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 July 18, 2024)
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Bayesian Inference of Nanoparticle-Broadened X-Ray Line Profiles
Published
Author(s)
N G. Armstrong, W Kalceff, ,
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
Pub Type
NIST Pubs
Keywords
Bayesian, fuzzy pixel, instrument broadening, inverse problem, maximum entropy, morphology, nanoparticles, size distribution, x-ray line profiles
Citation
Issues
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Created January 31, 2004, Updated October 12, 2021