Predicting the macroscopic response of electrospun membranes based on microstructure and single fibre properties.

In the present paper, the three-dimensional structure and macroscopic mechanical response of electrospun poly(L-lactide) membranes is predicted based only on the geometry and elasto-plastic mechanical properties of single fibres supplemented by measurements of membrane weight and volume, and the resulting computational models are used to study the non-affine micro-kinematics of electrospun networks. To this end, statistical parameters describing the in-plane fibre morphology are extracted from scanning electron micrographs of the membranes, and computational network models are generated by matching the porosity of the real mats. The virtual networks are compared against computed tomography scans in terms of structure, and against uniaxial tension tests with respect to their macroscopic mechanical response. The obtained virtual network structure agrees well with the fibre disposition in real networks, and the rigorous prediction of the mechanical response of two membranes with mean diameters of 1.10μm and 0.70μm captures the experimental behaviour qualitatively. Favourable quantitative agreement, however, is obtained only after lowering the Young's moduli, yield stresses and hardening slopes determined in single fibre tests, and after reducing the density of inter-fibre bonds in the model of the membrane with thinner fibres. The simulations thus demonstrate the validity and merits of the approach to study the multi-scale mechanics of electrospun networks, but also point to potential discrepancies between the properties of electrospun fibres within a network and those produced for single fibre characterisation, and highlight the existing uncertainty on the density and quality of bonds between fibres in electrospun networks.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2020. Published by Elsevier Ltd.)