基于光散射水下微纳粒子粒径分布测量技术

Measurement technology for particle size distribution of underwater micro and nano particles based on light scattering

摘要:

针对水下微纳粒子(0.1μm~5μm)粒径分布测量的技术瓶颈,本文提出一种融合多角度光散射与深度学习反演的高精度测量方法。基于Mie散射理论建立光散射模型,结合动态光散射(dynamic light scattering, DLS)与静态光散射(static light scattering, SLS)技术获取宽粒径范围的散射光谱,采用改进的Tikhonov-GRNN算法与Scattering Decoder深度学习模型实现复杂环境下的粒径分布反演。实验结果表明,该方法在浊度≤100NTU的水体中,对0.1~5μm粒径颗粒的测量误差小于8%,重复性优于95%,显著提升了水下环境中微纳粒子粒径分布的测量精度与鲁棒性。

Abstract:

This paper proposes a high-precision measurement method that combines multi angle light scattering and deep learning inversion to address the technical bottleneck of measuring the particle size distribution of underwater micro and nano particles (1nm~5μm). Based on Mie scattering theory, a light scattering model is established, and dynamic light scattering (DLS) and static light scattering (SLS) techniques are combined to obtain scattering spectra with a wide range of particle sizes. An improved Tikhonov GRNN algorithm and Scattering Decoder deep learning model are used to invert particle size distribution in complex environments. The experimental results show that this method has a measurement error of less than 8% and a repeatability of better than 95% for particles with a size of 0.1-5μm in water bodies with turbidity≤100NTU, significantly improving the measurement accuracy and robustness of micro - and nano particle size distribution in underwater environments.