Effective Thermal Conductivity of Hybrid AlN/Graphene Polymer Matrix Composites: A Hierarchical Micromechanical Model
- 1Advanced Manufacturing Institute, King Saud University, Riyadh 11421, Saudi Arabia
- 2Chemical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
Abstract
The demand for lightweight polymer composites with enhanced thermal conductivity is increasing in advanced thermal-management applications. This study develops a hierarchical micromechanical model to predict the effective thermal conductivity of hybrid polymer composites reinforced with aluminum nitride (AlN) particles and graphene nanoplatelets (GNPs). The modeling procedure is performed in two successive stages. First, the effective thermal conductivity of the GNP/polymer matrix is determined by considering the graphene volume fraction, length, thickness, and flatness ratio as well as the interfacial thermal resistance. The resulting graphene/polymer phase is then treated as an effective matrix for AlN micro-reinforcement. An analytical micromechanics formulation on the basis of the representative volume element (RVE) is used to determine the thermal conductivity of the final hybrid composite. The results show that adding graphene considerably enhances the thermal conductivity of the AlN/polymer composite. Larger graphene length also improves the heat-transfer characteristics of the hybrid polymer composite. In contrast, increasing graphene thickness reduces the thermal conductivity enhancement. It is found that the AlN content and aspect ratio strongly affect the AlN/GNP/polymer composite thermal conductivity. The proposed model provides useful guidance for designing thermally conductive polymer composite structures.
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BibTeX
@article{umer2026effective,
title = {{Effective Thermal Conductivity of Hybrid AlN/Graphene Polymer Matrix Composites: A Hierarchical Micromechanical Model}},
author = {Usama Umer and Mustufa Haider Abidi and Syed Hammad Mian and Khaja Moiduddin and Abdulrahman Bin Jumah},
journal = {Polymers},
year = {2026},
month = nov,
doi = {10.3390/polym18212716},
url = {https://www.mdpi.com/2073-4360/18/21/2716},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Effective Thermal Conductivity of Hybrid AlN/Graphene Polymer Matrix Composites: A Hierarchical Micromechanical Model
AU - Usama Umer
AU - Mustufa Haider Abidi
AU - Syed Hammad Mian
AU - Khaja Moiduddin
AU - Abdulrahman Bin Jumah
PY - 2026
DA - 2026/11/02
JO - Polymers
DO - 10.3390/polym18212716
UR - https://www.mdpi.com/2073-4360/18/21/2716
AB - The demand for lightweight polymer composites with enhanced thermal conductivity is increasing in advanced thermal-management applications. This study develops a hierarchical micromechanical model to predict the effective thermal conductivity of hybrid polymer composites reinforced with aluminum nitride (AlN) particles and graphene nanoplatelets (GNPs). The modeling procedure is performed in two successive stages. First, the effective thermal conductivity of the GNP/polymer matrix is determined by considering the graphene volume fraction, length, thickness, and flatness ratio as well as the interfacial thermal resistance. The resulting graphene/polymer phase is then treated as an effective matrix for AlN micro-reinforcement. An analytical micromechanics formulation on the basis of the representative volume element (RVE) is used to determine the thermal conductivity of the final hybrid composite. The results show that adding graphene considerably enhances the thermal conductivity of the AlN/polymer composite. Larger graphene length also improves the heat-transfer characteristics of the hybrid polymer composite. In contrast, increasing graphene thickness reduces the thermal conductivity enhancement. It is found that the AlN content and aspect ratio strongly affect the AlN/GNP/polymer composite thermal conductivity. The proposed model provides useful guidance for designing thermally conductive polymer composite structures.
SN - 2073-4360
ER -