近日，新加坡-麻省理工学院研究与技术联盟(SMART)中心的George Barbastathis及其研究团队取得一项新进展。经过不懈努力，他们从单个散斑图像中对粉末粒度分布进行无创估计。相关研究成果已于2024年8月21日在国际知名学术期刊《光：科学与应用》上发表。

最近的研究表明，通过物理信息半生成方法高效训练神经网络，实现散斑自相关的反转并获取粒径分布（PSD）是可行的。在这项工作中，研究人员通过设计瞳孔函数，消除了之前方法中耗时最长的步骤之一。通过精心阻挡瞳孔的部分区域，研究人员牺牲了一些光子，但换来了旁瓣的显著增强，因此对粒径分布变化的灵敏度也更高。

结果是总采集和处理时间减少了60倍，在该研究的实现中，每帧仅需0.25秒。在该系统中，几乎实时的操作不仅更有利于工业的快速采用，而且为干燥、混合以及其他化学和制药制造过程中复杂的空间或时间动态的定量表征铺平了道路。

据悉，粉末的无创表征可以采用以下两种方法之一：成像并计数单个颗粒；或依靠散射光来估算整体的粒径分布（PSD）。前者在实际操作中会遇到困难，因为系统必须符合成像光学器件的工作距离和其他限制。后者则需要从散斑自相关到颗粒大小的逆向映射。其原理依赖于瞳孔函数决定基本旁瓣形状，而颗粒大小的分布则调节旁瓣的强度。

附：英文原文

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

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

Issue&Volume: 2024-08-21

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 0.25seconds 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.

DOI: 10.1038/s41377-024-01563-6

Source: https://www.nature.com/articles/s41377-024-01563-6