title: Bayesian Inference of Nanoparticle-Broadened X-Ray Line Profiles.
abstract: A single-step, self-contained method for determining the crystallite-size distribution and shape from experimental x-ray line profile data is presented. It is shown that the crystallite-size distribution can be determined without invoking a functional form for the size distribution, determining instead the size distribution with the least assumptions by applying the Bayesian/MaxEnt method. The Bayesian/MaxEnt method is tested using both simulated and experimental CeO2 data, the results comparing favourably with experimental CeO2 data from TEM measurements.
authors.affiliation: University of Technology Sydney, PO Box 123, Broadway NSW 2007, Australia.
authors.forename: Nicholas
authors.initials: N
authors.lastname: Armstrong
authors.affiliation: University of Technology Sydney, PO Box 123, Broadway NSW 2007, Australia.
authors.forename: Walter
authors.initials: W
authors.lastname: Kalceff
authors.affiliation: National Institute of Standards and Technology, Gaithersburg, MD 20899, USA.
authors.forename: James P
authors.initials: JP
authors.lastname: Cline
authors.affiliation: National Institute of Standards and Technology, Gaithersburg, MD 20899, USA.
authors.forename: John E
authors.initials: JE
authors.lastname: Bonevich
keywords: Bayesian; fuzzy pixel; instrumental broadening; inverse problem; maximum entropy; morphology; nanoparticles; size broadening; size distribution; x-ray line profiles