Analysis and 3D modelling of percolated conductive networks in nanoparticle-based thin films

IF 7.5 Q1 CHEMISTRY, PHYSICAL Applied Surface Science Advances Pub Date : 2025-01-01 DOI:10.1016/j.apsadv.2024.100689
Stanislav Haviar , Benedikt Prifling , Tomáš Kozák , Kalyani Shaji , Tereza Košutová , Šimon Kos , Volker Schmidt , Jiří Čapek
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Abstract

A methodology to model the percolated conductive network in nanoparticle-based thin films, synthesized by means of a magnetron-based gas aggregation source, was developed and validated. Two differently sized copper oxide nanoparticles were produced by varying the diameter of the exit orifice. Comprehensive characterization of these films was performed using scanning electron microscopy, transmission electron microscopy, small-angle X-ray scattering and X-ray diffraction to determine particle morphology, size distribution, porosity, vertical density profiles, and phase composition. Using the experimental data, virtual films were generated through a data-driven stochastic 3D microstructure model that is based on a sphere packing algorithm, where the particle size distribution, porosity and vertical density profile are taken into account. The generated 3D structures have been then refined to cover the effect of oxidation of as-deposited nanoparticles and non-zero roughness of real films. A computational model incorporating a simplified adsorption model was developed to simulate the effects of oxygen adsorption on the surface conductivity of the nanoparticles. Then, the electrical conductivity of the percolated networks in these virtual structures was computed using the finite element method for various partial oxygen pressures. Simulated resistivity values were compared with experimental measurements obtained from four-point probe resistivity measurements conducted under varying oxygen partial pressures at 150 °C A discussion of the validity of the model and its ability to cover qualitatively and quantitatively the observed behaviour is included.
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CiteScore
8.10
自引率
1.60%
发文量
128
审稿时长
66 days
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