IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-36816-2.html
   My bibliography  Save this article

Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE)

Author

Listed:
  • Qihang Zhang

    (Massachusetts Institute of Technology)

  • Janaka C. Gamekkanda

    (Massachusetts Institute of Technology)

  • Ajinkya Pandit

    (Massachusetts Institute of Technology)

  • Wenlong Tang

    (Takeda Pharmaceuticals International Co)

  • Charles Papageorgiou

    (Takeda Pharmaceuticals International Co)

  • Chris Mitchell

    (Takeda Pharmaceuticals International Co)

  • Yihui Yang

    (Takeda Pharmaceuticals International Co)

  • Michael Schwaerzler

    (Takeda Pharmaceutical Company Limited)

  • Tolutola Oyetunde

    (Takeda Pharmaceutical Company Limited)

  • Richard D. Braatz

    (Massachusetts Institute of Technology)

  • Allan S. Myerson

    (Massachusetts Institute of Technology)

  • George Barbastathis

    (Massachusetts Institute of Technology
    Singapore-MIT Alliance for Research and Technology (SMART) Centre)

Abstract

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.

Suggested Citation

  • Qihang Zhang & Janaka C. Gamekkanda & Ajinkya Pandit & Wenlong Tang & Charles Papageorgiou & Chris Mitchell & Yihui Yang & Michael Schwaerzler & Tolutola Oyetunde & Richard D. Braatz & Allan S. Myerso, 2023. "Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE)," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-36816-2
    DOI: 10.1038/s41467-023-36816-2
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-36816-2
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-023-36816-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Dongyu Du & Xin Jin & Rujia Deng & Jinshi Kang & Hongkun Cao & Yihui Fan & Zhiheng Li & Haoqian Wang & Xiangyang Ji & Jingyan Song, 2022. "A boundary migration model for imaging within volumetric scattering media," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    2. Y. Jauregui-Sánchez & H. Penketh & J. Bertolotti, 2022. "Tracking moving objects through scattering media via speckle correlations," Nature Communications, Nature, vol. 13(1), pages 1-6, December.
    3. Y. Jauregui-Sánchez & H. Penketh & J. Bertolotti, 2022. "Author Correction: Tracking moving objects through scattering media via speckle correlations," Nature Communications, Nature, vol. 13(1), pages 1-1, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yaoyao Shi & Wei Sheng & Yangyang Fu & Youwen Liu, 2023. "Overlapping speckle correlation algorithm for high-resolution imaging and tracking of objects in unknown scattering media," Nature Communications, Nature, vol. 14(1), pages 1-8, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-36816-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.