Hydrogel-Based Network Metamaterials with Biological Tissue-like Poisson's Ratio Behavior and Stress Response

Area of Science:

• Materials Science
• Mechanical Engineering
• Biomedical Engineering

Background:

• Soft network metamaterials offer desirable properties like high stretchability and breathability for flexible electronics, tissue engineering, and biomedicine.
• Predicting and customizing the nonlinear mechanical behavior of these materials presents significant challenges.

Purpose of the Study:

• To develop hydrogel-based network metamaterials with biological tissue-like mechanical properties.
• To establish a machine learning-driven optimization design method for these metamaterials.
• To investigate the relationship between microstructural features, stretching ratios, and mechanical properties.

Main Methods:

• Utilized a machine learning-driven optimization design method.
• Developed hydrogel-based network metamaterials.
• Conducted numerical and experimental analyses to correlate microstructural features and stretching ratios with mechanical properties.

Main Results:

• Achieved hydrogel-based network metamaterials exhibiting J-shaped stress-deformation behavior, mimicking biological tissues.
• Demonstrated a transition in deformation mode from bending-dominated to stretching-dominated with increasing stretching ratio.
• Enabled wide-range tunability of Poisson's ratio (-1.06 to 1.34) and customization of zero Poisson's ratio behavior.

Conclusions:

• The developed machine learning approach effectively predicts and designs nonlinear mechanical behavior in soft network metamaterials.
• Hydrogel-based network metamaterials can be customized for tissue-like properties, enabling applications in artificial skin and integrated devices.
• This research provides a framework for fabricating soft network metamaterials with tailored mechanical responses.