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Integrating simulations and experiments to predict sheet resistance and optical transmittance in nanowire films for transparent conductors.

Metal nanowire films are among the most promising alternatives for next-generation flexible, solution-processed transparent conductors. Breakthroughs in nanowire synthesis and processing have reported low sheet resistance (Rs ≤ 100 Ω/sq) and high optical transparency (%T > 90%). Comparing the merits of the various nanowires and fabrication methods is inexact, because Rs and %T depend on a variety of independent parameters including nanowire length, nanowire diameter, areal density of the nanowires and contact resistance between nanowires. In an effort to account for these fundamental parameters of nanowire thin films, this paper integrates simulations and experimental results to build a quantitatively predictive model. First, by fitting the results from simulations of quasi-2D rod networks to experimental data from well-defined nanowire films, we obtain an effective average contact resistance, which is indicative of the nanowire chemistry and processing methods. Second, this effective contact resistance is used to simulate how the sheet resistance depends on the aspect ratio (L/D) and areal density of monodisperse rods, as well as the effect of mixtures of short and long nanowires on the sheet resistance. Third, by combining our simulations of sheet resistance and an empirical diameter-dependent expression for the optical transmittance, we produced a fully calculated plot of optical transmittance versus sheet resistance. Our predictions for silver nanowires are validated by experimental results for silver nanowire films, where nanowires of L/D > 400 are required for high performance transparent conductors. In contrast to a widely used approach that employs a single percolative figure of merit, our method integrates simulation and experimental results to enable researchers to independently explore the importance of contact resistance between nanowires, as well as nanowire area fraction and arbitrary distributions in nanowire sizes. To become competitive, metal nanowire systems require a predictive tool to accelerate their design and adoption for specific applications.