Machine learning-guided morphological property prediction of 2D electrospun scaffolds: the effect of polymer chemical composition and processing parameters
Mohammad Hossein Golbabaei, Mohammadreza Saeidi Varnoosfaderani, Farshid Hemmati, Mohammad Reza Barati, Fatemehsadat Pishbin, S.A. Seyyed Ebrahimi
Abstract
, polyvinyl alcohol and polyvinyl alcohol/polypyrrole). Field-emission scanning electron microscope (FE-SEM) images were used to measure fiber diameter. These results demonstrated the efficacy of the proposed model in predicting the polymer nanofiber diameter and reducing the parameter space prior to the scoping exercises. This data-driven model can be readily extended to the electrospinning of various biopolymers.
Topics & Concepts
Materials sciencePolymerArtificial neural networkProperty (philosophy)FiberArtificial intelligenceComputer scienceComposite materialPhilosophyEpistemologyElectrospun Nanofibers in Biomedical ApplicationsConducting polymers and applicationsAdvanced Sensor and Energy Harvesting Materials