An integrated experimental-computational investigation of the mechanical behavior of random nanofiber networks.

Abstract

An integrated experimental-computational methodology was developed to study the mechanical behavior of random polymer nanofiber networks with controlled network structural parameters. Random nanofiber networks, comprised of continuous polyethylene oxide (PEO) nanofibers with ∼250 nm diameter and controlled mean fiber segment length, were designed with a computer algorithm and printed via near-field electrospinning. The structure of the same networks served as input to a computational model to obtain predictions of the macroscopic mechanical response. This methodology provides consistency in fabricating, testing and simulating nominally identical random fiber networks. Specimens with 500 to 5000 nanofibers were subjected to uniaxial tension and compared to modeling predictions for the network mechanical behavior. The predictions by the computational model, with inputs from the experimental network structure, the measured single PEO nanofiber properties, and the fiber crimp parameter, agreed with the experimental results both quantitatively and with respect to the dependence of the measured quantities on the network parameters. The network stiffness and strength followed a power-law scaling with the network density, with exponents 2.78 ± 0.15 and 1.59 ± 0.04, respectively, while the network stretch at failure gradually decreased with increasing network fiber density. Finally, the experimentally determined network toughness demonstrated a rather weak power-law dependence on the network fiber density (exponent of 1.18 ± 0.12).