Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE)
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s41467-023-36816-2.pdf
Description
Published version
Size
5.22 MB
Format
Adobe PDF
Checksum (MD5)
130872839d4d4ec4511b3c243c6eb0fd
Author(s)
Zhang, Qihang
Gamekkanda, Janaka C
Pandit, Ajinkya
Tang, Wenlong
Papageorgiou, Charles
Mitchell, Chris
Yang, Yihui
Schwaerzler, Michael
Oyetunde, Tolutola
Braatz, Richard D
Date Issued
March 1, 2023
Journal
Nature Communications
Publisher
Springer Science and Business Media LLC
Citation
Zhang, Qihang, Gamekkanda, Janaka C, Pandit, Ajinkya, Tang, Wenlong, Papageorgiou, Charles et al. 2023. "Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE)." Nature Communications, 14 (1).
Version
Final published version
Abstract
AbstractExtracting 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.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1038/s41467-023-36816-2
