Analysis and 3D Modelling of Percolated Conductive Networks in Nanoparticle-based Thin Films

Stanislav Haviar & Tomáš Kozák
Department of Physics and NTIS - European Centre of Excellence,
University of West Bohemia in Pilsen, Czechia

In this lecture, we present a series of studies focused on nanoparticles synthesized using a magnetron gas aggregation (MGA) source [4]. Such nanoparticle-assembled thin films have potential for various applications, especially where their high specific surface area is crucial. In our work, we focus on their use as conductometric gas sensors [2,5]. However, the experimental fabrication of such layers remains highly demanding [3], making it advantageous to complement measurements with modelling approaches.

In one of our recent studies [1], thin films composed of copper oxide nanoparticles were synthesized, with nanoparticle size controlled by varying the exit orifice diameter [4]. Comprehensive characterization by SEM, TEM, SAXS, and XRD provided details on particle morphology, size distribution, and porosity. The obtained experimental data served as input for generating virtual 3D microstructure models using a data-driven stochastic hard-sphere packing algorithm that incorporated parameters such as size distribution, porosity, and vertical density profiles. A computational model of current flow through the conductive network was developed, enabling the modelling of large networks of core-shell-type nanoparticles. A simplified adsorption model is included to describe the effects of oxygen adsorption on surface conductivity. The model was used to calculate electrical resistivity of virtual 3D microstructures under various oxygen partial pressures, which were validated against experimental four-point probe measurements at 150 °C, showing both qualitative and quantitative agreement.

To our knowledge, we were the first to attempt a 3D reconstruction of MGA-generated nanoparticles. We believe that a deeper understanding of the percolation behaviour of nanoparticle-based layers will aid in designing films with optimized composition, structure, and heterojunction density, thereby explaining the exceptional performance observed experimentally.

References:

[1] Haviar, S.; Prifling, B.; Kozák, T.; et al. Appl. Surf. Sci. Adv. 2025, 25.
[2] Shaji, K.; Haviar, S.; Zeman, P.; et al. Surf. Coatings Technol. 2024, 477.
[3] Shaji, K.; Haviar, S.; Zeman, P.; et al. Surf. Coatings Technol. 2025, 503.
[4] Batková, Š.; Kozák, T.; Haviar, S.; et al. Surf. Coatings Technol. 2021, 417.
[5] Haviar, S.; Čapek, J.; Batková, Š.; et al. Int. J. Hydrogen Energy 2018, 43.

CRC 1461: Neurotronics
Colloquium: 27-November-2025_39, Thursday, 02:00 pm to 03:00 pm (CET)

Link to the zoom meeting

Invited by Jan Trieschmann
Kiel University, Faculty of Engineering, Department of Electrical and Information Engineering

Download the announcement here.