Impact resistance and fire resistance of solid waste based interface self-assembled fiber reinforced composite structures

•Industrial sludge resource utilization provides silicon source for pore structure preparation.•Metal ion bridging grafting reaction of fiber interface composite structure.•Flame retardant, heat-insulating, and bulletproof multi load protection mechanism.•Optimization of coupled protection threshold...

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Veröffentlicht in:Thin-walled structures 2025-04, Vol.209, p.112870, Article 112870
Hauptverfasser: Yan, Ke, Qi, Shaobo, Shen, Xingyu, Yuan, Mengqi, Wu, Hao, Yang, Yunxian, Qian, Yazhuo
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Sprache:eng
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Zusammenfassung:•Industrial sludge resource utilization provides silicon source for pore structure preparation.•Metal ion bridging grafting reaction of fiber interface composite structure.•Flame retardant, heat-insulating, and bulletproof multi load protection mechanism.•Optimization of coupled protection threshold based on economic and other predictive factors. Inspired by the resource utilization of solid waste, industrial sludge has been developed as a raw material NH2-MCM-41@Cu-AF. The preparation method of fiber self-assembly structure design was explored by combining macroscopic grafting behavior with the evolution of microscopic bridging structure. The basic mechanical properties, impact resistance, flame retardant, and thermal insulation properties were analyzed. By exploring and predicting the transition from single fiber composite materials to multi-protection, experimental results have shown that NH2-MCM-41@Cu-AF significantly enhances the protective performance. The composite fabric has increased the pulling force by 3.25 times, and the ballistic limit speed has been increased from 57 m/s to 94 m/s. The strong penetration resistance of composite materials has been demonstrated through multi-layer bulletproof performance. Composite fabrics have stronger flame retardancy and thermal insulation, forming a dense protective layer under high temperatures, ensuring structural integrity to the greatest extent possible. Machine learning prediction methods and threshold analysis processes have been established for the final implementation of NH2-MCM-41@Cu-AF. The development of new protective materials and their application in engineering practice provide theoretical support and an experimental basis. [Display omitted]
ISSN:0263-8231
DOI:10.1016/j.tws.2024.112870