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<p>This study proposes a multi-phase synergistic conductive network design strategy, innovatively utilizing industrial solid waste iron tailings sand (ITs) as a low-cost, eco-friendly conductive phase alongside carbon fibers (CFs) within an alkali-activated geopolymer matrix. This approach develops geopolymer mortar (TCAGM) with integrated superior mechanical properties and self-sensing functionality. Through Response Surface Methodology-Box-Behnken Design (RSM-BBD), the alkaline activator modulus (A), sol–gel ratio (B), and CF volume fraction (C) were optimized, overcoming the performance-cost-sustainability trade-off inherent in conventional self-sensing materials. The optimal mix proportion (A = 1.42, B = 0.82, C = 0.4%) achieves high electrical conductivity (1.98 × 10<sup>−2</sup>(Ω ·cm)<sup>−1</sup>, stable without degradation) and piezoresistive performance (− 0.0157&#xa0;MPa<sup>−1</sup>, fluctuation within ± 5%). The multi-scale conductive network (long-range CF pathways + short-range ITs electron hopping + ionic transport) not only reduces CF dosage by 20–60% and raw material costs by 20% through ITs substitution but also enhances electromechanical performance. This work establishes a sustainable paradigm for high-performance, low-environmental-impact intelligent construction materials.</p>

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Iron tailings-carbon fiber synergy in geopolymer composites: multi-objective optimization of self-sensing mortar

  • 期刊论文

  • 发表日期：

    2025年10月10日

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  • Ning Zhang,
  • Weikun Zhai,
  • Zexuan Cheng,
  • Yue Geng,
  • Yongqiang Li,
  • Weijun Mi,
  • Shiyang Yin

摘要

This study proposes a multi-phase synergistic conductive network design strategy, innovatively utilizing industrial solid waste iron tailings sand (ITs) as a low-cost, eco-friendly conductive phase alongside carbon fibers (CFs) within an alkali-activated geopolymer matrix. This approach develops geopolymer mortar (TCAGM) with integrated superior mechanical properties and self-sensing functionality. Through Response Surface Methodology-Box-Behnken Design (RSM-BBD), the alkaline activator modulus (A), sol–gel ratio (B), and CF volume fraction (C) were optimized, overcoming the performance-cost-sustainability trade-off inherent in conventional self-sensing materials. The optimal mix proportion (A = 1.42, B = 0.82, C = 0.4%) achieves high electrical conductivity (1.98 × 10−2(Ω ·cm)−1, stable without degradation) and piezoresistive performance (− 0.0157 MPa−1, fluctuation within ± 5%). The multi-scale conductive network (long-range CF pathways + short-range ITs electron hopping + ionic transport) not only reduces CF dosage by 20–60% and raw material costs by 20% through ITs substitution but also enhances electromechanical performance. This work establishes a sustainable paradigm for high-performance, low-environmental-impact intelligent construction materials.