Manning Hugh G, da Rocha Claudia Gomes, Callaghan Colin O', Ferreira Mauro S, Boland John J
School of Chemistry, Trinity College Dublin, Dublin 2, Ireland.
Centre for Research on Adaptive Nanostructures and Nanodevices (CRANN) & Advanced Materials and Bioengineering Research (AMBER) Centre, Trinity College Dublin, Dublin 2, Ireland.
Sci Rep. 2019 Aug 9;9(1):11550. doi: 10.1038/s41598-019-47777-2.
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.
金属纳米线网络有潜力满足下一代需要柔性透明导体的器件技术的需求。目前,不存在能够预测纳米线网络电光性能的第一性原理模型。在此,我们将基于基本材料特性和电学方程推导的电学模型与基于小颗粒对光的米氏理论散射的光学模型相结合。这种方法能够生成任何纳米线网络的模拟物,然后在不使用拟合因子的情况下准确预测透明电极的光学透过率和薄层电阻。使用来自文献的由各种纵横比(纳米线长度/直径)组成的网络的实验数据对预测进行了验证。材料电阻和结电阻贡献的分离使得能够评估沉积后处理方法的有效性,并为可达到的最小薄层电阻提供了一个基准。该模型的预测能力实现了一种按设计选材的方法,据此可为目标技术应用指定合适的系统。