Login / Signup

Prolonged in situ self-healing in structural composites via thermo-reversible entanglement.

Alexander D SnyderZachary J PhillipsJack S TuricekCharles E DiesendruckKalyana B NakshatralaJason F Patrick
Published in: Nature communications (2022)
Natural processes continuously degrade a material's performance throughout its life cycle. An emerging class of synthetic self-healing polymers and composites possess property-retaining functions with the promise of longer lifetimes. But sustained in-service repair of structural fiber-reinforced composites remains unfulfilled due to material heterogeneity and thermodynamic barriers in commonly cross-linked polymer-matrix constituents. Overcoming these inherent challenges for mechanical self-recovery is vital to extend in-service operation and attain widespread adoption of such bioinspired structural materials. Here we transcend existing obstacles and report a fiber-composite capable of minute-scale and prolonged in situ healing - 100 cycles: an order of magnitude higher than prior studies. By 3D printing a mendable thermoplastic onto woven glass/carbon fiber reinforcement and co-laminating with electrically resistive heater interlayers, we achieve in situ thermal remending of internal delamination via dynamic bond re-association. Full fracture recovery occurs below the glass-transition temperature of the thermoset epoxy-matrix composite, thus preserving stiffness during and after repair. A discovery of chemically driven improvement in thermal remending of glass- over carbon-fiber composites is also revealed. The marked lifetime extension offered by this self-healing strategy mitigates costly maintenance, facilitates repair of difficult-to-access structures (e.g., wind-turbine blades), and reduces part replacement, thereby benefiting economy and environment.
Keyphrases
  • reduced graphene oxide
  • mental health
  • healthcare
  • life cycle
  • single cell
  • aqueous solution
  • small molecule
  • high throughput
  • mass spectrometry
  • high resolution
  • machine learning
  • electronic health record