Sizing aerosolized fractal nanoparticle aggregates through Bayesian analysis of wide-angle light scattering (WALS) data
Sizing aerosolized fractal nanoparticle aggregates through Bayesian analysis of wide-angle light scattering (WALS) data
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DOI：
10.1016/j.jqsrt.2016.06.030
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发表时间：
2016-11-01
影响因子：
2.3
通讯作者：
Daun, Kyle J.
中科院分区：
文献类型：
--
作者：
Huber, Franz J. T.;Will, Stefan;Daun, Kyle J.
从弹性散射光的角分布推断气溶胶分形聚集体的尺寸分布是一个数学不适定的问题。本文提出了一种使用贝叶斯推理分析广角光散射（WALS）数据的过程。结果是恢复的粒度分布和聚集体形态参数的概率密度。这种技术适用于合成数据和实验数据收集的煤烟载气溶胶，使用来自瑞利德拜甘斯分形聚集（RDG-FA）理论的测量方程。在实验数据的情况下，恢复的聚集体尺寸分布参数与TEM导出的值一般是一致的，但精度受损的RDG-FA理论的众所周知的有限的准确性。最后，我们展示了如何使用近似误差技术来避免这种偏差。(C)2016爱思唯尔有限公司版权所有
Inferring the size distribution of aerosolized fractal aggregates from the angular distribution of elastically scattered light is a mathematically ill-posed problem. This paper presents a procedure for analyzing Wide-Angle Light Scattering (WALS) data using Bayesian inference. The outcome is probability densities for the recovered size distribution and aggregate morphology parameters. This technique is applied to both synthetic data and experimental data collected on soot-laden aerosols, using a measurement equation derived from Rayleigh-Debye-Gans fractal aggregate (RDG-FA) theory. In the case of experimental data, the recovered aggregate size distribution parameters are generally consistent with TEM-derived values, but the accuracy is impaired by the well-known limited accuracy of RDG-FA theory. Finally, we show how this bias could potentially be avoided using the approximation error technique. (C) 2016 Elsevier Ltd. All rights reserved.