Energy‐Dissipative Matrices Enable Synergistic Toughening in Fiber Reinforced Soft Composites

By Yiwan Huang, Daniel R. King, T. Sun, Takayuki Nonoyama, T. Kurokawa, Tasuku Nakajima, J. Gong. 2017

153 citations recorded by the source corpus.

Tough hydrogels have shown strong potential as structural biomaterials. These hydrogels alone, however, possess limited mechanical properties (such as low modulus) when compared to some load‐bearing tissues, e.g., ligaments and tendons. Developing both strong and tough soft materials is still a challenge. To overcome this obstacle, a new material design strategy has been recently introduced by combining tough hydrogels with woven fiber fabric to create fiber reinforced soft composites (FRSCs). The new FRSCs exhibit extremely high toughness and tensile properties, far superior to those of the neat components, indicating a synergistic effect. Here, focus is on understanding the role of energy dissipation of the soft matrix in the synergistic toughening of FRSCs. By selecting a range of soft matrix materials, from tough hydrogels to weak hydrogels and even a commercially available elastomer, the toughness of the matrix is determined to play a critical role in achieving extremely tough FRSCs. This work provides a good guide toward the universal design of soft composites with extraordinary fracture resistance capacity.

Subfields: Biomaterials, Mechanical Engineering, Molecular Medicine

View Paper: https://www.semanticscholar.org/paper/67965a2c2cf8fb316aa7019d987fa93315127bdf

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomaterials

  1. Jingda Tang
  2. Jian Hu
  3. Zhigang Suo

In Mechanical Engineering

  1. Tongqing Lu
  2. Jingda Tang
  3. Jian Hu
  4. Zhigang Suo
  5. T. Y. Ng
  6. Zishun Liu
  7. W. Toh

In Molecular Medicine

  1. Jingda Tang
  2. Zhigang Suo
  3. Jikun Wang
  4. Xiao Liu
  5. Jian Hu
  6. Tongqing Lu
  7. Wenlei Zhang
  8. Zishun Liu

Citations in the corpus