Microstructure-property correlation for the tunneling-percolation conduction in metal-insulator nanocomposites

Liu-Juan Zhu; Wen-Zhong Cai; Shan-Tung Tu

doi:10.1117/12.843205

20 October 2009 Microstructure-property correlation for the tunneling-percolation conduction in metal-insulator nanocomposites

Liu-Juan Zhu, Wen-Zhong Cai, Shan-Tung Tu

Author Affiliations +

Proceedings Volume 7493, Second International Conference on Smart Materials and Nanotechnology in Engineering; 74936B (2009) https://doi.org/10.1117/12.843205
Event: Second International Conference on Smart Materials and Nanotechnology in Engineering, 2009, Weihai, China

Abstract

A theoretical model is proposed for the tunneling-percolation (TP) conduction in the metal-insulator nanocomposites based on the equivalent-particle concept. To establish a clear microstructure-property correlation, many-particle statistics is adopted firstly for the microstructure characterization in this TP model, and then incorporated hierarchically into effective-medium theory and classical percolation (CP) theory for the local and global TP conduction, respectively. The availability of this TP model is confirmed by experimental data. Results also show that the conventional CP model, regardless of universal or nonuniversal exponents, fails to account for the whole transition process from the electrically tunneling conduction to geometrically percolating conduction in the nanocomposites. Furthermore, the effect of metal particle size on the tunneling conduction thresholds is investigated with an experimental verification. The dominant role of interparticle tunneling conductance on the nanocomposite conductivity is explored, which exactly clarifies the main cause to failure of CP model--significant nonlinearity of percolation exponent.

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Liu-Juan Zhu, Wen-Zhong Cai, and Shan-Tung Tu "Microstructure-property correlation for the tunneling-percolation conduction in metal-insulator nanocomposites", Proc. SPIE 7493, Second International Conference on Smart Materials and Nanotechnology in Engineering, 74936B (20 October 2009); https://doi.org/10.1117/12.843205

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KEYWORDS

Nanocomposites

Particles

Data modeling

Metals

Nanoparticles

Composites

Statistical modeling

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