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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-9920040 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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