Bayesian Inference of Nanoparticle-Broadened X-Ray Line Profiles

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 inst...

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Detalles Bibliográficos
Autores principales: Armstrong, Nicholas, Kalceff, Walter, Cline, James P., Bonevich, John E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: [Gaithersburg, MD] : U.S. Dept. of Commerce, National Institute of Standards and Technology 2004
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4849618/
https://www.ncbi.nlm.nih.gov/pubmed/27366604
http://dx.doi.org/10.6028/jres.109.012
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author Armstrong, Nicholas
Kalceff, Walter
Cline, James P.
Bonevich, John E.
author_facet Armstrong, Nicholas
Kalceff, Walter
Cline, James P.
Bonevich, John E.
author_sort Armstrong, Nicholas
collection PubMed
description 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 CeO(2) data, the results comparing favourably with experimental CeO(2) data from TEM measurements.
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spelling pubmed-48496182016-06-30 Bayesian Inference of Nanoparticle-Broadened X-Ray Line Profiles Armstrong, Nicholas Kalceff, Walter Cline, James P. Bonevich, John E. J Res Natl Inst Stand Technol Article 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 CeO(2) data, the results comparing favourably with experimental CeO(2) data from TEM measurements. [Gaithersburg, MD] : U.S. Dept. of Commerce, National Institute of Standards and Technology 2004 2004-02-01 /pmc/articles/PMC4849618/ /pubmed/27366604 http://dx.doi.org/10.6028/jres.109.012 Text en https://creativecommons.org/publicdomain/zero/1.0/ The Journal of Research of the National Institute of Standards and Technology is a publication of the U.S. Government. The papers are in the public domain and are not subject to copyright in the United States. Articles from J Res may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
spellingShingle Article
Armstrong, Nicholas
Kalceff, Walter
Cline, James P.
Bonevich, John E.
Bayesian Inference of Nanoparticle-Broadened X-Ray Line Profiles
title Bayesian Inference of Nanoparticle-Broadened X-Ray Line Profiles
title_full Bayesian Inference of Nanoparticle-Broadened X-Ray Line Profiles
title_fullStr Bayesian Inference of Nanoparticle-Broadened X-Ray Line Profiles
title_full_unstemmed Bayesian Inference of Nanoparticle-Broadened X-Ray Line Profiles
title_short Bayesian Inference of Nanoparticle-Broadened X-Ray Line Profiles
title_sort bayesian inference of nanoparticle-broadened x-ray line profiles
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4849618/
https://www.ncbi.nlm.nih.gov/pubmed/27366604
http://dx.doi.org/10.6028/jres.109.012
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