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<p>Existing models of thermal conductivity in polymer composites typically address dilute to semi-dilute filler concentrations (≤ 40 vol%) yet fail to capture the strong interactions that arise when fillers are closely packed. In densely filled systems, these interactions reorganize heat transport according to the principles of thermal resistance, directing it along preferred pathways. In this study, we introduce a mechanistic thermal conductivity model based on a simplified body-centered cubic framework. This model effectively captures the essential impact of reduced interparticle distances and enhanced filler interactions in densely packed composites, enabling a more accurate description of heat flow by forcing it to follow the least-resistance pathways. This approach delivers high-precision predictions across a wide range of filler concentrations (0-68 vol%), spanning nearly the entire practical concentration range, and has been rigorously validated against diverse cross-material datasets. This model also can help to offer a physical interpretation of the thermal percolation transition, identifying both its underlying mechanism and the quantitative conditions for its onset. Its modular and computationally efficient design reduces reliance on extensive experimental testing, providing both a practical tool for optimizing composite design and a deeper quantitative understanding of thermal transport in composite materials.</p>

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Thermal conductivity modeling beyond the dilute limit using a body-centered cubic framework for densely packed polymer composites

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月6日

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  • Lu He,
  • Kai Wu,
  • Qiang Fu

摘要

Existing models of thermal conductivity in polymer composites typically address dilute to semi-dilute filler concentrations (≤ 40 vol%) yet fail to capture the strong interactions that arise when fillers are closely packed. In densely filled systems, these interactions reorganize heat transport according to the principles of thermal resistance, directing it along preferred pathways. In this study, we introduce a mechanistic thermal conductivity model based on a simplified body-centered cubic framework. This model effectively captures the essential impact of reduced interparticle distances and enhanced filler interactions in densely packed composites, enabling a more accurate description of heat flow by forcing it to follow the least-resistance pathways. This approach delivers high-precision predictions across a wide range of filler concentrations (0-68 vol%), spanning nearly the entire practical concentration range, and has been rigorously validated against diverse cross-material datasets. This model also can help to offer a physical interpretation of the thermal percolation transition, identifying both its underlying mechanism and the quantitative conditions for its onset. Its modular and computationally efficient design reduces reliance on extensive experimental testing, providing both a practical tool for optimizing composite design and a deeper quantitative understanding of thermal transport in composite materials.