Login / Signup

Stochastic models of dense or hollow nanoparticles and their scattering properties.

Cedric J GommesRaphael ChattotJakub Drnec
Published in: Journal of applied crystallography (2020)
A family of stochastic models of disordered particles is proposed, obtained by clipping a Gaussian random field with a function that is space dependent. Depending on the shape of the clipping function, dense or hollow particles can be modelled. General expressions are derived for the form factor of the particles, for their average volume and surface area, and for their density and surface-area distributions against the distance to the particle centre. A general approximation for the form factor is also introduced, based on the density and surface-area distributions, which coincides with the Guinier and Porod expressions in the limits of low and high scattering vector magnitude q. The models are illustrated with the fitting of small-angle X-ray scattering (SAXS) data measured on Pt/Ni hollow nanoparticles. The SAXS analysis and modelling notably capture the collapse of the particles' porosity after being used as oxygen-reduction catalysts.
Keyphrases
  • metal organic framework
  • monte carlo
  • high resolution
  • molecularly imprinted
  • highly efficient
  • electronic health record
  • magnetic resonance imaging
  • big data
  • computed tomography
  • machine learning
  • electron microscopy