Programming filamentous network mechanics by compression

TL;DR

This study shows that fibrous networks like fibrin can be mechanically programmed to become stiffer through compression, enabling precise control of their rigidity without changing composition.

Contribution

It introduces a method to mechanically program network stiffness via compression and develops a minimal model explaining the bond formation mechanism.

Findings

Compression induces irreversible stiffening of fibrin networks.

Network stiffness follows a universal power-law dependence on prestress.

The minimal model accurately predicts experimental results.

Abstract

Fibrous networks are ideal functional materials since they provide mechanical rigidity at low weight. Such structures are omnipresent in natural biomaterials from cells to tissues, as well as in man-made materials from polymeric composites to paper and textiles. Here, we demonstrate that fibrous networks of the blood clotting protein fibrin undergo a strong and irreversible increase in their mechanical rigidity in response to compression. This rigidification can be precisely predetermined from the level of applied compressive strain, providing a means to program the network rigidity without having to change its composition. To identify the mechanism underlying this programmable rigidification, we measure single fiber-fiber interactions using optical tweezers. We further develop a minimal computational model of adhesive fiber networks that shows that load-induced bond formation can explain the adaptation of the fibrin networks to compressive loading. The model predicts that the network stiffness after compressive programming obeys a universal power-law dependence on the prestress built in by new bond formation, which we confirm experimentally. The generality of this functional stiffening mechanism together with our ability to quantitatively predict it provides a new powerful approach to program the stiffness of fibrous materials and dynamically adapt them to different loading conditions.