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

作　　者：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 

机构地区：[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

出　　处：《Light(Science & Applications)》2024年第10期2221-2230,共10页光（科学与应用)（英文版）

基　　金：the following funding support:Millennium Pharmaceuticals,Inc.(a subsidiary of Takeda Pharmaceuticals)D824-MT15;National Research Foundation,Singapore,Intra-Create thematic grant NRF2019-THE002-0006.

摘　　要：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：SPECKLE SIZE DISTRIBUTION 

分 类 号：O43[机械工程—光学工程]