Graphene-based macromaterials (GBMs) transfer the excellent properties of graphene from the microscale to the macroscale. In contrast to graphite, a large portion of graphene flakes is decoupled from each other, thereby preserving the excellent properties of single-layer graphene. Thanks to high electrical and thermal conductivities, low specific weight, and suitable mechanical properties, GBM are interesting for a wide range of applications.

To understand the material, to perform parameter variations, and to give guidance to experimentalists and process engineers, computationally efficient models are essential. Within the project DiMoGraph, we develop material models for GBMs using a modeling workflow. A structural model is built to incorporate relevant structural parameters. Flake connections are then determined and the resulting network is solved to yield the (electronic) conductivity [1]. For example, the change of the total conductivity as a function of the interlayer conductivity can be studied. While the network model is fast enough to compute systems with several thousand flakes, it is still too slow to sample the entire parameter space in sufficient resolution or to predict material properties in real-time. Thus, simulation results from the network model are used to train more efficient data-based models. In addition, experiments are performed for validation and to supply further data, which are unavailable from the models. Graphene oxide flake dispersions of different size distributions are fabricated into a freestanding membrane and transformed to graphene by thermal treatment. The conductivity is measured via the eddy current method and compared to the simulations.

In our contribution, we will discuss the methodology in detail and present selected results. For example, we show the strong anisotropy of the electric conductivity in GBM [2] and reveal relevant trends of the conductivity as a function of material parameters.

References
[1] L. Rizzi et al., ACS Appl. Mater. Interfaces, 2018, 10, 43088.
[2] D. Slawig et al., Journal of Materials Science, 2021, 56, 14624.