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Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE)
Extracting quantitative information about highly scattering surfaces from an imaging system is challenging because the phase of the scattered light undergoes multiple folds upon propagation, resulting in complex speckle patterns. One specific application is the drying of wet powders in the pharmaceu...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9977959/ https://www.ncbi.nlm.nih.gov/pubmed/36859392 http://dx.doi.org/10.1038/s41467-023-36816-2 |
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author | Zhang, Qihang Gamekkanda, Janaka C. Pandit, Ajinkya Tang, Wenlong Papageorgiou, Charles Mitchell, Chris Yang, Yihui Schwaerzler, Michael Oyetunde, Tolutola Braatz, Richard D. Myerson, Allan S. Barbastathis, George |
author_facet | Zhang, Qihang Gamekkanda, Janaka C. Pandit, Ajinkya Tang, Wenlong Papageorgiou, Charles Mitchell, Chris Yang, Yihui Schwaerzler, Michael Oyetunde, Tolutola Braatz, Richard D. Myerson, Allan S. Barbastathis, George |
author_sort | Zhang, Qihang |
collection | PubMed |
description | Extracting quantitative information about highly scattering surfaces from an imaging system is challenging because the phase of the scattered light undergoes multiple folds upon propagation, resulting in complex speckle patterns. One specific application is the drying of wet powders in the pharmaceutical industry, where quantifying the particle size distribution (PSD) is of particular interest. A non-invasive and real-time monitoring probe in the drying process is required, but there is no suitable candidate for this purpose. In this report, we develop a theoretical relationship from the PSD to the speckle image and describe a physics-enhanced autocorrelation-based estimator (PEACE) machine learning algorithm for speckle analysis to measure the PSD of a powder surface. This method solves both the forward and inverse problems together and enjoys increased interpretability, since the machine learning approximator is regularized by the physical law. |
format | Online Article Text |
id | pubmed-9977959 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-99779592023-03-03 Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE) Zhang, Qihang Gamekkanda, Janaka C. Pandit, Ajinkya Tang, Wenlong Papageorgiou, Charles Mitchell, Chris Yang, Yihui Schwaerzler, Michael Oyetunde, Tolutola Braatz, Richard D. Myerson, Allan S. Barbastathis, George Nat Commun Article Extracting quantitative information about highly scattering surfaces from an imaging system is challenging because the phase of the scattered light undergoes multiple folds upon propagation, resulting in complex speckle patterns. One specific application is the drying of wet powders in the pharmaceutical industry, where quantifying the particle size distribution (PSD) is of particular interest. A non-invasive and real-time monitoring probe in the drying process is required, but there is no suitable candidate for this purpose. In this report, we develop a theoretical relationship from the PSD to the speckle image and describe a physics-enhanced autocorrelation-based estimator (PEACE) machine learning algorithm for speckle analysis to measure the PSD of a powder surface. This method solves both the forward and inverse problems together and enjoys increased interpretability, since the machine learning approximator is regularized by the physical law. Nature Publishing Group UK 2023-03-01 /pmc/articles/PMC9977959/ /pubmed/36859392 http://dx.doi.org/10.1038/s41467-023-36816-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Zhang, Qihang Gamekkanda, Janaka C. Pandit, Ajinkya Tang, Wenlong Papageorgiou, Charles Mitchell, Chris Yang, Yihui Schwaerzler, Michael Oyetunde, Tolutola Braatz, Richard D. Myerson, Allan S. Barbastathis, George Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE) |
title | Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE) |
title_full | Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE) |
title_fullStr | Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE) |
title_full_unstemmed | Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE) |
title_short | Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE) |
title_sort | extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (peace) |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9977959/ https://www.ncbi.nlm.nih.gov/pubmed/36859392 http://dx.doi.org/10.1038/s41467-023-36816-2 |
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