Non-invasive estimation of the powder size distribution from a single speckle image

Qihang Zhang; Ajinkya Pandit; Zhiguang Liu; Zhen Guo; Shashank Muddu; Yi Wei; Deborah Pereg; Neda Nazemifard; Charles Papageorgiou; Yihui Yang; Wenlong Tang; Richard D. Braatz; Allan S. Myerson; George Barbastathis

doi:10.1038/s41377-024-01563-6

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Non-invasive estimation of the powder size distribution from a single speckle image

Original Articles

- Non-invasive estimation of the powder size distribution from a single speckle image
- Light: Science & Applications Vol. 13, Issue 10, Pages: 2221-2230(2024)
- Affiliations：
  
  1.Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  2.Singapore-MIT Alliance for Research and Technology (SMART) Centre, Singapore 117543, Singapore
  3.Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  4.Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  5.Process Chemistry Development, Takeda Pharmaceuticals International Co, 40 Landsdowne St, Cambridge, MA 02139, USA
  6.ShinrAI Center for AI/ML, Data Sciences Institutes, Takeda Pharmaceuticals International Co, 650 E Kendall St, Cambridge, MA 02142, USA
  7.Present address: Department of Precision Instruments, Tsinghua University, Beijing 100084, China
- Author bio：
  
  George Barbastathis (gbarb@mit.edu)
- Funds：
- DOI：10.1038/s41377-024-01563-6
  CLC：
- Published：31 October 2024，
  
  Published Online：21 August 2024，
  
  Received：12 March 2024，
  
  Revised：28 July 2024，
  
  Accepted：02 August 2024
- Accepted：
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Zhang, Q. H. et al. Non-invasive estimation of the powder size distribution from a single speckle image. Light: Science & Applications, 13, 2221-2230 (2024).
DOI：

Zhang, Q. H. et al. Non-invasive estimation of the powder size distribution from a single speckle image. Light: Science & Applications, 13, 2221-2230 (2024). DOI： 10.1038/s41377-024-01563-6.

摘要

Abstract

Non-invasive characterization of powders may take one of two approaches: imaging and counting individual particles; or relying on scattered light to estimate the particle size distribution (PSD) of the

ensemble

. The former approach runs into practical difficulties

as the system must conform to the working distance and other restrictions of the imaging optics. The latter approach requires an inverse map from the speckle autocorrelation to the particle sizes. The principle relies on the pupil function determining the basic sidelobe shape

whereas the particle size spread modulates the sidelobe intensity. We recently showed that it is feasible to invert the speckle

autocorrelation and obtain the PSD using a neural network

trained efficiently through a physics-informed semi-generative approach. In this work

we eliminate one of the most time-consuming steps of our previous method by engineering the pupil function. By judiciously blocking portions of the pupil

we sacrifice some photons but in return we achieve much enhanced sidelobes and

hence

higher sensitivity to the change of the size distribution. The result is a 60 × reduction in total acquisition and processing time

or 0.25 seconds per frame in our implementation. Almost real-time operation in our system is not only more appealing toward rapid industrial adoption

it also paves the way for quantitative characterization of complex spatial or temporal dynamics in drying

blending

and other chemical and pharmaceutical manufacturing processes.

关键词

Keywords

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