Additive manufacturing of molecular architecture encoded stretchable polyethylene glycol hydrogels and elastomers

POSTER

Abstract

Polyethylene glycol (PEG) networks are widely used in biomedical applications and are emerging as solid-state polymer electrolytes for next-generation lithium batteries. Using additive manufacturing such as digital light processing (DLP), PEG networks can be transformed into micro-architected metals and biomimetic vascular networks. However, developing stretchable and 3D printable PEG networks remains a fundamental challenge. Here, we report 3D printable, highly stretchable foldable bottlebrush PEG networks formed by rapid photopolymerization of low-cost commercial chemicals in air. The bottlebrush architecture enables high molecular weight PEG network strands that do not crystallize and remain elastic without solvents. Upon large deformations, the folded bottlebrush backbone releases stored lengths to enable extreme stretchability. We create hydrogels and elastomers with tissue-mimicking moduli ranging from ~1 to ~100 kPa and tensile breaking strains up to 1500%. We demonstrate the applications of bottlebrush PEG networks as matrices for highly stretchable and conductive solvent-free polymer electrolytes at room temperature (~900% strain and 1.2 mS/cm), as well as resins for DLP printing of complex architectures, cytocompatible organ-like geometries, functional devices, and multi-material structures with seamless interface integration. The developed photocurable bottlebrush PEG networks promise immediate applications in advanced (bio)manufacturing and beyond.

*This work is supported by the National Science Foundation (DMR-1944625, DMR-2512794), the National Institute of Health (1R35GM154912), UVA LaunchPad for Diabetes, and Virginia Innovation Partnership Corporation’s Commonwealth Commercialization Fund (CCF24-0268-HE).

Publication: Additive manufacturing of molecular architecture encoded stretchable polyethylene glycol hydrogels and elastomers

Presenters

  • Baiqiang Huang

    • University of Virginia

Authors

  • Baiqiang Huang

    • University of Virginia
  • Myoeum Kim

    • University of Virginia
  • Pu Zhang

    • University of Virginia
  • Emmanuel Oduro

    • University of Virginia
  • Daniel A Rau

    • University of Virginia
  • Li-Heng Cai

    • University of Virginia