Dimension Effects of Graphene Sheets as Building Blocks: Implications for Thermal Conductivity Improvement of Graphene Films

Graphene films with excellent thermal conductivity have realized applications in many electronic devices. Improving the performance of graphene thermally conductive films (GFs) further is the key to meeting the thermal management needs of devices with higher power densities. However, factors determining the performance of GFs are numerous and even interrelated, making it difficult to experimentally investigate the structure-performance relationship and enhance the performance. Herein, we developed a multiscale strategy to predict the effective thermal conductivity (kappa(eff)) of GFs. Two interrelated dimension factors, namely lateral size and thickness of the graphene sheets composing the GFs, and their dispersity were taken into consideration by combining the molecular dynamics and finite element modeling. After experimental calibration, the multiscale model can accurately predict the kappa(eff) of thermally conductive films prepared by graphene sheets with specific dispersity in size and thickness. More importantly, the dimension effects revealed by the simulation data lead to several important conclusions that may guide the screening and compounding of graphene precursors to improve the thermal conductivity of graphene films. The proposed multiscale modeling provides an interpretation of the structure-performance relationship of GFs and represents an efficient strategy to guide the preparation of high-performance macroscopic structures assembled from graphene and other two-dimensional materials.

Automated summary

A multiscale strategy was developed to predict the effective thermal conductivity of graphene films, providing accurate predictions after experimental calibration. The dimension effects revealed by simulation data offer important conclusions for enhancing the thermal conductivity of graphene films.