Characterizing soot in TEM images using a convolutional neural network
Characterizing soot in TEM images using a convolutional neural network
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DOI：
10.1016/j.powtec.2021.04.026
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发表时间：
2021-04-22
影响因子：
5.2
通讯作者：
Rogak, Steven N.
中科院分区：
文献类型：
--
作者：
Sipkens, Timothy A.;Frei, Max;Rogak, Steven N.
煤烟是一种重要的材料，其影响取决于颗粒的形态。透射电子显微镜（TEM）是定性评估粒子特性的最直接途径之一。然而，产生定量信息需要强大的图像处理工具，这是复杂的低图像对比度和烟尘的复杂聚集形态特征。目前的工作提出了一个新的卷积神经网络明确训练表征烟灰，使用来自天然气发动机的颗粒的预分类图像；实验室气体耀斑；还有一个船用发动机。在考虑分类器对自动初级粒度方法的影响之前，将结果与其他现有分类器进行比较。综合表征的全自动方法之间的总体不确定性估计范围从d(p)（100）的25%到d - tem的85%。在所有的技术中，在投影面积等效直径和初级粒径之间观察到一致的相关性。(C) 2021 Elsevier B.V.版权所有
Soot is an important material with impacts that depend on particle morphology. Transmission electron microscopy (TEM) represents one of the most direct routes to qualitatively assess particle characteristics. However, producing quantitative information requires robust image processing tools, which is complicated by the low image contrast and complex aggregated morphologies characteristic of soot. The current work presents a new convolutional neural network explicitly trained to characterize soot, using pre-classified images of particles from a natural gas engine; a laboratory gas flare; and a marine engine. The results are compared against other existing classifiers before considering the effect that the classifiers have on automated primary particle size methods. Estimates of the overall uncertainties between fully automated approaches of aggregate characterization range from 25% in d(p),(100) to 85% in D-TEM. A consistent correlation is observed between projected-area equivalent diameter and primary particle size across all of the techniques. (C) 2021 Elsevier B.V. All rights reserved.