Aggregate Formation of Surface-Modified Nanoparticles in Solvents and Polymer Nanocomposites

Abstract

A new method based on the combination of small-angle scattering, reverse Monte Carlo simulations, and an aggregate recognition algorithm is proposed to characterize the structure of nanoparticle suspensions in solvents and polymer nanocomposites, allowing detailed studies of the impact of different nanoparticle surface modifications. Experimental small-angle scattering is reproduced using simulated annealing of configurations of polydisperse particles in a simulation box compatible with the lowest experimental q-vector. Then, properties of interest like aggregation states are extracted from these configurations and averaged. This approach has been applied to silane surface-modified silica nanoparticles with different grafting groups, in solvents and after casting into polymer matrices. It is shown that the chemistry of the silane function, in particular mono- or trifunctionality possibly related to patch formation, affects the dispersion state in a given medium, in spite of an unchanged alkyl-chain length. Our approach may be applied to study any dispersion or aggregation state of nanoparticles. Concerning nanocomposites, the method has potential impact on the design of new formulations allowing controlled tuning of nanoparticle dispersion.

Structural identification of percolation of nanoparticles

“…The polymer was dissolved in MEK (10%v), then mixed with the NP suspension in MEK at 1%v, followed by solvent casting on a Teflon support for 24 h at 50°C. 52 The final PNC samples have a typical size of 3 cm diameter with a thickness of ca. 100 µm.…”

“…14, 53-55 simulation, and converted into distribution functions of aggregate mass by an aggregate recognition algorithm. 52 Details and fine-tuning of the latter are given in the results section. The cubic box size with periodic boundary conditions was set by the experimental minimum q-value, L box = 2π/q min , which in turn determines the number of particles given the experimental silica volume fraction, Φ.…”

“…11 The natural base unit of the calculation is thus a sphere drawn from the experimental size distribution, which allows the construction of any types of aggregate or dispersion. The initial configuration has been defined with a pre-aggregation parameter α = 5% (see 52 for details). This parameter has been introduced in order to accelerate the convergence of the algorithm in presence of aggregation, but does not predefine the final state.…”

“…In PNCs made by solvent casting using well-defined colloidal silica grafted with silane-molecules, the dispersion in a SB matrix was recently studied at low concentration using a combination of small-angle X-ray scattering (SAXS) and reverse Monte Carlo (RMC). 52 In particular, the impact of functionality (mono-or tri-functionality) of silanes of identical hydrophobicity was explored. The effect of concentration on model bare nanoparticles incorporated into a SB matrix with a complete control of colloidal stability throughout the solventcasting protocol has not been addressed.…”

The Payne Effect: Primarily Polymer-Related or Filler-Related Phenomenon?

“…11,26 Alternatively, if sufficient knowledge is available on the primary particles, e.g., if it is possible to measure them under dilute conditions in order to describe shape, polydispersity, and surface roughness quantitatively, then reverse Monte Carlo modelling may be used for a quantitative analysis of the particle and aggregate structure. [27][28] This technique provides a series of real space snapshots with particle distributions, which are compatible with the observed scattered intensity. In the present contribution on more or less ill-defined carbon black particles, the above-mentioned multi-scale approach had to be used.…”