consistency of Feature Importance in Electrospinning: Separating Robust from Model-Dependent Features
Solution concentration is the only robust feature across ML models for electrospinning while flow rate and applied voltage show high model-dependent variability in importance rankings.