School of Computer Science, Hubei University of Technology, Wuhan 430068, P. R. China.
School of Environmental Science and Engineering, Hubei Polytechnic University, Huangshi 435003, P. R. China.
ACS Sens. 2024 Mar 22;9(3):1208-1217. doi: 10.1021/acssensors.3c02009. Epub 2024 Mar 11.
Optical scattering has been widely used for aerosol sizing due to its noninvasive and real-time measurement. However, it is crucial to retrieve the particle size distribution (PSD) of aerosols without prior knowledge of the refractive index. Now, it has been a great challenge to measure the refractive index in situ. In this study, a novel PSD sensing method utilizing the light scattering angular spectrum (LSAS) and machine learning techniques is proposed to address this challenge. The complex nonlinear relationship between LSAS and PSD can be constructed while accounting for the refractive index of aerosols. A miniaturized prototype sensor is designed and tested on different sizes of aerosol samples. The experiment results showed that the maximum Kullback-Leibler divergence () of PSD is 0.07, which indicates that the sensing method can provide the ability for highly accurate aerosol PSD measurement without requiring prior knowledge of the refractive index. The compacted prototype sensor shows great potential for aerosol analysis in conventional field measurements outside the laboratory.
光学散射由于其非侵入性和实时测量的特点，已被广泛用于气溶胶粒径测量。然而，在不了解折射率的情况下，准确反演气溶胶的粒径分布（PSD）至关重要。目前，原位测量折射率仍是一个巨大的挑战。在这项研究中，提出了一种利用光散射角谱（LSAS）和机器学习技术的新型 PSD 传感方法来应对这一挑战。该方法可以在考虑气溶胶折射率的情况下，构建 LSAS 和 PSD 之间复杂的非线性关系。设计了一个小型化的原型传感器，并在不同尺寸的气溶胶样本上进行了测试。实验结果表明，PSD 的最大 Kullback-Leibler 散度（）为 0.07，这表明该传感方法能够在不依赖折射率先验知识的情况下，提供高精度的气溶胶 PSD 测量能力。紧凑型原型传感器在外场常规测量中具有很大的气溶胶分析潜力。
