Improvements and considerations for size distribution retrieval from small-angle scattering data by Monte-Carlo methods

TL;DR

This paper discusses improvements to Monte-Carlo methods for retrieving particle size distributions from small-angle scattering data, addressing convergence issues and uncertainty estimation.

Contribution

It introduces enhanced MC techniques with a new convergence criterion and uncertainty assessment for size distribution retrieval.

Findings

Improved convergence criteria for MC methods.

Enhanced uncertainty quantification in size distribution results.

Discussion on size sensitivity related to scattering vector range.

Abstract

Monte-Carlo (MC) methods, based on random updates and the trial-and-error principle, are well suited to retrieve particle size distributions from small-angle scattering patterns of dilute solutions of scatterers. The size sensitivity of size determination methods in relation to the range of scattering vectors covered by the data is discussed. Improvements are presented to existing MC methods in which the particle shape is assumed to be known. A discussion of the problems with the ambiguous convergence criteria of the MC methods are given and a convergence criterion is proposed, which also allows the determination of uncertainties on the determined size distributions.