Three-dimensional tomography reveals distinct morphological and optical properties of soot aggregates from coal-fired residential stoves in China. (October 2020) - Zhang, Chenchong; Heinson, William R.; Liu, Pai; Beeler, Payton; Li, Qing; Jiang, Jingkun; Chakrabarty, Rajan K.
Highlights: 3D structure reconstruction of freshly emitted soot aggregates using Electron Tomography. Fractal Dimension of 3D soot aggregates ranges from 2.2 to 2.6. Soot morphology and optical properties deviates from those modeled using DLCA and equivalent-sphere models Abstract: Electron tomography (ET) is used to reconstruct the exact 3-dimensional morphologies of fractal-like soot aggregates sampled from a household heating stove commonly used in China. Conventional ET techniques suffer from "the missing wedge" problem caused by unreachable tilt angles, leading to noisy reconstructed tomograms. We overcame this problem by implementing a high-resolution object-edge identification method coupled with a novel voxel-filling algorithm to improve the reconstruction quality. Our reconstructed micron-length aggregates highlight the local non-idealities present throughout a particle's surface; these characteristics are almost impossible to account for in existing computational simulation exercises. Q-space analysis predicts the fractal dimension of our ET reconstructed aggregates to be in the range between 2.2 and 2.6, which deviates significantly from the universal value of 1.8 obtained using the widely adopted diffusion limited cluster-cluster aggregation (DLCA) model. The optical properties of our ET reconstructed aggregates are compared with those built with a DLCA model and equivalent spheres . The most striking optical characteristics of the ET reconstructed aggregates are their wavelength invariant mass absorption cross-sections of ~3.5 m 2 /g and single scattering albedo of ~0.5. The sample size investigated in this work is constrained by the extremely time-consuming object-edge identification process of electron tomography. This issue necessitates the development of more efficient computer vision algorithms for future research.