Biomimetic Mechanical Robust Cement-Resin Composites with Machine Learning-Assisted Gradient Hierarchical Structures

Area of Science:

• Materials Science
• Biomimetics
• Composite Materials

Background:

• Hierarchically ordered biological materials inspire advanced composites with superior mechanical properties.
• Efficient topology optimization and large-scale manufacturing of such composites remain significant challenges.

Purpose of the Study:

• To develop a scalable bottom-up approach for fabricating a nacre-like cement-resin composite with a gradient brick-and-mortar (BM) structure.
• To utilize a machine learning-assisted method for optimizing the gradient structure.
• To evaluate the mechanical performance and strengthening/toughening mechanisms of the developed composite.

Main Methods:

• Fabrication of a novel nacre-like cement-resin composite using a scalable bottom-up approach.
• Implementation of a machine learning-assisted method for gradient structure optimization.
• Characterization of mechanical properties including flexural strength, toughness, and impact resistance.
• Analysis of strengthening and toughening mechanisms.

Main Results:

• The fabricated gradient composite exhibits an extraordinary combination of high flexural strength, toughness, and impact resistance.
• Toughness and impact resistance surpass cement counterparts by approximately 700 and 600 times, respectively.
• Performance exceeds natural rocks, fiber-reinforced cement-based materials, and some alloys.
• Strengthening and toughening attributed to regional-matrix densifying and crack-tip shielding effects.

Conclusions:

• The developed gradient composite offers a promising structural material for protective applications in harsh environments.
• The study presents a novel pathway for designing biomimetic metamaterials with enhanced mechanical properties.
• The findings highlight the potential of gradient structures inspired by nature for advanced material design.