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Determination of Particle Size Distributions of Bulk Samples Using Micro-Computed Tomography and Artificial Intelligence

The knowledge of product particle size distribution (PSD) in crystallization processes is of high interest for the pharmaceutical and fine chemical industries, as well as in research and development. Not only can the efficiency of crystallization/production processes and product quality be increased...

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Detalles Bibliográficos
Autores principales: Höving, Stefan, Neuendorf, Laura, Betting, Timo, Kockmann, Norbert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9920040/
https://www.ncbi.nlm.nih.gov/pubmed/36770009
http://dx.doi.org/10.3390/ma16031002
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author Höving, Stefan
Neuendorf, Laura
Betting, Timo
Kockmann, Norbert
author_facet Höving, Stefan
Neuendorf, Laura
Betting, Timo
Kockmann, Norbert
author_sort Höving, Stefan
collection PubMed
description The knowledge of product particle size distribution (PSD) in crystallization processes is of high interest for the pharmaceutical and fine chemical industries, as well as in research and development. Not only can the efficiency of crystallization/production processes and product quality be increased but also new equipment can be qualitatively characterized. A large variety of analytical methods for PSDs is available, most of which have underlying assumptions and corresponding errors affecting the measurement of the volume of individual particles. In this work we present a method for the determination of particle volumes in a bulk sample via micro-computed tomography and the application of artificial intelligence. The particle size of bulk samples of sucrose were measured with this method and compared to classical indirect measurement methods. Advantages of the workflow are presented.
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spelling pubmed-99200402023-02-12 Determination of Particle Size Distributions of Bulk Samples Using Micro-Computed Tomography and Artificial Intelligence Höving, Stefan Neuendorf, Laura Betting, Timo Kockmann, Norbert Materials (Basel) Article The knowledge of product particle size distribution (PSD) in crystallization processes is of high interest for the pharmaceutical and fine chemical industries, as well as in research and development. Not only can the efficiency of crystallization/production processes and product quality be increased but also new equipment can be qualitatively characterized. A large variety of analytical methods for PSDs is available, most of which have underlying assumptions and corresponding errors affecting the measurement of the volume of individual particles. In this work we present a method for the determination of particle volumes in a bulk sample via micro-computed tomography and the application of artificial intelligence. The particle size of bulk samples of sucrose were measured with this method and compared to classical indirect measurement methods. Advantages of the workflow are presented. MDPI 2023-01-21 /pmc/articles/PMC9920040/ /pubmed/36770009 http://dx.doi.org/10.3390/ma16031002 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Höving, Stefan
Neuendorf, Laura
Betting, Timo
Kockmann, Norbert
Determination of Particle Size Distributions of Bulk Samples Using Micro-Computed Tomography and Artificial Intelligence
title Determination of Particle Size Distributions of Bulk Samples Using Micro-Computed Tomography and Artificial Intelligence
title_full Determination of Particle Size Distributions of Bulk Samples Using Micro-Computed Tomography and Artificial Intelligence
title_fullStr Determination of Particle Size Distributions of Bulk Samples Using Micro-Computed Tomography and Artificial Intelligence
title_full_unstemmed Determination of Particle Size Distributions of Bulk Samples Using Micro-Computed Tomography and Artificial Intelligence
title_short Determination of Particle Size Distributions of Bulk Samples Using Micro-Computed Tomography and Artificial Intelligence
title_sort determination of particle size distributions of bulk samples using micro-computed tomography and artificial intelligence
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9920040/
https://www.ncbi.nlm.nih.gov/pubmed/36770009
http://dx.doi.org/10.3390/ma16031002
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