Networks of metallic nanowires have the potential to meet the needs of next-generation device technologies that require flexible transparent conductors. At present, there does not exist a first principles model capable of predicting the electro-optical performance of a nanowire network. Here we combine an electrical model derived from fundamental material properties and electrical equations with an optical model based on Mie theory scattering of light by small particles. This approach enables the generation of analogues for any nanowire network and then accurately predicts, without the use of fitting factors, the optical transmittance and sheet resistance of the transparent electrode. Predictions are validated using experimental data from the literature of networks comprised of a wide range of aspect ratios (nanowire length/diameter). The separation of the contributions of the material resistance and the junction resistance allows the effectiveness of post-deposition processing methods to be evaluated and provides a benchmark for the minimum attainable sheet resistance. The predictive power of this model enables a material-by-design approach, whereby suitable systems can be prescribed for targeted technology applications.

Flexible transparent conductors made from networks of metallic nanowires are a potential replacement for conventional, non-flexible, transparent conducting materials such as indium tin oxide. Cu nanowires are particularly interesting as a cost-effective alternative to Ag nanowiresthe most investigated metallic nanowire to date. To optimize the conductivity of Cu nanowire networks, the resistance contributions from the material and nanowire junctions must be independently known. In this paper, we report the resistivity values (ρ) of individual solution grown Cu nanowires ⟨ρ⟩ = 20.1 ± 1.3 nΩ•m and the junction resistance (Rjxn) between two overlapping Cu nanowires ⟨Rjxn⟩ = 205.7 ± 57.7 Ω. This electrical data is incorporated into an electro-optical model which generates analogs for Cu nanowire networks, that accurately predict without the use of fitting factors the optical transmittance and sheet resistance of the transparent electrode. The model's predictions are validated using experimental data from the literature of Cu nanowire networks comprised of a wide range of aspect ratios (nanowire length/diameter). The separation of the material resistance and the junction resistance allows the effectiveness of postdeposition processing methods to be evaluated, aiding research and industry groups in adopting a materials-by-design approach.