Modelling self-assembling of colloid particles in multilayered structures

Abstract

Simulations of particle multilayer build-up in the layer by layer (LbL) self-assembling processes have been performed according to the generalized random sequential adsorption (RSA) scheme. The first (precursor) layer having an arbitrary coverage of adsorption centers was generated using the standard RSA scheme pertinent to homogeneous surface. Formation of the consecutive layers (up to 20) was simulated by assuming short-range interaction potentials for two kinds of particles of equal size. Interaction of two particles of different kind resulted in irreversible and localized adsorption upon their contact, whereas particles of the same kind were assumed to interact via the hard potential (no adsorption possible). Using this algorithm theoretical simulations were performed aimed at determining the particle volume fraction as a function of the distance from the interface, as well as the multilayer film roughness and thickness as a function of the number of layers. The simulations revealed that particle concentration distribution in the film was more uniform for low precursor layer density than for higher density, where well-defined layers of closely packed particles appeared. On the other hand, the roughness of the film was the lowest at the highest precursor layer density. It was also predicted theoretically that for low precursor layer density the film thickness increased with the number of layers in a non-linear way. However, for high precursor layer density, the film thickness increased linearly with the number of layers and the average layer thickness was equal to 1.58 of the particle radius, which is close to the closely packed hexagonal layer thickness equal to 1.73. It was concluded by analysing the existing data for colloid particles and polyelectrolytes that the theoretical results can be effectively exploited for interpretation of the LbL processes involving colloid particles and molecular species like polymers or proteins.