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Muscle-Inspired Micro/Nanofiber Composites via Flow-Assigned Hierarchical Alignment.

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Researchers designed a plant-fiber material by arranging tiny wood-based fibers in an ordered pattern, inspired by human muscle structure. Lab testing showed this material was lightweight yet stronger than standard wood-fiber film, and it could be broken down in water for recycling or degraded in soil. The work offers a design approach for creating strong, lightweight materials from plant fibers.

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Plastic accumulation and microplastic exposure have increased the demand for renewable lightweight materials. Lignocellulosic fibers are promising alternatives for acoustic diaphragms, paper-based packaging, and automotive interiors. However, their practical application is limited by the inherent trade-off between low density and mechanical performance, with most high-strength composites relying on densification or additional reinforcing phases that compromise lightweight characteristics. Here, inspired by the multiscale alignment of human muscle, we develop an oriented lignocellulose micro/nanofiber composite (O-LMU) through a flow-field-assisted assembly strategy. This approach enables the construction of a highly ordered multiscale fiber architecture coupled with optimized nanoscale interfaces. Multiscale simulations reveal that optimized interfacial interactions and hierarchical fiber alignment promote hydrogen-bond network reconstruction, mitigate stress concentration, and facilitate efficient load transfer within the fiber network. Therefore, O-LMU achieves a low density of 0.71 g·cm-3, a specific strength of 205 kN·m·kg-1, a tensile stiffness of 2245 kN·m-1, and a toughness of 394 J·m-2. Notably, O-LMU exhibits a specific strength that is 2.4 times that of the lignocellulosic nanofiber film. The material can be recycled by aqueous disintegration and biodegrades under natural soil conditions. This work establishes a structure-driven strategy for designing lightweight, strong, and sustainable lignocellulosic materials beyond conventional densification-based reinforcement approaches.

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