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 (D_KL) 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.

中文翻译：

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基于机器学习的光散射气溶胶粒径测定方法

光学散射由于其非侵入性和实时测量而被广泛用于气溶胶粒径测定。然而，在事先不知道折射率的情况下检索气溶胶的粒径分布 (PSD) 至关重要。目前，原位测量折射率已成为一个巨大的挑战。在本研究中，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。在考虑气溶胶折射率的同时，可以构建 LSAS 和 PSD 之间复杂的非线性关系。设计了微型原型传感器，并在不同尺寸的气溶胶样品上进行了测试。实验结果表明，PSD的最大Kullback-Leibler散度（D _KL）为0.07，这表明该传感方法可以提供高精度气溶胶PSD测量的能力，而无需事先了解折射率。紧凑型原型传感器在实验室外的传统现场测量中显示出气溶胶分析的巨大潜力。