Bayesian Inference of Nanoparticle-Broadened X-Ray Line Profiles

Published

February 1, 2004

Author(s)

N G. Armstrong, W Kalceff, James Cline, John E. Bonevich

Abstract

A single and self-contained method for determining the crystallite-size distribution and shape from experimental line profile data is presented. We have shown that the crystallite-size distribution can be determined without assuming 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 CeO(2) data. The results demonstrate that the proposed method can determine size distributions; while making the least number of assumptions. The comparison of the Bayesian/MaxEnt results from experimental CeO(2) with TEM results is favorable.

Citation

Journal of Research (NIST JRES) -

Volume

109 No. 1

NIST Pub Series

Journal of Research (NIST JRES)

Pub Type

NIST Pubs

Keywords

Bayesian, fuzzy pixel, instrument broadening, inverse problem, maximum entropy, morphology, nanoparticles, size distribution, x-ray line profiles

Citation

Armstrong, N. , Kalceff, W. , Cline, J. and Bonevich, J. (2004), Bayesian Inference of Nanoparticle-Broadened X-Ray Line Profiles, Journal of Research (NIST JRES), National Institute of Standards and Technology, Gaithersburg, MD (Accessed October 15, 2025)