☆ 4.3 Article

Taylor cone height as a tool to understand properties of electrospun PVDF nanofibers

JOURNAL OF ELECTROSTATICS (2022)

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Article Materials Science, Multidisciplinary

Towards an interpretable machine learning model for electrospun polyvinylidene fluoride (PVDF) fiber properties

Shrutidhara Sarma et al.

Summary: A robust understanding of the structure-property relations of electrospun fibers is crucial for device design. Machine learning techniques are employed in this study to model the fiber diameter of electrospun polyvinylidene fluoride (PVDF) polymer. Experimental attributes such as feed, polymer concentration, Flory-Huggins Chi parameter, and relative energy difference were found to have the most impact on modeling fiber diameter. This research overcomes limitations in existing literature and bridges the gap between experimental and computational studies.

COMPUTATIONAL MATERIALS SCIENCE (2022)

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Review Chemistry, Analytical