Speaker
Description
Graphene-based nano-laminates (GNL) consist of disordered stacked sheets of pure graphene, which can form macroscopic films or fibers. GNLs are flexible, strong, and lightweight. They have a very high potential as electrical and thermal conductors [1].
We show different modeling approaches for GNLs, which relate the structural arrangement and the properties of the individual graphene flakes to the macroscopically observed electrical conductivity. Based on these structure-property relationships we can predict the potential of the material for electrical conductor applications and provide guidelines for optimized processing towards high-performance conductors.
Our approach relies on conductor network models and statistical sampling based on a high number of representative arrangements. The results of our simulations show clearly, that the in-flake as well as the inter-flake conductivity in relation to the flake size are the key factors to reach a highly conductive macroscopic material [2]. Only for GNLs consisting of large flakes of several tens of microns, we can transfer the excellent conductivity of the single graphene flakes to the macroscopic conductivity. The observed trends are in excellent agreement with experimental tests on systematically prepared GNL-films [3].
As a layered material GNLs show a strong anisotropy of the electrical transport along and across the plane of the aligned flakes. Based on our network model we can relate the microscopic conductivities within or between overlapping flakes to the anisotropic macroscopically observed conductivity and investigate how it depends on structural properties like the flake size.
Finally, to obtain quick-response digital material models for GNLs we demonstrate implementations of data-based models which were trained by machine-learning methods on data generated from our network models.
References:
[1] Cesano et al, Front. Mat. 7, 2020, 219
[2] Rizzi et al, ACS Appl. Mater. Interfaces 10, 2018, 43088
[3] Rizzi et al., Nano Express 1, 2020, 020035
| Speaker Country | Germany |
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