Strain localization and yielding dynamics in disordered collagen networks

TL;DR

This study investigates the yielding behavior of disordered collagen networks, revealing strain localization and network detachment mechanisms, with implications for tissue engineering and scaffold design.

Contribution

It provides a detailed analysis of local deformation and failure in collagen networks, linking macroscopic rheology to microscopic dynamics and network architecture.

Findings

Differential shear modulus peaks before network yields

Strain localization leads to network slippage and detachment

Yield stress and strain depend on network architecture parameters

Abstract

Collagen is the most abundant extracellular-matrix protein in mammals and the main structural and load-bearing element of connective tissues. Collagen networks show remarkable strain-stiffening properties which tune the mechanical functions of tissues and regulate cell behaviours. Linear and non-linear mechanical properties of in-vitro disordered collagen networks have been widely studied using rheology for a range of self-assembly conditions in recent years. However, a one to one correlation between the onset of macroscopic network failure and local deformations inside the sample is yet to be established in these systems. Here, using shear rheology and in-situ high-resolution boundary imaging, we study the yielding dynamics of in-vitro reconstituted networks of uncrosslinked type-I collagen. We find that in the non-linear regime, the differential shear modulus ($K$) of the network initially increases with applied strain and then begins to drop as the network starts to yield beyond a critical strain (yield strain). Measurement of the local velocity profile using colloidal tracer particles reveals that beyond the peak of $K$, strong strain-localization and slippage between the network and the rheometer plate sets in that eventually leads to a detachment. We generalize this observation for a range of collagen concentrations, applied strain ramp rates, as well as different network architectures obtained by varying the polymerization temperature. Furthermore, by fitting the stress vs strain data with a continuum affine network model, we map out a state diagram showing the dependence of yield-stain and -stress on the reduced persistence length and mesh size of the network. Our findings can have broad implications in tissue engineering, particularly, in designing highly resilient biological scaffolds.