Computer-Aided Engineering of Stretchable Hollow Fibers to Enable Wearable Microfluidics

Melt-extruded hollow thermoplastic fibers are a new class of materials for advanced applications, i.e., robotic clothes, stretchable touch and twist sensors, and programmable knitted textiles. Under large mechanical deformation (strain), such materials exhibit hyperelasticity. However, fundamental knowledge of strain-energy density harnessing integrated computational materials engineering (ICME) has remained untapped for hollow fibers. Due to this key knowledge gap, most emerging applications demand iterative and costly approaches to producing and studying hollow fibers. Herein, a data-driven pathway harnessing the ICME is introduced to study, predict, and optimize the hyperelastic behavior of hollow fibers. MCalibration software is used to calibrate the material properties of the fibers utilizing the Three-Network Model (TNM) and emulated experimental stress-strain behavior in a finite-element-based multiphysics environment (COMSOL) with 99.99% confidence. The feasibility of predicting strain-energy density is shown to modulate shape and geometries for new understandings without running cost-incurring melt extrusions or experiments. Furthermore, the strain-energy density is leveraged as a tool to attach fibers on fabrics that otherwise need iterations. As a proof-of-concept, fibers to fabrics are attached for wearable microfluidic demonstrations. This ICME approach can be extended to design the next generation of pre-programmed, undiscovered functional devices fabricated from hyperelastic hollow fibers.