REPOSITORY > RESULTS

Abstract

The fabrication of functional materials composed of metallic nanoparticles is one
of the most important steps in the contemporary nanotechnology. Nonlinear selfassembly
of nanoparticles into macroscopic aggregates are promising methods in
which various structures can be obtained by tuning certain control parameters. Such
large-scale aggregates exhibit new physical features, which are often induced by the
structural complexity of the system.
In this thesis, we study the self-assembly processes of metallic nanoparticles on substrates
with numerical methods. The aggregates or nanoparticle films are further
investigated by examining their structure and their conduction via quantum tunnelling
under applied voltage. Our approach is based on mapping the nanoparticle
films onto planar graphs, or nano-networks, which enable quantitative analysis of the
structure and dynamical processes using the methods of graph theory and statistical
physics of complex networks.
Three models of the nanoparticle aggregates are introduced and studied: (a) networks
based on fixed positions of the nanoparticles on substrate from empirical data;
(b) nearly-equilibrium assemblies emerging in bio-molecular recognition binding; and
(c) cellular networks grown by cell-aggregation rules. Charge transport through such
nano-neworks is simulated, introducing a generalized model of single-electron conduction
through capacitively coupled nanoparticle arrays. That nontrivial topology can
lead to new conduction properties is shown. Specifically, non-linear current-voltage
characteristics and non-Gaussian fluctuations at the electrode can arise due to the
coalescence of conduction pathways through the sample, and long-range temporal
correlations of charge fluctuations along the conduction pathways.