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使用改进的气溶胶三维光散射传感器(3D-LSS)对颗粒形状进行表征

Characterization of Particle Shape with an Improved 3D Light Scattering Sensor (3D-LSS) for Aerosols.

作者信息

Weirich Marc, Misiulia Dzmitry, Antonyuk Sergiy

机构信息

Institute of Particle Process Engineering, University of Kaiserslautern-Landau (RPTU), Gottlieb-Daimler-Strasse 44, 67663 Kaiserslautern, Germany.

出版信息

Sensors (Basel). 2024 Feb 1;24(3):955. doi: 10.3390/s24030955.

DOI:10.3390/s24030955
PMID:38339671
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10857102/
Abstract

To characterize fine particulate products in industrial gas-solid processes, insights into the particle properties are accessible via various measurement techniques. For micron particles, online imaging techniques offer a fast and reliable assessment of their size and shape. However, for the shape analysis of submicron particles, only offline techniques, such as SEM and TEM imaging, are available. In this work, an online sensor system based on the principle of elastic light scattering of particles in the gas phase is developed to measure the shape factor of non-spherical particles in the size range of 500 nm to 5 µm. Single aerosol particles are guided through a monochromatic circularly polarized laser light beam by an aerodynamic focusing nozzle, which was developed based on the CFD simulation of the flow and particle movement. The intensity of the scattered light is measured at several discrete positions in the azimuthal direction around the particles. An algorithm computes the sphericity of the particles based on the distribution of the intensity signals. The sensor construction, data processing and analysis are described. Model aerosols with particles of different shapes are investigated to test the developed sensor and show its performance in the determination of the sphericity distribution of particles.

摘要

为了表征工业气固过程中的细颗粒产物,可以通过各种测量技术来深入了解颗粒特性。对于微米级颗粒,在线成像技术能够快速可靠地评估其尺寸和形状。然而,对于亚微米级颗粒的形状分析,只有离线技术,如扫描电子显微镜(SEM)和透射电子显微镜(TEM)成像可用。在这项工作中,开发了一种基于气相中颗粒弹性光散射原理的在线传感器系统,用于测量尺寸范围在500纳米至5微米的非球形颗粒的形状因子。单个气溶胶颗粒由气动聚焦喷嘴引导通过单色圆偏振激光束,该喷嘴是基于流动和颗粒运动的计算流体动力学(CFD)模拟开发的。在颗粒周围方位角方向的几个离散位置测量散射光的强度。一种算法根据强度信号的分布计算颗粒的球形度。描述了传感器的构造、数据处理和分析。研究了具有不同形状颗粒的模型气溶胶,以测试所开发的传感器,并展示其在确定颗粒球形度分布方面的性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2013/10857102/e419016972cd/sensors-24-00955-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2013/10857102/e0fe2e48bb8a/sensors-24-00955-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2013/10857102/c225b47c72db/sensors-24-00955-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2013/10857102/3ed734abfc4b/sensors-24-00955-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2013/10857102/b2d5f1f3d194/sensors-24-00955-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2013/10857102/2a63ac5c6284/sensors-24-00955-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2013/10857102/5c83def19ca0/sensors-24-00955-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2013/10857102/50d10a440501/sensors-24-00955-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2013/10857102/865e8adee621/sensors-24-00955-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2013/10857102/e419016972cd/sensors-24-00955-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2013/10857102/e0fe2e48bb8a/sensors-24-00955-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2013/10857102/c225b47c72db/sensors-24-00955-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2013/10857102/3ed734abfc4b/sensors-24-00955-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2013/10857102/b2d5f1f3d194/sensors-24-00955-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2013/10857102/2a63ac5c6284/sensors-24-00955-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2013/10857102/5c83def19ca0/sensors-24-00955-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2013/10857102/50d10a440501/sensors-24-00955-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2013/10857102/865e8adee621/sensors-24-00955-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2013/10857102/e419016972cd/sensors-24-00955-g009.jpg

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本文引用的文献

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Deep learning for automated size and shape analysis of nanoparticles in scanning electron microscopy.用于扫描电子显微镜中纳米颗粒自动尺寸和形状分析的深度学习
RSC Adv. 2023 Jan 19;13(5):2795-2802. doi: 10.1039/d2ra07812k. eCollection 2023 Jan 18.
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Deep Learning Based Instance Segmentation of Titanium Dioxide Particles in the Form of Agglomerates in Scanning Electron Microscopy.基于深度学习的扫描电子显微镜下团聚体形式的二氧化钛颗粒实例分割
Nanomaterials (Basel). 2021 Apr 9;11(4):968. doi: 10.3390/nano11040968.
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Particle shape: a new design parameter for micro- and nanoscale drug delivery carriers.
颗粒形状:微米级和纳米级药物递送载体的一个新设计参数。
J Control Release. 2007 Aug 16;121(1-2):3-9. doi: 10.1016/j.jconrel.2007.03.022. Epub 2007 Apr 11.